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Inventory_optimization_model.html
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<meta name="author" content="Afsar Ali" />
<title>Analysis of the Proposals to reduce average level of in-process inventory</title>
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NOjMKhUpWZWHK5LZgl9279229we2OBUX50kuVjv5QDo7PBwnsvrhWJF%2BYDIuVagZDxeFHOF1MEKbsBMEQS%2BKJjOVdXJ1BKw61EH%2BfeqSTzTz3I7ZA3Zuv%2Bwhshy3sDFL2TjctJR6n2SDsfFJ3A0I5ewXfAgugw7s%2B0XQG0SAfFVWHOEsr6TyphSHW5NHFc9J6Wa%2B7B3Dfp42HguHAUINniPlZCpQ%2Fl0CogDIrW%2F8u85iv7sGv8ZzGzYAxjwV%2FMCxTwobJQCTWU8HRPQeruaaXpRqestVdUOXso7dupeF7px4Z8%2Bed3arKFc44AIg51W9ch4kIIiUEocmSk4sBpCcj15oUDRJXYYExl37RmirrkIv55rLASYJJF%2BS3t0nopeptU%2BE%2BmLrLK%2BlPgQyid3mCBU6UP1rVz8R2n770zc%2FXf7x8s%2FNn9fvaFi3rmFHPfmMLWRP4lycho%2FjNPY4W82Os88wiJ34K4tdAIQjAOQkx8YArcM2PaAOjSZBL8uolzAJFFvGDXd8ej67P2AvKpUkOYghcnK7zl300RBcsExwzJ%2Fhbrd7GuYBwhgAIYtbTx%2F3%2Bd4klJ3gtKCQnGIz9InYZEzqG8EkjSzNavCB%2FcXYlcQshhyMsZrI6PYLWc3lOG%2FvlA4rHr%2F3uTFD3r38%2Fr%2B3fMKOke9W4oJ9G566u7au84CpOz%2Fct5R99wF7W6dIYjjnawrHIAh3hlungFOWgXoyzVKbHOr1eD19Il6vISsrrU8kSzbY%2B0QMGpdjgYh60zDTHJKHoyP4404pw27zB4o1o62gq%2BBLL299am8j%2Bzv774zj995%2FdgTOZsOfWr3rnTWPj2h8qGbo1%2FM%2F%2FkYYvmxfms7TtPrM54E7ns4vwBw0rFy%2FaNJjRRVTet31OgCBPABhongUDOCAzuE0h6gnxChToCJ1ulB0iH0jeqvscFBZotflk%2BhMQ5oJDqhrC%2Fl%2F%2FFxmAUlGYeK5Z6Jl5MDec2yJQdc%2Bl5ViNduL1avoZ805eGll04jy6COKheT8S%2BU6kQwdw%2BlW6nPpXF4qtEoBziwAye3mMnRLkqlPRLqZdQlsKxTcLghkqhzjrLL5M%2BWgUwldSkjbL1HPLrCf51d8MHbv66zu%2FmcGl5Kz0YNZ0%2Bmcf759kbEB29qGGrZiYWop2b2R9fYqnKnlWOVzqXqgNfQIB5LtRr8fQLLT7CyT0ZLaL2K0WFzU5e0TcfmojkckcgvcyhJ4pNlr8Bd63VyEhIbiGhfIBFGTq8R9lqcWB2Dl1G79Rn%2F9i8n08OU3L%2F760UX2E369YuvqVUPrI9VryFR8CXc5V%2FrYefbW7svv%2FYNdxUHv%2FOnFVQ1V8yse2Dde0UcAIY%2FzU4L0sA1FEQg3jJT0jVAJFBlqbOOrALk1dCOmkuHNF%2BmpaKOYunHhldNAlZhEyFGpz4R20C%2Bc47Vmu%2B6gqXo9lewuq5TfXrLnZORk9Ink5JjAlNwvYvJBoF8E5N8qd9nN3jrmj7mOx8OPLDXqolpgwv0zZkpuzaeTynf%2BvWjNvnr22b%2BbsfDJR7%2Be%2BcL6dQ1bXlu3CDvOWfHIMytnrhJPHt7x4L7eg%2F48%2B8C5U0euLuu%2Ff8ozr1xteHTRssdGru8V3kwfeHTMsN937%2FzksLEzFdlO5NQpNsMLWdAtnJlizzQYAAQu26AljUvWZbEQlyuJi1Ymcr8Iaal2jjKNg5qJ9Ctqx02jMyDFKHJw8TpUIvjHKhXZQlZ0%2FIwe1eO%2B%2B6%2FRVHpg2mv%2FuPbBuguPMtfKLU%2BtuXfjkIFraEVzg2tlMuZg6O57%2FvXBP1C3kZ3H9od2PPV81RMVE%2FaNAy3HEcaokRS34Ta%2BLAA8XotzQMRiizkRDVfN87X0JXae6NzkVR6Znehb6J8XL%2BY3IKovXMjn0oEDMrkmmc2iXu9yGm0DIkab6hgTZklwj%2FT6FDccpXsmn6Rjlxv%2BknyrTFMR8%2BU%2FcF9%2BDiRwh%2FUCiChwdeXD58cDhSwsRjeikNNcTo83%2F0AtP2DDKLywji1nhxSezMTjgo9eVHOy3LBbJgIQ0OsEsToiIFRHrIjI4wHOlfxEz6a4ZOTXTLq9eTjdTofW1bEH6up%2Bg5GIBDhGEr2BkRNVlMZTa%2FP3HKVyrMMKrF3H%2FKPYUAWjlGsXaRnXrxTIhrJwqp%2FbMtnphFYWIdgGoLWtddqASGuPzdA7YhNaqFZLvVJSEa48LZwUd4YSN4mJ%2Baq%2FctSSXgtmD6gf2emV91%2F9KNj38bHd9l3PX0tq19dMnzFw3OSsgsWjj%2BzqPXn0w4On3e9nZ%2BNJLYFZ1yqkQ2ITFEM5zzwyA%2B1KLJ1kVwpAjsvSTgx3S%2BrQQeiisxv5Ky%2B9kGbnqUmllmSFEhOP6%2FG4ug6C2nJQUPdSt0td36R1IFMgbsUalrqlQAbw4KK1v1BwIH%2FudKqm8NCQbeMHP2LUtVk3rv7Fb4712N3Tt%2FDeaWvZt3%2B8wA7swe6Y%2F5cvjv3I1rHJn%2BAyhLM44ODVn14%2F7bBUDpq%2Fhpxb8c388XfdM%2BrU3veu%2BTws17Pv7O79aFvzMnvxc3aaHRq8sAZX4jgUsP7CfvYntoNhGYquJiAAAKJNPAIyWLjk0ojFqENR0SwqyILNaiG9I0bRYhFECoKD518xh6iplZYz%2B5W8H0OIlBsz%2FtURB6IHmnaT7itJORvb6A94cnbjGZYvHrnSg0zENwfPGTGddQIKJwCEo9xyW8ALGdA7nO0UUg1Wn89iEGQLjwd01iRrUlXEarWAxVcVsTjAWxUBevt4QnM9%2FgxBMbluwe4SAjxpj%2FmcgN0ef3cCt2IAhVVLsR%2F7%2BTIjjZjU9PTeY1ew4I9%2FOvhn8cCeI%2FNf9BnK2Pk3%2FkZ7TF00%2B6HoquhndauXPAGAMIdb09Oqr8gOu6jFpbdQb5IDekccglHi%2FHK2DL%2B4emRymUNIE3%2BRo3WokKfbtNP37Cs0%2F7rxjQ0X2Cvs2Rex%2FNNLuysbxBB7lX3FPmdvl64rwyU44QusOVSzuj8AUTgmDuEc04FdsYcWQQ8COJyiuSoiUsFSFREct4ppwc9rSBlA%2BZuAPZTBx2Az2Uo2CY%2FhIHysic%2F1z59PI%2FdU5CtWz%2BaJB9gi9gKmYebVKZgHgMq89Bc%2Br1GJWSSDAQXQoWAyS%2FreEUlCQsTeEUKRr3B03DZmUZBwxy%2F6S%2FMZmh%2BdTYZHt5OF4oH1LKc%2BeilhJj0UhpMlAKQ6pAbjTRPxSW45Q0CbAac3asPzwaNfrY9LTuyi2ilOhUvnI8SSohNapUJK7wiAaDLZe0dMgujtHRGdt4%2B8%2FHaphRyV9%2Brq5lT1xe9nfPc0a2IrDuKQL%2F%2F9bve3DrL%2Fso%2FQj0kbVrGXCYuWZWXjUhzzD7xn%2F%2BD6GvYau8Q%2BZe8H8LUY7WK6yuVQ2KdHBJ0giCCaTTraO6LTiQaJoshJV81RgnG%2FQbydi5f%2FDYnpjc2ssZGSRrI3Ws1z7dXkYQC8NoLNxfFqVpwaNht1OotVT4GzFDJj9GrpGI15%2BJJiPpxLMg0v6dVv9AONx9jclFWuR6fyFGvI0TNxvRC%2BUjHmnkjBViRGg4Ix0Yn6RGzLWkgJZRVRDKHw1TvRrzc2NpL1J6JN5M0l0dc5snnk4%2BjCBF0QIT1soQCCJCMFzgtw3EBXxTekkO0%2B0aio0pV%2FbIp9V%2BKIgpPrUZJOFCUev%2FJSmsuNBjuVjDK1gKQgp2DnLbuZlRjwuJUAn2MY4nce4COtZjadZSsCntbhh6zRomMm0bbpo%2Bbh4oGrVQLPOume7Uev%2FBCXo1IDsUG7sFsvcaytVpDB7jBS2aqjKCdypaUI4xPzabNJKZdj%2BWvNn%2BtsW4%2FRVB2xkGeEk582NR%2FnE3ZMwaxy2guAqFp99FZ5bu%2BIXqDW3hHqvLVNiOltBiTmueJRtpW9oZgjHIE9sBOOujo9%2Bv1%2Ffvn5h%2F9Eeb77LHuYa%2B94HIt1bArbxs6yU1iIuRjEAnYqZp%2BE8erqdUBRONnA%2Bc75DE6XQaiKGAySLDuqIjKVEtavhpXmSgW%2FmlplYChutYXx7Ay7tLsRZ5PWUePGL949euKoYPr7t1HOh2jK6mdXrVC5wHaoXLBCCp%2BZp8MeAIEa%2BOqmZtns6x0xC7KTL2yZM%2BMtlRs3J6I2pViG8q258sX7OOxndrH0tpz5ki3rzuqxivyf%2FDnN%2BWMCN1SGs8yIxKS3y0aDQdYTwePVm8EMVRGzmVDK5UepkSi6cntnp2Ku8ktw20SOf5bGNm4BcRXyGdhfcfkJ9jQ7%2FVXTzl2vfEZGRLeJB94%2Fzf4%2BLjqZjFi9cuWqJwDVHIFw29ha4V6a0wSQ5BSFrGxTGvV4uH30CFSfoEoJiY4mt0CGlozy8D%2Bo5jgx%2B6jmBbwy4BEI%2B9d3rHnZ0I%2FGN%2B7usnL1ey%2BxM389WLx%2F1%2BINHRbWXfoDLjz%2B6Z07su%2BYN73vyIFFvd959sV3qtf2nfFA35F3FQw8AoDgABCGcv7JvJ7iABSRUp1epgK3CYLmFeJ5qGYSi7k3IEsbWYFQyQrE9PWqJzjM14yPj2OHrLDdhgYZZafDrqOCmQ8UpzGUuFzsLkUnVHMYs4uij%2F2F%2FcJfFxrfee3ld8QDzf2vsC8wo5nuaa44%2BMabh%2BghQAAA4XW1%2FpMcNqJgMuooCJQqiPLlrxWvQhjgF8%2F%2FSgXTwej3O6M%2FNmF1x8zWHdVaFh%2F5uU3bnwXkmg1yXz6aT6km%2BQwpyW6LRdQn2Q0U9TGTotqUGOKqNclWAjJldKcyenwSZ0h8cyc75y5CT3v2xU42u%2BnL9p6UYpSa0Nne7yy%2B1EQ%2F7PaW6%2Fdbm0N88llHNx18ic5qnrv59RXv0YUK93QAQr1q9QNhhyCJ3ORLiskXFJMvtDT5KhocAz63Yu7rj%2FPIY0oTXmKdjuAkfHg%2F60QWROeQZnI4%2Bgq5M9oX4lybrUY5GWGrIBJRpnoDiChTUeOcJmE%2BqKL%2BGCJdcNEhlrSb%2BQ6T8%2BR887zoCZJPFyv1ZQBBscZ6pWKmQyqDLKBgMIoCNwcUdUrMcuuKmVot8AvlzU6qi9roq82%2F0LSFwoaNC69OAIQGdoRMVnSRY2mRUFAYoxcJlTDIOdBSfeJRD5nMSvEEu4B%2BdkS6svyKX6HWC0A%2Bi1c2Kd5c2XRy3h0mgYbo%2F4spg%2FKNEDuCzdrMFFACSacHOUgFevPMXj5rMb9CfMoLfOrSA%2BKF5b9KyigFJCgExOMgQVJYD1TWiQQEwrO%2BG5rpVFUTC3DfaPxsA1vG9pEg3dQ8jnwV9QJea2Zv0k3XKtUKsJLHIlEqwBgjmU%2FLQUfRp9mbCwCxTjhHHZIf9OA8AILRID2BkJ%2Bs1ZoxwDW1OMStBHU83G1fm5MZ0%2B4QzhUdK3f33F8MRKk50lPCUEXzoVc4K1NnTEvz%2BRw6yqMpYkzrFSFGI7jd1ooIt4LJFRHRA24o%2F98LVH4tX7NllapJZ7zS6LZn8QVeLKsVKjrQrxv43GPPvUychyc%2FVveH0F3HR77xCrNs%2FmPDWy89tOWB3js3Y1%2Bb1GPe7Jq5dxTuORZ11TZuHC3LD00fOhwI7OVWtVZygRPSeVUt0%2BD1Wq2mVGqiGX4zmNwOu8HOhccRljzgqoiArYV5DSXF1SDB1sddEk825YBijeRQiVcrvHAqyJ5Pv%2F3%2Bk0l%2F7GwKzGzQ6Wa811i%2FqXFjfb0wlJ1jP%2FDXxwMGLpdcbNHcsTuWvv7ll29fOPPJXwAQpnMOLxWGxbIaK6VuPU3ySmaOmQ0cHDPPzVmNGM9qlJ1DHgNzu6hmOGTcZXYV9f8d8HTbUOn8QrbvuW11Tz3swiw0oRPvyPQu96Sywe9%2B2mlNGRBlVqGU88fB%2BdM97E%2BVvGCx2CV7ht%2FhtgIgmqhez9mjt1FnRYR6bscerSYTkLTqvTcUDPLPA6osi%2BJOiG7ST%2F%2Fn2W%2B%2F%2B%2BTCTLMsNCxmTzdu3Ny4evOmNS9gNlr5647tA%2Frh0V%2B%2Fmfny%2B4Gv3r54%2Bi%2BfxLF0cN44IRk6hdOTDF4jpdzqtkrxGit4uRskyaUyyqIw6paZQyiRZQ632%2B%2BJsUuivNbh53Kb%2Bx%2F2JYp%2Fe%2F%2B7qFl8eecf%2FzBk65bfb7WQLstc2AZl1GMH9v3fJxx%2Fp2pttp%2F%2Bc%2FeGrS8oUksFoBYpHVxK3cVlMjkJ4UaSuj0GvhQMgKIsVkScspUqq0GtY98IAxWmOZS1p2QNgeJSXkPW3DX3mE%2BzrxreeANH3lObN6LH8KHopW83l9G3%2B3TugmsDC9PnPNkLgEKQuYQCzplcKIVu8HC4a56vQ5YpvYtY4ESnSHIzW6Vn%2BQzd72xlLbYWV0R0nXpFDJm6XKvOqvPk5pJekVxrm%2FJekTY2T7teEU9KnHUa%2Bzj%2F8pXd%2BrzbxD1uragaVBdAqDC%2BjaAUkrJv%2FOXKcGMXmJOnbhQXF%2FF3QsHJVnf87VhB3sSqoa%2Fte5X9jf3r7FdPzMgtC%2FccNOnTtwb3ZPb6ZWdOPLzh7amPD50%2F4z8%2F1T4uVE5ICkzt9ewxXYdBbfPqVx54ddvqMauTndXFnYfmBnY%2B2PS66ypEhs2ZFOn5IO08%2FZFvfn4cEPYCCD24nnuUzM5i0nFz7dF7vEkWvcMhVEQcNgOA3q0Y7xjlCatesVT2mALbtRUfM1P06cfm%2F%2BGZhgadoWD%2FjBMnyJuLfn%2Fkk%2BjrfHXnDOow4N5XP4gWAxDYDoDjxAtAwcr9tZ3PJCDa7Ga5MmImVlQ04%2F3EwqZSIqAJJVQc3NDQ1CG3TceObXI7CJWYU1Zc0qFDaSkAubaKudSxTZAEd4Q9TqPRrNP5kj22yognrLcC1z6ISzW5xSTOhATTljhb3v2det7Zv%2FeNGZnLt9g16B6h%2BaqNHZHv0yaP8TSV89QGJTzetxgMRqNOEkSdYHeYAGw2nY7KRje1xiKGfD5zeUyFyuJsRTUiQi0bdclYkzcER73JeuD5E2zOnB07dKSgy2icydpGlxLpQTZOcjW%2FXTo9NjcO5nNT4GQCoiASQHfca2tMVBjHYVRo6SRfJQGoCAfcdruDiz%2BgdwRo66xWHrfb4RPMPm5p0302p1UPDkUPuCLEt534Igi1bHVIVIgEzfAqepHh1bRDypryyOa1DVNmblnVsDhFl79rIuIAXcHhmYdfJicWLNj3cnSLcv%2Fzx9HjQmV99dDDg8e8%2BheuMZq2cnxdUBBOApeiri69x23S22xcWW02g%2FV2ytpSV72Jmrp7m4JG6NDUt95RNPXwJ%2Bq8d0XUSWM2dhSfU9EknsU6wSyDnOwzeLgds1GbYvxvmcVylSHFilGFxE4PYRT74fKaf%2FwOTZcvobX5lZ3PPffii88%2F10Cy2I%2FswyeR%2FAFNmMfeZ1f%2F8rfzH545p1j5vdyW1apU%2B6E8nOEzCrKsS3foHJkBwQhWq7siYrXprboUaHXDzMdZ0GLBqpaeO2hPAhMUr62Y%2BgRHrThpU8Niry7c%2BPBf%2F%2Bf7yzvryabGFc8%2B6xowcMRg1kUqqh9azT5h%2F1GcNr14%2BGTWl29fevfUeYVXHNNSlVexqMKW6qHJyT6bL8OfnOK1pqalecxOp8wtv80MFRHz%2F%2BY2VT5yJ1l63Ul6r3vQ0njtQyL9GzaIW15cvXnjnI8uf%2FfJ57P0SQsajObpM%2Fd9mHXp3YunT59birloRDO2a6z%2F9T38eEzFCzE9okGOpw1ywy6zXm8wEF4DsZrB4FYtg03rc2nRkaE5IY15ZEfvjt4eRQtfaahz6rrsFoaZNlk%2FfTbaJFSenDQjlrnS6XyW1twOtIplrqLzeuZaEfHYJKq%2Frj%2F5t8pdueG5kbsG25Hfpq50%2Bj%2Fe%2F%2BtjA%2FbXzF82%2BdmN88r%2FevSPL3Z6ftEjj7Yds%2BJ13jSzsaHnpjbt7h4Uvrdr2aAH%2ByzaXLm4R1W3O7p2KO71FCCkX%2FuG7BQrwKPWJlwu3jPioEKS1%2BC0OXtFLGGbVeaCkj1xU3kqIVjV5ONWqo52xVGXhtxKNuHyEMcdA5NSJuSy17ZurRiBXdlrw2vN8lyzHQeQZdU9%2F83mRWePngiAsIOvrjKhElx8fh86ZZPJ4DS4PSaz2aZzWdVV7TFqEbMS%2F4daVmW0rJcrhBY127EvX9TPNNQl6UP7Z7zztlAZLeMO6GMSvnpozV2Dj54hp7RcjgiVau%2BHAQ0ms6hHK6jhiJZl%2BNX0NFTicIYQt7ER%2B76ptuiMte%2FtYyP4oI%2F8o0cx9iPtrx6K5UpSgI%2FWinsblz4lNc3rsZipYBZ0yQ7ubnTuxCyYK7c2A1U2Z2Rlk8LhUHSq1BmbsoRPKeSfcBbp2qSdPsY%2B3jNxsk5nLHCcaHqjg0snBF7dzc6QBZ3OvHR%2FdK5QyUaz6j5l%2B4tJbXTp7trW9eRvHClACAIIOpXGzLBdFiVAUWlxQZ3RLaD1pnQ4ngmjmhUfYgteQT9m%2FJktwFVH2Cn27hFSQLxsGO6IfhU9jUdYD0AgfL1LfHw3z%2FsVMqnHK5jB7OBLO0UHfIJCVam1GRJo46KKOdrSUrLvuwFOnfnuS%2FtYTsWfl%2FStKu2xq3cXzuCVn9wf%2Bpn87mrGy5vtC03HtkAsZ6YPCZW3yJl7RUQr6npF0P2%2F5cz0oeZ%2FksHR0%2BTL6D5y31Q6eN685sPxrixetlPl5%2FYlJxu9AFbZRbmnpqlpTq09K3F7TdV%2FbpXcPJZTfEtxCddDvj7d3EK4ZLfHjedrpx794PFH58%2F49MClCxdM44aRZaRxE%2BaPjywnw0Zg4ebdS6Xj7NzZoCl4FhAvMxuZrfluorSo0RSABN%2BtlHzx8nKeJv3cDAiV7Ijaw5Oq4OwWDQ4H8UFqqsXiE2laujso0QScEzYFFXSDxYr7U7DPVNCV5Dj2pcRw4eKhDx%2BZ%2F9jjp45OnvHwVFIePIvB49LSPRvZ%2ByPvJcsjvOq5cRenZNg4zJn2qEvdpyXVQg6tAS%2FXAzu1JvkcpuoIdVglCaojEuTngS3pjfw38rSkOlOZT8nQVNOmbD9lKoU5HFg8t2TMUz2mRrqPyi95omTcisrHK%2FsMJSfuLFn%2FUKvsVinhsvqH%2FRkZSeoOPFuKdcJwrcuYCALV8343AGpSu4xtNPOWXcZcCQNO1%2FXt0PNKk%2FGszp3Ly0IVZPfVC2Lfxb3C5ZVhQDjK7fd5dVemazjNozNTahCARxo62irVJxKnwUz4SzDKgg%2B07k9ljt9sw2apra1KOJCldLR6NAOuqD89OWHNwpPHcdniPisKChY%2BtHv7My8sX%2FFdifTO%2Bxlov4LNXXfvoH7vstCH5z462QkQypUYSDzBpV4Zzk5y6s3mZI%2BdGD1OMS3dlORL6h%2FR%2B3xOcNr6RpxJIPa5uRWkRdPQzZ6Nm29lf5Lfinl2ypuduEqQxqONXTatnD0HG9jQblU05erVU2%2B99f%2FEEzUL%2B%2F1uGTs397MxS%2B7YtDz%2FxwtzsfO%2BU4psZqMkeIVtnHNByAibW0GmBSxtctLd7iwZeNSYn1gJchaVBku9il8r9co82Ja9clCxDnKwNLs0IXQ6VLV4%2BOLx8%2BeOq7t%2FUVXVgmF14%2BYuGrN42MKqeVtnzHh627QZW8mHj01aNmxh794Lhz059ZEFD%2FCHvfj7JZN%2BN2XbM1Onbd8BiscDEJT9Fw8MDrdzWGSj0WYS9URPTS6LW%2FYmGSwW2So5HBScbqsz3UmsTqvThG7JlATlWg%2B33RHrzL7lpjuGUOGj1uaovjBEKnH2HjYCJfY6dmGv72BvYGd%2BARu7j1wgZ5vZ3Ma57Ec08RslQBKsgaxUVYkkUR726QUqUDlmFjgmiYqtbgjFLYRiI5p%2FYebmnxVpXPuF1kupUABdeGdcdiE4pdy0Dj5fmkmCgNS13E07lbRqK%2Fn1%2FmCviN%2Btt%2FWK6OGGznh%2Fs4t9I39VVFmLztSUlwuwZdCiRC2l%2FKk33lG0dHD%2FqprTbw5%2FZmTxqMV9Z8yYvelw%2FcCqjf%2F%2B6K9P9H9t4KLl7R%2BcvmJR99W%2Ff6Ggbs3LPQbRnMF1WW0mD5q1NDW4IJjSKdy5prTH%2BklDl%2BfctXrZxm5rs9r27dWuY8e8oqHTRvWb0MVZPfnuKWXOMUCwWLTQ8eKH6u5TWpiTanKAI8lnpW495N90QCAhzctKeI%2FFxVnZpaXZWcU4pzgrq7Q0K6tYnFrUrl1RYUFBYfwOQGEM7xzvEdt5hxKeSwWDXmrNT0936a1esbSDZAKH1ZRuIuCwOYjJYXKk5AWcoRQByhNPBdhblgFRMxHuG90bnN2obu8KDjc3eYHM1py5DiFU2NqhNXTQOXMWz10weE77sRWvffDZq0880vHB5vXv4PB3les1tv2D02z76xP2YNvdezD3pT3s7N497JOXhMCeTTu3t%2F2dq9X3n575qfMjIXZI%2FQ7b%2Fu6brOGD0zj0rT%2BwD%2F%2BwB3P2xr8GQKCCushU8W1OdzqUhlt5pRQDokeJazP8rQwGh88D1EYJNTvSOakf3feGku9qVGpqG4xTV8ojfbXWGSt18iYUtdZJXEnDlt0%2FedPztWvHjM%2BbtnB%2BHauecmLUlAeov2bk6HHjJkhCcGFoRIcJs1jnI2OaCgRBqd8NhFraSI%2BCBGbICTupxI21YNTrBbMkWKwmUYegHGS5WbPRiyhjVuw2EAfPVEriM1kjLsUhtexzTK9lO0kQ1%2Fdk29mzvXB9yo23qh9EHfeDXhAhJWwiKKAki0J1RCSQr20nattixUJOXfM71Bv9Hhc%2BCdeuaV3LRAIbAAjXdUoX16r7wqGgF3iOLui5Zpn1JodXKu1gsnFoi9Pi0DmtjnQHAR63E4fT4bythikCCP22ZKVVoUS%2Bhp0Bqm51Fnr%2BL2UjHz5YPXLwfRNx36B%2Bl3eeXrwWxYbNVy%2F8n%2BpGrtwd7tNtSfXsNFaLo9jTdPZ89ub%2FpXB47YrkEiRpzW3r%2BoJ09UfBJLnmAoG5dBi5LJ5U83Z%2F2GIGp7L7nGwzHPNQhS3J7yWaAKe27LkytvA6c%2FfPn39g4Oqa%2Bfun195VPX3qwLunC2vmH9i%2FoGZlTdOCgdOm3l0zdZoiv%2FGASic8yQYLAMhwBiA6Q93NqCLLub9OUmpcstOLaHGCwAsItnQvZqjyadHEUVx6cz%2B0JMt%2Bsjy645vIQH91edGont0XbPj9msiaPXiIVI2%2FNHhk35IePbMLh0yeP6V6%2FZPPA4KflKlzBqAsnGkVRaCONIPUOstxn%2FMhJ%2BnrRKMzxUmcTl2yP92s88eVhKvIfTe2KDHRmKtlyd%2F2PpPpA3vsPbRzw4w1sz%2F8snbmA6Or7%2Bw%2BpUPP8mXDl2wVvqx%2BwJu%2F%2FYmVHWb32L5q0oAeXXrkBYa2LZl5056LnkfvwhP6xD0X5YAIN3pyAOvaT85494494cnCD133dnN3O1oEqNZDegiV4IHicLJoMOhs4HS6dC6%2BLeC2ulLMRKks6LWkMWHX6XqfaELKyMnTOhsGs13PNCxJNkz%2BZ%2F0Qg6GhAeewK698pKaNLwyr2caOScrsU1mzMEJygRWCYYcgIoBopDa7TidSq4jaQa%2F8RJkG7MortqVTEvILI6Z9PL1rzacn%2F%2Fov0pY1S3t%2FraYhx5WrKDBA2ED6Yh0dqvitsEECMJuofkCEQsyAJOqq2jzatUOseZR82L1nz%2B7xMwlZzIVNAOBQIge7xQhgUfrILXa7jtog%2F71CzQq3qDNoZYbSkOzBpo31obZtOw24a8BDQx4ubWIXRk7UT9S1Kckrtu%2BbHgSEvqQKP1d3kPleHwFKDSZuX2mGBGlK3sc5EGO7FpnEzw8MXLlQ8pQsvpNv4K4ld9471NP2%2FhFAoDt1kaPi26q3zgo7lONnEnBvHfMfbr3iP964r4XTTjgzJSYsWHJ0V%2F3qF3eu3%2FB8lN07fsKwYRMeGCZM3nHw8LPP7T%2Bw%2FTH%2Bb%2FYjjwCBau4hdsY9BF%2BZRr1AgMrEoJdu5R%2F4fBhELEUxdqM72c5aTGef1%2BIQVnvjPTGxCb3wfhzek01IufGW24c%2BAOIZzq8gnCYLACAbHrsGKMNHNDV6EPR%2FosTBA8ziYuCw7Tjs%2BThseQz2CwV2Ou3PYeV9xMZBVchkAMkvnuAQM34FFf4CxEZ9KD5qXmxUIBBiM2mNMBxSoY3Sba1zpQWwlbVVwCXk5EIqmmhqKj93lzEgkm2zG3tH7IEWecP9w%2B9rGZ4ohslCYnXDUm9MGF2J0ihbnJBfkf59Rs7q4vv9Y9X1ozq9%2BdbRTwPhSMnYbk2zOnXtXqqkXKHH1tZM7NOvw5ip2e0XjzjcWDEhMjB%2FyIz70jFvcU%2FeGRvmVKrdoPJ0bltbq9R1v%2FYaDgTdn4hNzIa84ltA1MLCGETS7SCOQSAGkdoSIv86xGsg3HKMrOsQE6CUQxiaKGmtgtyAkWIwIMNxKIN5QK4xAIk3MIIVnNA%2FfAdPM%2BwIOhPaRNEtuvROycm7kHm7iMHM7wabASUqOtByowkglmHm5an5G8bOiYau9y%2FSAF7vYVQ2zqR5UUeUXdxLDtMT0SMkNXqR9Lhag0cfURpetbZG%2FAvZr2jRHOZSOkc5ztkqzrMIAf55rM9N5VmbON8PqhxBs8aRmyFqoTwG4b4dxLFrV2MQyS0hsq5DTACHylWC%2FhhXgUA%2BgFip9id54Z5wod3t1glmAKcgCUk%2BrogS11erXC6%2FJJ%2BWL8jcIsuyoNfbqiJ6Kri17tNEXW55EDWhHZV7uVhLarxnM5QhVqpNqbM3bcJ9eBf%2Bbn%2F07S9xNlt4lIyKtaWSunqyntWxHSQcba5nhhhNYrmqS%2B3jurSmJdWx7jiVLwUx3sKsmLb5bgdRi4YYhP92EMegKQaR3RIiX4PgeGy65RhZ1yEmwMdxnW4b5z7CQrQJJmEDGMEX1st6ino0mXXgy0%2B0x2rMHLeOu0ewbTh8BHua7RiLw9m2MThS2DCa%2F3fbaLyfPTsaR%2BCIsWwrAOXzv877434CJ6RAQFkZnnRvmsAPExtcAA6rqFMCF0%2Ba32f2945YHTpRoDazQHnjnES1lrm3%2BFq4%2BYgL%2Fygm0lglwc7fxSoM1BZEj3qKzovZ1zsLv1479tEH9ykddGe2jnx04rGmh6Mjpu%2F9zy%2FNwbFk68SdWpPhmOUDNr2FDyl9dMMXV699l61D26bmvgOVZjp2ZRN9qTc7xVdOrI9LlUxpXLoVMfk7Nb7fDFELp2MQKbeDOAZzYhAZLSGyrkNMgA3xlRNMtEfCbHWUTvF5CmKjOFSQeO%2FfrHjvH9%2BpMOtFUbKDBB6vWeALiC8fs96sl2LdkZoVarkRrHVH8v9lCDcaJGexM%2BzzQ42NZ9GHnuYrO3mL5LvvUdvFy4zXWq%2FB6ei%2FV%2B5Y9yQAqv0oW6R0aK94ppxcMTUAXpMJUu25YkGhw5Hbrl12RaQd5LrV3S5tj%2Bvm0xpaZCBL2vZIQjWCo6Q2%2F2lnOTKUqE%2F1UYJv5ZAOKb36Lxv32p%2BOTCrfUnn27ofnjujZq094yVz2TcPf%2Fv7%2B58IPi6dX3OnPyC0L3b917LZdPTcF8w%2F0mVQxcHZN%2BcTisqHF1YMuXO0r7Nv3562c52pXkOTnPL8TACXovgLUVWlXOH6L57V56vN2t3t%2B7FP1eajFc%2FGz689fe%2BUW3xc%2FvP58whegruiOKsCNGRZehzj%2BcwyiTQwCqAIhKbtXOVDENWdkOJQLre3tedlIaF%2BWlJTe3ghi5y4pbYNtKyK%2BAqGgV6RD66BdECyZQU%2BxzqKriLgsNtBaO9R97viBxZsNL1corarUot3Jy%2F%2BqHSkOv7bLFExMz5TiAMaaVIb%2Fwg7NmPnUc0VVb4%2Ba%2F3xO8a6Hj%2F0reqcOO967tWbwurHswpy73lz03Mt7Jg1ZtfPpwzvoK7OWGon8BOY%2F%2ByddrEUqp%2Fie%2B4eMYP%2F9%2ByRWGwjyVpav5k5sXH9%2F5MVNo2XdQ6Sw4ektO5V1zXc4lW4kzreeMU%2BJFaqnVDtxVIn1ikl8vyqRVppEbn5e21993vp2z4%2F9rD7PafGcS1R7PsEQk1d7TaLX%2FgqAo9URXolZHHYXKGOgqI3xIgApTICovZYRgzDHIa79iUMMSoA4xl6IQTg0iG84RDrHQ4OYwA4CqBbHZ9d89VRlx1zyq6euqsJ5fsnUqhXwYN5jsTttkj7YRp9eETFSj91nsfLIR0%2B9LqSttY3QmLJw6%2F3b430QyITiIlAqxdlBMcj%2FlHpUk%2B6gRVqnV4kwil39%2Be%2FsK5T%2F9sUYXdkp9n3vr4YN77ll3OW%2Bpzc8v7NpC3vppe0vPUtC7Ev2FzR%2FcQmlWcInr25%2BcGHXgtrefZ6cNHMlm8b%2BtaaRbXjh4Aku21jXgbraqmOrzaLyJC1RNqNUrt0Vk%2F1HquySb%2Fe8drD6PPN2z4%2Bp45Ngi%2Bd8fu35a9%2Ff4vtcJtrzCSkx3Wh3fS2Ph2YhR9gJVO1CD4WTPAaDTSACKjsZTifKZjMqJ%2FQQ8tX1yhOfG8nPjUN6iccXE96Pp8ejezqVFHXsFCrqot3J8iefZP%2Fq3KW8Y1m4nPwYfwOUY3tEGCUsjvv7PvxEa3orl8vQ6iZn76u47uxt1M%2Bb2Kjnf3P2ZWVxBdGcfXw7QXSpTl4Si1SnX6L2X2yaUjNt%2BDw0Xd40o6Z25NzmV4rxTJ9pvAljfYjl95r63Iuxboyetf0XbEBQGjL6zuy7cMOvu8aRRcWffLRjTHRO6DzXjNjutSq5e2KSf0PVDI8mmZuf107VNOfWz4851OeBFs%2B5ZLXnE%2FyxtZarrfrYDqw6wr2xGWIjpKsAWu%2BI2t%2BVyXex0jOkFJfNZpfsrQMOsKeYPHqqT%2BNdjB7q5euvRZPnb3oYUWsXUUomXo%2FW9JUVbx7J4HugOKR748Sz333%2Fyd8fMwk63mSElTs38OYRzF9LmyID2Efsvwpjn83sV86KdcDaFQ1NOXQi58u3ce%2FZMxo1nF6Nmgn7Y%2FTmxejV%2BpuEyuv9TaJArLfsb%2BIw6gkU6UvxFLggHe4Ot0uSrE5nKpjtqZKY4bc6eDxpBaOR51hGGj%2BVwg8UUAc4b5zk4det2ia1fWVJO2TlvZF9aafq7NnSl1EYN4y9zJ7BYRgeN5RaonxdR8%2BRfs09fmXXEH%2Becs89LqzDiTgeF3ljSZmwlZ1m55QTGn6hNi32qy1yujAU0iAXCmBQuG26zkI8nqx8t7tVlk4oDOW1Mbbh0RHvSCKixdiunWg32pIyxcyKCIieFj7YoVjVRAeseV9R9a0q5rdyvYktTFkxnyvWs%2FNzup6pu8B%2BROnrBae6djz2%2BInL0aAOq4Y%2Fe8%2BQDVf9G154buPm5xvWCb3mrjKRjN%2B7vp4xEwtQh3q8Y%2Ba0KbPYz19MYDO5tw1mkLIPz3985rOPP%2F10x9NP7wBEE68Q7pH8YFF6wGWwWXmN0KJs3CSfKkwsE%2FIgzx1QzhIE0DR3nLfB89CcmUMWLuFF2u%2BWPJGTu3C%2Bt3TBoiIAgpP5iG2lhdp%2BkEMyxSpMejflw753u9KSrHUfcfpp29njxj46a8zY3z3YPRTq3rmsqJu4b9TM2lGjps8c3qFLlw78AkQdn%2Bk78TN1N5wPn%2BSzg2gC%2FnKrZc73En4mKLYb3o4vKU6BwvQ0olRTQpJEXXkDB%2FTOLAxZRpmn39tucP%2FKjIL21tHmqcL5rLZZnbvMquO3Tl1n1aldEci5Ff%2FFEyCCePMvngykw%2BK%2FeMIh5f8VUtYgffQ49lB7%2BR0HUNTpQenhP6WBBkscHEs5y%2BQZ1WF29yx63DMUTVyicNM3RdTpRZly061Rq55Od5RisXIk%2FbGKDPGARzmLjqmfcouq%2Fe4LkcAKAEQZizSpY1khOWwS0KwXbHbQUZP2M1%2Bx3pUgbyrhA%2FvjeGG9tcNjs9M6maNnb2B4FnXTeR1Tw7TF6DZldL0ZRcHuMIs2WRn9LW10DWe%2Fei9JQJ4ELUkjOsxJ7m6%2BQYbnXvbTY2Ow6D6FHh%2F7lTTBZZSVLOtqB8g4iCCHzeZK%2BdC1Y38ymWJ3vb5SBnteXszG7cAfyXB6EYzgPBD%2FURrIP3Wr6u%2BOqQ9OmDF94qRp5JtZj%2F9u9sx5C%2Ficym8TiHvgB8gGOwAEwU4c%2FM4nELJA1RaoJelK5ZPTbBAIlYikk0WuCInpvPM3e2CJ%2B16ASv2UpGqjUBAIkMRRWhRNSeqtK6QAyGYBkJXxUyYgEkE7ZYLxAQJIVjbPWkkXx4%2BZIJRzr1gnnuT0TQ2Xp3rTPZ5kI5Hl5NZ2wZDslYJtjN4kb%2F%2BILklMTUvtHyFp1rT0tPw0qqdJaUlpzsxM6BvJlJ0W3iDhg5ZN3bwwdMsfKruRW2ZQbuRlt9evdcorVpPyolGwuJT%2FdUDsCHUKOz4AWfRHQvA065Z1snHLxtW7%2FoddaNewgZANO4LY%2Bn9OPN%2BrQSxmD80rC7ed1%2FRm9%2FpuaEacl3tH9TwUsfXIpYPVzprl6o4iBXdYT0AUtDAtYc3y%2BEuJtrjkUwGEVlI650ylKvE%2B5ABA%2FHNTwuf9lc%2BBgItUcf0%2FAgZwQedwuks0ypTyaYjSqY%2BiqLe60l3E5aIWOZ1mxPuV70toergeGwR4g0v8V2eKi0otVJZJ05xV7GHcsHQO%2B0ESk9LSjDup6913x%2FKzVKdeX9THFGzb1v5TDDfpQ45bECoJ9%2B43cBcf0nCXXr%2FF8%2F43notvxJ6rVEnqc1TWG05X9cp%2BAAQRKWiHl2Knck80KgqljCAC4Aq1QvJpPHP6XaxCImp1FiUv6pwAUXstt2Ud9NrbHGJCAsQx9ufEKktsFtJBzroOMYF9EK%2FV%2BGK1mv8PflNJUQAAAAABAAAAARmahXJJOF8PPPUACQgAAAAAAMk1MYsAAAAAyehMTPua%2FdUJoghiAAAACQACAAAAAAAAeAFjYGRg4Oj9u4KBgXPN71n%2FqjkXAUVQwU0Ap6sHhAB4AW2SA6wYQRRF786%2B2d3atm3b9ldQ27atsG6D2mFt2zaC2ra2d%2FYbSU7u6C3OG7mIowAgGQFlKIBldiXM1CVQQRZiurMEffRtDLVOYqbqhBBSS%2Fohgnt9rG%2BooxYiTOXDMvUBGbnWixwgPUgnUoLMJCOj5n1IP3Oe1ImajzZpD0YOtxzG6rSALoOzOiUm6ps4K8NJPs6vc%2F4cZ1UBv4u85FoRnHWr4azjkRqYKFej8hP3eqCfDER61uyT44DbBzlkBTwZD8h8%2FsMabOD3ZmFWkAiUs5f4f2SFNZfv6iTPscW%2BjOHynEzEcLULuaQbivCdW5SDNcrx50uFYLzFHYotZl1umvNM1tgNWX%2BV%2F3gdebi3ThTgVEMWKYci4kHZhxBie3TYx3rHbGr%2BPdo7x4dIHTKe5DFn%2BO%2Fj%2BW2VnE3ooW6isf0LIUENvZs1gf%2FLHojJwdpplCP5gn%2F5gi26FoYa19ZVFOJ6Sxuoz%2Fq2Ti20IKVJdnqvYJwnhfPH%2F2f6YHoQF30aZaK9J8T026RxH5fA%2FWPW%2F8IW4zkpnIfoFLifGB86v0ffm5nbyRs5iaHR3hNBD0HSfTzoPugRM%2BhdN0x052KoHLBS0tdgpidAiEesDsgWYO73RWQz2LWIwjqnMe%2FuYISQtlbyf2NlT9Q9PoBcBnrO6I5ELoMeyHkNnIXGdv809H%2FDXNOTeAEc0jWMJFcQxvFnto%2F5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56%2F%2FCz%2BVaqqrat5rY8x7xnzxl3nvo%2B27jFnz8c%2FmI9Nmh2XBdMsilrBitsnD9rI8aiN5DI%2FjSftC9mIf9pMfIB4kHiI%2BhWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW%2FgLbzNbnfwLt7DJ%2Fp0TX4%2BUucji1hCnY%2FU%2BcijVB7D46jzkb3Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV%2FEaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK%2FUVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN%2B59b410iF0sUFO0l2UJtY%2F8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC%2FLzdhmV2XBvpBF25IlLJOvEFfRI%2BNjgCFGGGNK5Rs6Z7Ij%2F45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j%2F%2B58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go%2Flfr05F%2BUa7CCzGx10sYA9tiWLxCWs2BfyN%2BIa1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt%2FQOZPfmY3%2F%2FSs3Y5tNpTpL9ZQeGR8DDDHCGN%2FwbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h%2B1FeZTKY3gcT2KvTWUf9pMZIB4kHiI%2BxcQzxGfpfA7P4wW8yG4eT%2FkYYIgRxvgb9TWsYwObmOAITlI%2Fxf7TOIOzOIfzuEDlIi7hMq7gFbyK1%2FA63sBbeJtvdwfv4j28zyaP8QmVL%2FimL%2FENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI%2FhcTzJp73Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe%2FgY%2F%2Begvq0YCAEoCNa1n%2BKVyTUl3Q0uIhoe%2B3DnRfV7nXGOc5zjHOc4xznO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Z9A7%2BtETl5RXdNNZGDm%2BvXYXWjgLDRzEhoLBAYv0%2F0NHAAAAAADBQ8CvAAFAAgFmgUzAAABHwWaBTMAAAPRAGYB%2FAgCAgsIBgMFBAICBOAAAu9AACBbAAAAKAAAAAAxQVNDACAAIP%2F9Bh%2F%2BFACECI0CWCAAAZ8AAAAABF4FtgAAACAAA3gBY2BgYGRgBmIGBh4GFoYDQFqHQYGBBcjzYPBkqGM4zXCe4T%2BjIWMw0zGmW0x3FEQUpBTkFJQU1BSsFFwUShTWKAn9%2Fw%2FUpQBU7cWwgOEMwwWg6iCoamEFCQUZsGpLhOr%2Fjxn6%2Fz%2F6f5CB9%2F%2Fe%2Fz3%2Fc%2F7%2B%2Bvv877MHGx6sfbDmwcoHyx5MedD9IOGByr39QHeRAABARzfieAFjE2EQZ2Bg3QYkS1m3sZ5lQAEscUDxagaG%2F29APAT5TwRIgnSJ%2Fpny%2F%2FW%2F%2Fv8P%2Fu0Bigj9C2MgC3BAqKcM3xgZGLUZLjNsYmQCsoGY4S3DfYZNDAyMIQAKyCHTAAAAeAGNVEd320YQ3oUaqwO66gUpi6wpN9K9V4QEYCquKnxvoTRA7VE5%2BZLemEvKyvkvA%2BtC%2BeRj6m9Iv0VH5%2BrMLEiml1XhzPdNn3n0rj6%2FEKn2%2FNzszO1bN29cv%2FbcdOtqGPjNxrPelcuXLl44f%2B7smdOnjh09crhe279vqrpXPuM%2BPbmzYj%2B2rVws5HMT42OjIxZnNQE8DmCkKiphIgOZtOo1EUx2%2FHotkGEMIhGAH6NTstUykExAxAKmEqSGMFl6aLn6J0svs%2FSGltwWF9lFSiEFfO1L0eMLMwrlT30ZCdgy8g2S0cMoZVRcFz1MVVStCCB8raOD2Md4abHQlM2VQr3G0kIRxSJKsF%2FeSfn%2By9wI1v7gfGqxXBmDUKdBsgy3Z1TgO64b1WvTsE36hmJNExLGmzBhQoo1Kp2ti7T2QN%2Ft2WwxPlRalsvJCwpGEvTVI4HWH0HlEByQPhx468dJ7HwFatIP4BBFvTY7zHPtt5Qcxqq2FPohw3bk1s9%2FRJI%2BMl61HzISwWoCn1UuPSfEWWsdShHqWCe9R91FKWyp01JJ3wlw3Oy2Ao74%2FXUHwrsR2HGHn4%2F6rYez12DHzPMKrGooOgki%2BHtFumcdtzK0uf1PNMOxwDhN2HVpDOs9jy2iAt0ZlemCLTr3mHfkUARWTMyDAbOrTUx3wAzdY%2BniaOaUhtHq9LIMcOLrCXQXQSSv0GKkDdt%2BcVypt1fEuSORsRUwgrZrAsamYJy8fu%2BAd0Mu2iYFhexjy9FIVLaLcxLDUJxABnH%2F97XOJAYQOOjWoewQ5hV4Pgpe0t9YkB49gh5JjAtb880y4Yi8AztlY7hdKitYm1PGpe8GO5vA4qW%2BFxwJfMosAk2X9n9X2cVVfnA36pzHNHJGbbITj75NTwpn4wQ7ySKfAu9u4kVOBVotr8LTsbMMIl4VynHBizBEJNVKBAfMNA9867j0InNX8%2BranLw2s6DOmqIHBIbDfQR%2FCiOVk4XBY4VcNSeU5YxEaGgjIEIUZOMi%2FoeJag4mEB3PUOweCaG4wwbWWAYcEMGKn9mR%2FsegY3R6zdYg2jipGKfZctzINQ%2FvxkJa9BOjR44W0OpTKAskcnjLTcKyuU%2FSVIWSKzKSHQHebYW9mfGYjfSHYfbT3%2Bv877XhsIwGzEUaleEwITyE2u%2F0q0Yfqq0%2F0dMDWuicvDanKbjsB2RY%2BTQwOnfvbMUhiNPFyDCRwhZhdjE69Ty6FjoOoeX0spZz6qKxxu%2Bed523KNd2do1fm2%2FUa6nFGqnkH8%2BkHv94bkFt2oyJj%2BfVPYtbzbgRpXuRU5uCMc%2BgFqEIGkWQQpFmUckZe2fTY6xr2FEDGH2px5nBcgOMs6WelWF2lmiKEiFjITOaMd7AehSxXIZ1DWZeymhkXmHMy3l5r2SVLSflBN1D5D5nLM%2FZRomXuZOi16yBe7yb5j0ns%2BiihRdlFbd%2FS91eUBslhm7mPyZq0MNzmezgspUUgVimQ3kn6ug48mntu3E1%2BMuBy8u4JnkZCxkvQUGuNKAoG4RfIfxKho8TPoEnyndzdO%2Fi7m8Dpwt4XrnSBvH45462t2hTEX4Bafun%2Bq8jIzK%2FAAEAAgAIAAr%2F%2FwAPeAF8egd8lFXW9zn3PmX6PNMnPZNJMRRDMkzmDYgZMRRDCEmMMUPJIgZEepHlRYyIiNhRUdYuS4ksy9reLDYsdOmLLC%2FLy7L2CgKrrCJkLt%2B9T2YyYPl%2BD8804J5zT%2Fn%2FzznPBQKbACSTvAEoqJAdtUhUJpQYjBJVAUrKSkIOJ1ZUOEKOUGkfV8ARiPB7E72m87WJZF58ibzhXPVE6QsAAnMufI4H9XXsUBh1UpOJSJLmQNWqNsasLkKhsrKnA%2FT1HCF9PQzSAPYtD5V5PW4lmFeIK86EcCRbObLp2lGjGxpH4%2Bf0wLkjjU3NDSNGxYSMxbSdDkzomhE1SypQalCISvniob1lDuTL7injC1O%2BMr%2FxmeJtxeRt%2FiJviJ8mmrjFOr0BJCZ3QAbkQFu0ypCZ45HcRqNJQkiT%2FLKsOO02s2Ryudze7CxVUnw%2Bv9%2BtmKTcgEEymzPRlgN2e5rHaeOXyeeiisnJFagMOSsqSkr45kL8Tr450SfM5%2Fy1V66pGvBwTV1BcYcDEX67QjQkbo8cigTplyVI2OHh%2F6zdXHO4%2BiR6SjoxMPzo8O21h2tPx7O2lmylNV%2FtY5Nwubj3fXUA%2F8BuFveBr74CoNB84V6pSnFCLhRCL7g7OijfR7Oy3FalR49AcXYRFBnsQUcgkAYO6H15j6wiAGu%2BI%2BAo6pleFDAWKJZMX%2BaImNunWOpiskIVH796ewAqEzvV9gqX9nQ4Qd8S%2F1V%2FScSM%2FrmsTP9FfNUNIvzuVlRPMFxY5PB6fY6iwsJw3%2FJIOOTx%2BlT%2BWzaR%2BxYWecrR7fWFFanqi%2F33nnn9%2Bv%2BMvXr7mk933%2Fv5Gy3PrN6yZjg7WFV1D5s2oGoh7nx%2Bk2vvTrkeDT0HKlieXvvakkfecj%2F5uKnhm6iNHRk27a6bevTL%2BclH3ulVkX3cBTJUXjip%2FCDvBiO4wQ95PB6qo%2Flen0%2BWTRpofo8nLa04mB3UgpeX5PbMLEzzKz4%2FtapOlXt5a1llpXhN7FF7r8zJ37o%2FiN15Q2XhvsE8RdajOqwFyrwFGETXr%2F0F9u9dNnZsWW9869X1azow9qe%2Fkpc7D52mPRf%2F%2FHcJFrR1npvf9sWX336EO7%2F9x7lqeUMn6frt8y%2B%2F%2FZD%2FJjzecOGEAnxvWdzjpTAzWtHbGjRhlhdMXqvLVZSWnl5kpSoChLJVtcwXSPea8vNLSrT0dEnTegyPaZIUqIlJLnSKhAV%2FpfBuhb9EbE53bYVIM%2F3S45hfiZ%2B7th8IFPHN5QuXcscms1vF8kiAZ2qBsEEEFQX7FnJDeNy%2B8nIF2JLZ7%2F77DPtk3rJhVV9vefPD%2B57CzCF98cr82%2Bs631s4%2FvbxrKPf1XjT0Iqrh%2F%2BuafTMxR%2B9e%2B%2BmxqZnxzzx5l8embstxo7PeX0Ju3DjoqYJA7C611hyd3hAtH%2FzpD5jAAVm4DM6Zjj5C5WIAIu9DuxCIB0kuvEBAKGBbSTz%2BL%2B3Qm7UZjaZqCSBqtrN%2BVQgmAMTua3joeaMhBTicTt9wULS8PSj5x58eNk9Z5c9RUrRiPte3MTKzvyHRd5Yh9vFygP4yq3JlfmyfHG%2Bso1LyP%2F5yqgRNVjuDPclRSGvk7Q%2B%2FejZJY89%2FOA5sTT7ifVb%2Bzru%2FOEM7tv0EisFhErSJGUpbrBBOOo3ms0ypVZUVc0umUyqilarYrDxpN1aJrKQuykJwvwz%2FyPMUOCTXSqlRa6CiEzJy8U4J8DWf%2FjpM%2FeeOMZeLMKpxYqbPTyx088Oz8MKtnMuFqefm4gzAKEZPpUqpG1g5qivGRSjkSKAxWo2giJRKOFCysqS4vjNhQXCAa4Bxz1HEI%2ByNlx0FBextqOk9SjezW49yhaIHbGzuBtOggKe1wgFWVapDCXbdSNt5ghfoNCgMxLA3X1v%2B%2BdV%2Beg%2FvIsdR9MJYWVcS5rISqDg%2BCuVQQLkSiTc7QoHPANIGq49dw6wi7GwgmvujZoUrrSRNsaMLqjsmfjnkYu4aU6SlJZ28xECNyqt0mMrM2pBricBidueiNS5iDcRA0ir4h%2By4yQgGJP%2FDwLVF05IQ%2BW9XLoPLou6LYoTFPCnGT0jYkaV2kfEaBok8y%2B1kkYCeeDQnIEyQI2nUrlDE3kkDT3PzsfZhXMoxZHGw2OmTRl7w%2BSpLeQoW8gexttwNi7C6ewO9hD7%2FusTaELr8eOAMA%2BA1nJtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae%2F8Tw%2F1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD%2BEQLt4TkOo6CU5f1drrhvrrVly%2FdspDayfe%2B8EtQx7fuJG0HcbZLyyc1r%2B5qXbojtE1xa0dt4x%2F5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8%2Fvq4tZ%2FGilfr8pX7VqJm1EzJQGeg3j5%2FxX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j%2Fo2ykPlEmSr335KxvYPvbZydev29P65KNrX58%2Bc92zfxv6%2BKil76PnU1Sl6fe%2Bl694%2F%2FzIweMjUO1ZPnH2TU3fxqa09%2Bl%2F6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5%2B98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4%2FHXxjyKYhkRU9LgYsIJ6e%2BpgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT%2Fn6efcC%2BPLN8xActmMGOhu%2B4bH6EpsV%2FyAgOoO0n9%2F%2BHnR2B5h7hr455LAPJ1%2Bwc%2B1i1AYGhXOs6eQf4IR%2BuigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd%2Fgvy8Q3E6TmsbErv%2B%2BZ2tRuuN%2F7f1X%2BzsNyv%2FvYhoN066sbVlcRuZiq%2FiWvuP7rEb%2F7LuhyPfsFPLMffdxfMnz7%2B1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U%2Bo3%2BQo%2FHnp1ttBtL%2BihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0%2FLqoW8qk7kShFiku3kK9cfCPVHyDedt%2FqpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg%2Bflxtq7zz3ZUzXhrU%2FO6sjqN73mrbXD2iY%2FKzm89vbBp7Y%2F3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR%2BNfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP%2F3%2BJIOl3xOz3v0nmr9Y%2Bf2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r%2B83%2FH0fEE%2B3DTXbdNum1%2BHfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU%2BxG7xcf6mPNQAQX6%2FIUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK%2FVYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304%2FFkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA%2BB2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m%2F7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44%2BjrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o%2BUfT%2Fqt9NdGau79%2FZhf73%2BexCP2T2Pz%2FZefZXez6I%2FgIyv%2FEkRs7Yf3IFpM1FG27n5x%2B%2BNQ9Q%2FotPPTGQSQBH%2FPd%2F9Yf%2Fvjjne1sx152gh0p6f3eKHwYW3%2FEZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik%2BLHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI%2Ft4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn%2FC%2FfPZh2fjx7C84%2FaZ8xev2nXHraxT3vDKpkVrHaacdQ%2B%2B%2FxGdXTuy8Zr4NrZo3PgNgDCXI%2FUBnh9eKI36VZeLN%2BNWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2%2B0Iw4l2BUdoTI%2BZiikBS%2B9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U%2FHmW846xyGi3DSZ3j9azd1FvUDImwoz%2BE2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO%2FxGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt%2Br%2BzlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr%2ByVfg43vTxgZtW%2FGXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu%2FUg004jIzW%2BQJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr%2FNdmE5uRrDZG78Xj5t2EIGAOCFiawBT%2BozgRw%2BbSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS%2BtfxVS0wMJpjIcRkNiOAzUBl2cq%2FUrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6%2FHf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI%2FEnAKwL1KEcM%2FJO9nv43fpSiwh81U7%2BqQGdrQtXseFv4FZvycdQPQ8%2BVKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj%2Bkj3BXubXE3gz1vNr%2FPlDb76Bs9nSNzaSY%2BxxdivejVP5tZCj0mP%2FOYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr%2FTSzoRn%2FZLXHoEyAo4ckJSx%2Bau%2BBBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb%2FgchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe%2BLX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C%2FWaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm%2BPz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n%2BRlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq%2BuhvwVHHWrRpn%2BIvGGoVFl%2FMhDadWMcJP9LZen9cr%2Bdin7JuOx%2FZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f%2Flkz5g4srx3e8%2Borqypveeqmzf8qL%2F13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k%2BdXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6%2B6CYLnlEXbyLfYFex%2FD9zz7BAf0ztqVZ%2B7EwHn6YufCPz33%2FDraBqjXfyHBI2K%2BRonRKAOiVZYkC3BDJ%2Bq9VNpUJOaj%2BsXtVx6h57CC2dmLTMMKdPlKFXO0a4DY%2BdTwvZeN%2FqJLhrqRy8gSsx%2BT0e52yQh%2Bv2ynlszMrKwci9mcnemSzdRvt6NJiOSi%2BEtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy%2Fnm1hKoRixV%2FmpUJxwVT85trLi1EAa6twb%2BaS%2B9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG%2BooUZ3VpjZj8Izex59h6fnXg56wfNmF%2FDGMtC5Pi%2BGHyHdka%2F47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA%2FLwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m%2FRXi%2F0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt%2FZIKYnGpfnBa6sUmAHY9%2FmJhqI4S4fJ%2BQL55xoKIY%2BVYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd%2FUoR8AzFIA%2BR3QIHeUTdBWVYkSTznFd6SVJko0DW%2BxLKLeyTRZYcwiGjADQ%2FjqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl%2F%2FvMAAGKNdIQ%2BqATAwK1gBjVKRVTIdwCUpB%2FrioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ%2Fz7GYIdmjju9pfm6E8Zq6OFTovBQvLy%2FP78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az%2Fy%2BZVYSeXlcUJWdMvMcN9%2B1u8h0omny9N6YT%2BhuGr1r0xzd%2BOr%2F5xbv%2FOn7T8Y9PswO%2FX3znY5MWPHHDsNfXvfono1K6rn7f%2BK3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX%2B7YNsMT%2BhpLLZbJM2ZOJJNvaZk%2BQ5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM%2B%2BlZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf%2FBTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi%2BobTZhqFy7xwIOIyi6fTTdPt5ft4%2BoT4Q%2BecShOXlPGioU%2FBLkji3iOnVPiAnZ9vHnOw9ON%2Fmw7Jv%2B1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y%2BDMEKROSUpzOLKcJlOm3%2BOkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs%2B%2FY3zDt7LdoiJfID985vmNc3Qb61PyZM%2Bd3NmAPdGAahth3Jx%2B789Eel5%2B4rCjB7nSOkgMeuCKa7SZElSn1%2BqwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l%2B9eu3bj%2Fufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz%2Bn4pxIOWz79gRHv0LneqJs2FQ76ewKfPao%2BpSsqEvmsj%2BykQFfCF6ZeRcGFyUQK8v26El%2F4WGzqS33OfxjpXbL2ndc3sTfYvm9%2BvP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5%2Fdrrmura10otT%2FceNqZjJ5Xzew187smt%2F1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D%2FJfU5336yUOPaKqJJEuCQeFQirWX7O%2B6YxfZjqapqE%2F61bQ958LsXt8S%2F40CwpeDekav%2Fvh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ%2FZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd%2FL0JgaDT%2FNcE9PAaBctOk%2FsjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd%2BeJXN6mThXRIWPMH%2FUn%2BNdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7%2BG7krl7%2BOC8ePqbjJDCRIiEfKFykdziVfBd8q%2Bke9n%2B%2BuvnTGL7vy529F437Xwso%2FdL097ZwvbVXz9jOnlw3rz12%2BLfSS1Lh1%2B%2FurZpy%2BF4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV%2FXLRG8TS0ierkyof%2BZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu%2FxUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl%2FI6Xt6%2F70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl%2BRVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p%2FtaZv8wfaPbt58TGf7vePdSx%2Fwsv5K9SPtbB87%2FT%2Fs7H10mU722JDgM67pTN1euaIq8dIsyh%2BTpOUZ%2Bfg6PcNnz%2FZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook%2BLIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9%2B6xqU%2FbcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy%2BJIqpdWewrCD%2B6iu3%2Fsre97yvSbCP7xLR8SXyH1LKxZTYkqp%2F1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX%2BvJ2%2F2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1%2FbDrDtW395oycuG8yJd%2F3%2FXu6MZHa5Zcv2zRrf2wZn1HILfzsvKx%2Bb0rCstHz73%2B8VXN%2F8y%2F%2FJriK%2FqHR%2F%2B30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu%2BTD4aeiNN0jGwgknTn4e%2F%2FyqK4UOT%2FGc4zM%2BcENZ1E8cDrfby3t%2Fj9NoJ7JNtumyPcmJ1sVDgItr7tQYgH%2BgrxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt%2FEpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X%2Fx0SbTfiOtaElf%2FINyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin%2FpGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH%2FPGvvE03jMkcer4XnuKKI5gApOW6bWqi%2BYoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu%2FmD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy%2BDRewhcJLzK17ycs1KTC73ZrXK0koahm%2FJgob%2FpNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu%2BvcfR9RErqqfl%2Bfkfr2%2FYcZNo8ic866XXnR8Z72xNZI450HXce2MIn%2BoKqkIYDYgmvQhAm8c7YR%2FMwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6%2B3xekDI4T38Id7D54ipCHUbcnIcfn%2BuNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm%2BREWRw08Coc18VTQ4xFQ6%2BEJhDmL2m6%2Fc%2FOZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg%2BknbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl%2F3%2F2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2%2B9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG%2FAg4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv%2FRAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii%2Bnphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA%2Bn4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8%2BePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN%2BrL%2FfHcyhjZk77c8arjmflv%2FBn9kZObzbAuFFEB4A0ST%2Bd2BztZXeaidFqTfd6iV%2FzO51ado7Fn%2BavjxnT0sDFqcleG3P6QR7xs%2BNNXUfUIJTSVqjbjT%2BpBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3%2Fmvbhr%2B6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM%2FOyYp9LSycWo2xQPZ4ctYBHz%2FYyHc11H2qb9S%2BiA4oURXyC3SM%2B0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt%2BNn93Y9siC8v1T6%2BqnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo%2FiNtYyYOnz6ttm7aDBHpCoDEp%2BPghZnR%2F7I53U6Plce2UaYyMYkJqxeRED%2FHBp%2FidDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448%2FvsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw%2F0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO%2FObSe%2B8uy39mVlqEzr76oeyi%2BbG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb%2Fbpj257c%2BOS5s4tvmrim7appHXPputbn8kPlVdURssit194%2FxklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0%2FYrQaWIvIdGcQ4pBB7dzU9snCdpLZJF%2FSOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0%2F7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7%2Fc7yzPc%2BbV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul%2Bc91p4c3j55tf%2FfvilPddGFx5b8zJqf5X9DCi9v%2Fm10vvcrj6U09uHsg%2F0Ke%2F29invHSBfX7VJ%2BTAv99nwkcNvfNd82xjlI%2F4%2FSu%2BrLyi3%2FObXaPaLTJb0b6xlBfCX%2BDHKMLqgAOoieZk65HLlmXXU56PLK%2FRmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy%2FKbYvMvxsYvGRLrPu%2FBCWegef0l%2BcNcmpeGP%2FqIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI%2BK5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12%2FoayC49HL1E%2Fs9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW%2BS5kUqBYFHUgcViJEbamxB9uT%2FROLFE8QLBIegdsp5%2BnaSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h%2FcO3K%2Bs89eFTJuceP%2Bdft9utd0xUFqDpyj3kqh3K1%2BH6uhrlzX%2FZctHQEckuSNLhJG8MjPTGCNLRbwWDZH%2BFr%2F6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg%2B7W0766bZL10emd02YWJ7G%2BNFAYSwiCGdcx%2BZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2%2Fh3kG0RVlFY12UNylYiiCAo%2FgZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF%2BJnjK%2F5%2BbriipGXeqiuf3np9%2B4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo%2BIr4APJkRy7enY7QbIl%2FLTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9%2Fn1P7N541ards5oXtJ3REgwFWsOdE%2Fb9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn%2BqLI0JEDyCnCdwXdyjui3uFjR%2FVNMjMIUk6ao6YiGZWHZ0i%2FDX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR%2FdrXNlOGl7yiCq4Y2Z%2BzTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF%2Bvigy%2Byj67YoVdz%2FPrAF7Z8wo%2F9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1%2F72IW5Tjkmn%2F3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr%2F%2B2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1%2F3PjAIiZPQYprQ3RWgnr3Xd88KXuOu%2FGW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD%2BWfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw%2FxTGK3kxc9YlKPgt969IarH3%2FwwP4nFG9dY%2BPEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79%2FOej1B9UBL%2B6NL0aoFonqQehRdg4ip%2FLxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y%2BTuAYvg1fgUA6eAP5Nrjj4L8IMFW%2BuJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as%2BEb9qmXpVwDBeWUH%2BLLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6%2F%2FYLru%2Bj0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2%2F%2FheIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj%2FJFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9%2FghbKIUT%2BhoLlq5jiPvcYqZDnXNrC6WKXZGjNP8%2BVlGYAXOBfY556p5%2BZaodTT0KC89ZE%2BUXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia%2BgnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0%2BBgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9%2Bd%2BCzzzahATIdd1Du%2FG5GoOPWnR9%2BofQoyl1qHsRXeDuriLez36eUA%2BdUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL%2BuB%2BVocqr6Rhetkf5tfL7vfj3qKxH%2BSMavZf%2B%2BVuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW%2BTMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO%2FyqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1%2FZBiOG0%2BWqzyvXWXXHDpANrEwNWGNfM3DSi%2FfHYJ%2Frbsp%2B8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv%2FagMUij5c2Vc18rz5%2FNZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH%2FaTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1%2FRVfoo3fmXTau%2BubIbfnTo2vnNjQ0TVjXsWQjbb4%2BhL9FfuGvkV%2BcNqai1JldVTJn7srmu%2B7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv%2FLO%2FazH5ASbVEBr5VQ%2BUtQfAPb2jbbEazY1vfvCE6Xna%2BkHfxhi6RUj001a%2BkAasPTikemClt4lAX%2B3T%2BGCYcUDmqJ%2FlKrwqwogTCEpQjeUQBBOgS2RydU1JDM%2FP2g3GoNBuabG7%2FGMKZPlsC%2FfW50fjVVXsyDp7OxQNJZtNo6aSoF3p%2BS0NFDHPHgbYiBJgQZGv%2FERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19%2F03NdfPRtr2nHzvKc%2Femdx%2Fd3LDyM4XkaJq%2Bcfm%2FbY8bqFq1fv6FyOvX%2B1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T%2F05acLVrNap952xtZhP0xWx0%2B0iY%2BfnCrjtT1FbQ2389oqStRWanr34n%2BeflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA%2Fy5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ%2BkEY0tOTnr6qEKLpcQNmZh%2BSJ2ImdjppB56CnnSKS02%2BRpiJifBU2MEnYC8izsQ2clwI9I%2B1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5%2F0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n%2Fm5lz4fKh8pLyPrJR8gyV%2Bhdtuva4%2FMv2Lj1ih27%2Blg74MwMf2tPV9%2FaEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS%2BgUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2%2BK1tf%2FPFpXNzVaf2ojO%2BKOwcEvTpva%2FPOG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx%2FC%2BzhmCNsJ9hQWsvOLVn0n5GBm1eUrt%2FzK5jR21o%2FOiJKy9AhwzKa%2F6alefjSoYJlXV2dVyL7IwUqpp%2BQes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np977PkV5qi9zXh%2BBQ8XJOgmziYWsLhqD%2B1vHQZzli2Dxi8VWsCcbXDIRM6dEpOdxEnL%2BCQocxLLTDtnDWdWTT4Wyh0nAU7ot8Herhf%2F%2FuZLf5xv0ulUfvGjOONEDrXMYEgzK%2BCtE9qVsXpQVixvbB7mnLQ8CVqeut5Qc%2F0zNdcJKk9oH6byMk5M5VGJGk2mO108BE7wQmekxuJwGFF%2Bvs6WAeDL0umKLHa6drMgI7HQX0YznaWSNBddcwhCLotpRQ5tBcd%2BThplmiAy%2BBMMx2M6XcOLuERnVGvx%2B3WnH9vn31Wm9Cv3oTPQhPGbvaRDW9Q9dstdd%2FXVrfR7t8jpaBvqQuejTSZZXeCR145%2B8%2B1PDivZbnPyN%2BhT3SphMXhgNARhQWRMoMKEHQ6%2FX19RkWu3V%2BXr9aEchzvgiMYCATCbfxaNmc3YJNDOmfLEZnDT4VwQvFNiQupwHj45Cp00iOdT56kG4bniI7dDo6KTeT2fSk%2BLtyhf7dl5pPfHLSgb4QUvT7nsi2%2BR%2BbhTt2fL%2BU90tDx99FwN5Pu4fbWMBnC3%2FZprdiD9%2FciByqY1XcvYaf26naXlbOCeHGf7BhavuJhFHD0h%2FFXwSAVgZP0Zi5ozAMh6jE0ZWF4vsh39sg5pyx2NKqQzEZ2XGU%2BdFNAgrdc1Ne977elTUafn6kbhr2ed0XJ29tMLqh5sYBENqFX4M4lKD8Q9ehmS1eqmkUWyR8ay7CDxvRTYHVKNZ7qk8YhEdy1YcOklCy%2B67Pqa0tKaiorSGvGlCzavv%2BiCDZu7ykKhsrKqKkDwa%2BHPgkEygQuqIm4KNEUEQjLdBhvobPTrYvM6MzavFyCQ9fpZmoNENQebXw6qkISXvbF5mNVHiE23yjF6xRM27knfvXTUtKZoET%2B%2FfAk7F%2Buray7vKyjOr%2BKHAr4bGHqI3IN7%2BG5S%2BAS7SU0nbeih999Xlbp%2FqtQllG7Sj%2Fp4jIw7kiaIOqTTySBou5KZB5gLq7jGWhvCumKTs7N6sN5L%2Bp1zkG2h8t3HkHQFCVwRmQhIknSCRC8wvD8WUrffQHtNwbWDkz3iI84XlPdRySFI3luLeVIwEfnuWhIEtNuffHstwOzeZBl%2F%2BgzwRczUIGsiggSSZNFlkHRtI0Z%2BoT8E%2BbOoWSnwxY%2FoUzVPdILhSZyRP8ezp2Vz%2BE4SGJn%2FndpNDXwrMFMaMYjsRi%2BqN9Luoz60qB5QH885cqO31JNM8Ua1DBJFgVlJkOt5SRihMGIaeQcIpN7Ap91gROGgt0eWkkvbi2wunXrfKIyCdLA9wszuRplAgHssUq3uc6%2FavnXvvku37cGf9hzou3r%2FLbcAELbTizQXhfm75mXsYF6m6kEvys4gbKuXAofMQuS5LUhtbJnmP9AJy8gdX3yp56m7v%2BAps89kZzPacGPqPmctKUf%2BVkA7vpHbtCsijrgDV9RLQAg9pa0JI9VZmsxW0W%2FVN5vqlE12xKZeO24nRzp2bfoHPRPEf7z2SBs4vvHEBm8ApCxj83oe25YVSSeAEcaCFtqW8B8j5EX48mN%2F%2FIKMjge2AeK7BW0S%2B6EYdkQaJaL3%2BXI8RW5ntmywWIrSafaLika5cnP12dklBpdLzpRy83Knx0heRt66PJxOMvMy82yFPiiEabFCndlkMzXHbNp2YiNNoxZenyxzKUghO%2FCtQOhvro%2FH5DgKdA420DrVfS4oWELdb%2F7qWvq7BuL7XXhXXu9CVyrtGKN5yj0hZNq9ecn93ynPj9q6VMBLtvjQpG%2Be6ps7ebnwys5f3ucNFDzwTXgIxqK0Tx5wFVff9zVyT%2F%2FQ4%2BXsWgfzjp%2B0n6MTYDbdHRriMbs%2FSh7wQyNfQ04lboD45x8nfd7MPgcMBhzF34tPQRpYGbthFXUmWnBEBixim90k62TJikTRaiW6PJLPDTwBLSYu4RpNwn%2B8DhpfWI1CfA%2BzWrZnHP5%2BzefKBrTh0zXKHkmuzliH39q3rwfXHT%2FUN3Nu1gWuZ9Wn05u0pyuGRuJWn14KAMTT4QTpzcPp0q6k3PF0dS8BvtMDAcsjIIiIQGKXQLYPAt8FgTU2uvZ8EQDruB3sL%2FEV7krVDmZIWNNupYoPkxTdQ3NGKoYYgS4mKQ4q76sKS0JxHADfqZupKbq4gq9wuaT6%2FwCVeR0IAAAAAQAAAAEZmiehT9dfDzz1AAkIAAAAAADJQhegAAAAAMnoSqH7DP2oCo0IjQABAAkAAgAAAAAAAHgBY2BkYODo%2FbuCgYGr9zfPv0quXqAIKrgJAJZXBsIAeAFtkQOsGEEQhv%2Fbnd272rZtG0Ft27ZtW1G9dYMiamrbZlgrqN17M89K8uVfTna%2FoRs4AwCUGVBCU0zQl7DAlEIZWoPOfhXUs0BbVQAL1CG0ZepQd9STPdUW9dQ61FGN%2BU5LpOW1pswUpmU0hZj%2BTGOmWnQ2lPNyV2rEoO%2FA%2BmUw0CwATG8cNjkwyXzEYZrG9Of5NUyy%2BXBY7Q4Hm9a8tgCH%2FWU4bOcwPfmsjc7GvDcYPWk7StjU2G8qAf5xwHQE6D%2BzHRXUbqzi96bmrEQNEeim4V965jWnB%2Bho0sNRHnTn7E5H0V3nQAlaAGsawqkxWKfGhDPoO2Ts%2FGdwsk5fIecd011vh9O%2FOaegHO9toBWAfYLM5JBSxvoNquliyEeDvUucbeXvMd55vIqRtTGMJTnzAkP5bdnsXvTX6VGOPkbfYe%2ByRgh%2F6xHoLms6QDmmlvyFPThTB2PEtbczfMbr3XUu1JD7fmqUjaYre68jzpPD3wJIH6QH0RyQ5L6Ui%2FGeGFqDOZLiPj7iXnpkDsKJ5%2BTwO3LmEe8JYecb2fcazoXMC%2FEd4z0J7EFS3MdH3EuPJJX07gom%2Bff4%2FDMcpS1ee85bBLQNGO84cgiqPerpVcghUBEeK%2FS1jzBBfUZbwUv5X%2F7bkOlslqCEwJ5TBw4lBFsBJdRuHA4vYk%2Fown8RLYvLrQAAeAEc0jWMJFcQxvFnto%2F5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56%2F%2FCz%2BVaqqrat5rY8x7xnzxl3nvo%2B27jFnz8c%2FmI9Nmh2XBdMsilrBitsnD9rI8aiN5DI%2FjSftC9mIf9pMfIB4kHiI%2BhWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW%2FgLbzNbnfwLt7DJ%2Fp0TX4%2BUucji1hCnY%2FU%2BcijVB7D46jzkb3Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV%2FEaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK%2FUVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN%2B59b410iF0sUFO0l2UJtY%2F8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC%2FLzdhmV2XBvpBF25IlLJOvEFfRI%2BNjgCFGGGNK5Rs6Z7Ij%2F45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j%2F%2B58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go%2Flfr05F%2BUa7CCzGx10sYA9tiWLxCWs2BfyN%2BIa1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt%2FQOZPfmY3%2F%2FSs3Y5tNpTpL9ZQeGR8DDDHCGN%2FwbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h%2B1FeZTKY3gcT2KvTWUf9pMZIB4kHiI%2BxcQzxGfpfA7P4wW8yG4eT%2FkYYIgRxvgb9TWsYwObmOAITlI%2Fxf7TOIOzOIfzuEDlIi7hMq7gFbyK1%2FA63sBbeJtvdwfv4j28zyaP8QmVL%2FimL%2FENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI%2FhcTzJp73Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe%2FgY%2F%2Begvq0YCAEoCNa1n%2BKVyTUl3Q0uIhoe%2B3DnRfV7nXGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOM8XZouTZemS1OAKcAUYAowBZgCTAHm3x31O7p3vNf5c1iXeBkEAQDFcbsJX0IqFBwK7tyEgkPC3R0K7hrXzsIhePPK%2F7c77jPM1yxSPua0WmuDzNcuNmuLtmq7sbyfsUu7De%2Fxu9fvvvDNfN3ioN9j5pq0ximd1hmd1TmlX7iky7qiq7qmG3pgXYd6pMd6oqd6pud6oZd6pdd6p%2Ff6oI%2F6pC%2FKSxvf9F0%2F1LFl1naRcwwzrAu7AHNarbW6oEu6rCu6qmu6ob9Y7xu%2BkbfHH1ZopCk25RVrhXKn4LCO6KiOGfvpd%2BR3is15xXmVWKGRptgaysQKpUwc1hEdVcpEysTI7xTbKHMcKzTSFDtCmVihkab4z0FdI0QQBAEUbRz6XLh3Lc7VcI%2FWN54IuxXFS97oH58%2BMBoclE1usbHHW77wlW985wcHHHLEMSecsUuPXMNRqfzib3pcllj5xd%2B0lSVW5nNIL3nF6389h%2BY5NG3Thja0oQ1taEMb2tCGNrQn%2BQwjrcwxM93gJre4Y89mvsdb3vGeD3zkE5%2F5wle%2B8Z0fHHDIEceccMaOX67wNz3747gObCQAQhCKdjlRzBVD5be7rwAmfOMQsUvPLj279OzSYBks49Ibl97In%2FHCuNDGO%2BNOW6qlWqqlWqqlWqqlWqqYUkwpphTzifnEfII92IM92IM92IM92IM92IM92I%2FD4%2FA4PA6Pw%2BPwODwOj8M%2Ff7kaaDXQyt7K3mqglcCVwNVAq4FWA60GWglZCVkJWQlZCVkJWQlZDbQyqhpoNdAPh3NAwCAAwwDM%2B7b2sg8kCjIO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO47AO67AO67AO67AO67AO67AO67AO67AO67AO67AO67AO63AO53AO53AO53AO53AO53AO53AO53AO53AO53AO53AO5xCHOMQhDnGIQxziEIc4xCEOcYhDHOIQhzjEIQ5xiEMd6lCHOtShDnWoQx3qUIc61KEOdahDHepQhzrUoQ6%2Fh%2BP6RpIjiKEoyOPvCARUoK9LctP5ZqXTop7q%2F6H%2F0H%2B4P9yfPz82bdm2Y9ee%2FT355bS3%2FdivDW9reFtDb4beDL0ZejP0ZujN0JuhN0Nvht4MvRl6M%2FRm6M3w1of3PVnJSlaykpWsZCUrWclKVrKSlaxkJStZySpWsYpVrGIVq1jFKlaxilWsYhWrWMUqVrGa1axmNatZzWpWs5rVrGY1q1nNalazmtWsYQ1rWMMa1rCGNaxhDWtYwxrWsIY1rGENa1nLWtaylrWsZS1rWcta1rKWtaxlLWtZyzrWsY51rGMd61jHOtaxjnWsYx3rWMc61rEeTf1o6kdTP%2F84rpMqCKAYhmH8Cfy2JjuLCPiYPDH1Y%2BrH1I%2BpH1M%2Fpn5M%2FZh6FEZhFEZhFEZhFEZhFEZhFFZhFVZhFVZhFVZhFVZhFVbhFE7hFE7hFE7hFE7hFE7hFCKgCChPHQFlc7I52ZxsTgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQti5bl63L1mXrsnXZuggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCyt5GQBFQBPTlwD7OEIaBKAxSOrmJVZa2TsJcwJ6r0%2F%2B9sBOGnTDshOF%2BDndyXG7k7vfh9%2Bn35fft978Thp2wKuqqqKtarmq58cYbb7zzzjvvfPDBBx988sknn3zxxRdfPHnyVPip8FPhp8JPhZ8KP78czLdxBDAMAMFc%2FbdAk4AERoMS5CpQOW82uWyPHexkJzvZyU52spOd7GQnu9jFLnaxi13sYhe72MVudrOb3exmN7vZzW52s8EGG2ywwQYbbLDBBnvZy172spe97GUve9nLJptssskmm2yyySabbLHFFltsscUWW2yxxX6%2B7P%2BrH%2Fqtf6%2B2Z3u2Z3u2Z3u2Z3u2Z3s%2BO66jKoYBGASA%2FiUFeLO2tqfgvhIgVkOshvj%2F8f%2FjF8VqiL8dqyG%2Bd4klllhiiSWWWGKJJY444ogjjjjiiCOO%2BPua0gPv7paRAHgBLcEDlNxQAADArI3Ydv7Vtm3btm3btm3btm3bD7VvBoIgLXVVqCf0ztXT9dzd3j3cvcX90CN5Snmae%2Fp45np2e356gbeH94HP8Q3x3feH%2FX38NwJwoHigQ2Ba4GBQCK4NfgxVDE0OnQr7w1nCI8P7wi8jdqR4ZGzkRDQSLRmdH%2F0UqxTrEVsbux%2FPHe8b3xh%2FlgglzESJRJfE6MS6ZChZJzkj%2BRouCA9GJKQuMhI5hsZRHR2A7kZ%2FYZWxldhtPDPeFd%2BIPybyE0OIy2SIrEy2IneSX8mvFKB6UpfodPQYeiOTjmnK3GOzsCPYpexaLjdXiRvBHeJ%2B8BX5Lvxe%2FqOACmWEnsJ60SsyYjqxiLhE3CoeE6%2BLL8RvUlRqJXWThkszpJXSbjkq83JaOZ9cXm4gd5IXKZACK4qSSSmiVFWmq0lVUtOr%2BdXyagO1oxbRSM3UsmnFtOpaC62nNkqbo7M60HPppfXaemu9j77X4IwUI49RxqhrtDWOGzeM92Y985lFWWWtcdZia4d10%2FpiU3YZu6%2B91j7rME5xp5szGVAgDcgBioDhYDpYDjaDE%2BAmeAW%2Bp8R%2FA5ajfCcAAAABAAAA3QCKABYAWAAFAAIAEAAvAFwAAAEAAQsAAwABeAF9jgNuRAEYhL%2FaDGoc4DluVNtug5pr8xh7jj3jTpK18pszwBDP9NHTP0IPs1DOexlmtpz3sc9iOe9nmddyPsA8%2BXI%2BqI1COZ%2FkliIXhPkiyDo3vCnG2CaEn0%2B2lH%2BgmfIvotowZa3769ULZST4K%2BcujqTb%2Fj36S4w%2FQmgDF0tWvalemNWLX%2BKSMBvYkhQSLG2FZR%2BafmERIsqPpn7%2ByvxjfMlsTjlihz3OuZE38bTtlAAa%2FTAFAHgBbMEDjJYBAADQ9%2F3nu2zbtm3b5p9t17JdQ7Zt21zmvGXXvJrZe0LA37Cw%2F3lDEBISIVKUaDFixYmXIJHEkkgqmeRSSCmV1NJIK530Msgok8yyyCqb7HLIKZfc8sgrn%2FwKKKiwIooqprgSSiqltDLKKqe8CiqqpLIqqqqmuhpqqqW2Ouqqp74GGmqksSaaaqa5FlpqpbU22mqnvQ466qSzLrrqprs9NpthprNWeWeWReZba6ctQYR5QaTplvvhp4VWm%2BOyt75bZ5fffvljk71uum6fHnpaopfbervhlvfCHnngof36%2BGappx57oq%2BPPpurv34GGGSgwTYYYpihhhthlJFGG%2BODscYbZ4JJJjphoykmm2qaT7445ZkDDnrujRcOOeyY46444qirZtvtnPPOBFG%2BBtFBTBAbxAXxQYJC7rvjrnv%2FxpJXmpPDXpqXaWDg6MKZX5ZaVJycX5TK4lpalA8SdnMyMITSRjxp%2BaVFxaUFqUWZ%2BUVQQWMobcKUlgYAHQ14sAAAeAFFSzVCLEEQ7fpjH113V1ybGPd1KRyiibEhxt1vsj3ZngE9AIfgBmMR5fVk8qElsRjHOHAYW%2BQwyumxct4bKxXkWDEvx7JjdszQNAZcekzi9Zho8oV8NCbnIT%2FfEXNRJwqmlaemnQMbN8E1OE7Mzb%2FP%2F8xzKZrEMA2hl3rQATa0Uxs2bN%2B2f8M2AEpwj5yQBvklvJ3AqRcEaMKrWq%2F19eWakl7NsZbyJoNblqlZc7KywcRbRnBjc00FeF6%2Fenoi05EcG62tsXhkPcdk87BHVC%2BZXleUPrOsUHaUI2tb4y%2F8OwbsTEAJAA%3D%3D%29%20format%28%22woff%22%29%7D%2A%7Bbox%2Dsizing%3Aborder%2Dbox%7Dbody%7Bpadding%3A0%3Bmargin%3A0%3Bfont%2Dfamily%3A%22Open%20Sans%22%2C%22Helvetica%20Neue%22%2CHelvetica%2CArial%2Csans%2Dserif%3Bfont%2Dsize%3A16px%3Bline%2Dheight%3A1%2E5%3Bcolor%3A%23606c71%7Da%7Bcolor%3A%231e6bb8%3Btext%2Ddecoration%3Anone%7Da%3Ahover%7Btext%2Ddecoration%3Aunderline%7D%2Epage%2Dheader%7Bcolor%3A%23fff%3Btext%2Dalign%3Acenter%3Bbackground%2Dcolor%3A%23159957%3Bbackground%2Dimage%3Alinear%2Dgradient%28120deg%2C%23155799%2C%23159957%29%3Bpadding%3A1%2E5rem%202rem%7D%2Eproject%2Dname%7Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A%2E1rem%3Bfont%2Dsize%3A2rem%7D%2Eproject%2Dtagline%7Bmargin%2Dbottom%3A2rem%3Bfont%2Dweight%3A400%3Bopacity%3A%2E7%3Bfont%2Dsize%3A1%2E5rem%7D%2Eproject%2Dauthor%2C%2Eproject%2Ddate%7Bfont%2Dweight%3A400%3Bopacity%3A%2E7%3Bfont%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<body>
<section class="page-header">
<h1 class="title toc-ignore project-name">Analysis of the Proposals to reduce average level of in-process inventory</h1>
<h4 class="author project-author">Afsar Ali</h4>
</section>
<div id="TOC" class="toc">
<ul>
<li><a href="#executive-summary">Executive Summary</a><ul>
<li><a href="#current-status-report">Current Status Report</a></li>
<li><a href="#proposed-decreasing-print-time">Proposed: Decreasing print time</a></li>
<li><a href="#proposed-increasing-print-time">Proposed: Increasing print time</a></li>
<li><a href="#proposed-experienced-inspector">Proposed: Experienced inspector</a></li>
</ul></li>
<li><a href="#recommended-proposal-2-inspector-8-presses-at-increased-rate">Recommended Proposal: 2 Inspector, 8 Presses at increased rate</a></li>
<li><a href="#relevant-final-thoughts">Relevant Final Thoughts</a></li>
</ul>
</div>
<section class="main-content">
<div id="to-seymore-butts" class="section level5">
<h5><strong>To:</strong> Seymore Butts</h5>
</div>
<div id="from-afsar-ali" class="section level5">
<h5><strong>From:</strong> Afsar Ali</h5>
</div>
<div id="date-september-21-2018" class="section level5">
<h5><strong>Date:</strong> September 21, 2018</h5>
</div>
<div id="re-analysis-of-the-proposals-to-reduce-average-level-of-in-process-inventory" class="section level5">
<h5><strong>RE:</strong> Analysis of the Proposals to reduce average level of in-process inventory</h5>
<hr />
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Title: Artie's Dream</span>
<span class="co"># Purpose: Final Project </span>
<span class="co"># Date: May 31, 2018</span>
<span class="co"># Author: Afsar Ali</span>
<span class="co"># Clear packages </span>
<span class="cf">if</span>(<span class="kw">is.null</span>(<span class="kw">sessionInfo</span>()<span class="op">$</span>otherPkgs) <span class="op">==</span><span class="st"> </span><span class="ot">FALSE</span>)<span class="kw">lapply</span>(
<span class="kw">paste</span>(<span class="st">"package:"</span>, <span class="kw">names</span>(<span class="kw">sessionInfo</span>()<span class="op">$</span>otherPkgs), <span class="dt">sep=</span><span class="st">""</span>),
detach, <span class="dt">character.only =</span> <span class="ot">TRUE</span>, <span class="dt">unload =</span> <span class="ot">TRUE</span>)
<span class="co"># Clear environment</span>
<span class="kw">rm</span>(<span class="dt">list =</span> <span class="kw">ls</span>(<span class="dt">all =</span> <span class="ot">TRUE</span>))
<span class="co"># Load packages</span>
<span class="kw">library</span>(tidyverse)
<span class="kw">library</span>(queueing)
<span class="kw">library</span>(kableExtra)
<span class="kw">library</span>(formattable)</code></pre></div>
</div>
<div id="executive-summary" class="section level2">
<h2>Executive Summary</h2>
<p>How can we reduce the average number of poster sheets waiting to complete inspection? I was tasked with analyzing and producing solutions to reduce the backlog by taking into account the cost associated with each task and cost of in-process inventory. According to my findings, we should only use 8 printing press, increase the print time by 15 mins and hire an additional inspector. This will reduce our <strong>Total cost by $50.63 per hour</strong> from current the total cost of $203.14 per hour to $152.51 per hour and optimizes our overall operation. With the task of analyzing the two initial proposals we discussed, I was able to create a third solution that maximizes the poster sheet flow while minimizing our cost.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#Current Status:</span>
<span class="co"># Evaluating the status quo print reproductions of the paintings and photographs</span>
lam_f <-<span class="st"> </span><span class="dv">7</span> <span class="co"># The poster sheets arrive randomly to the group of presses at a mean rate of 7 per hour </span>
mu_f <-<span class="st"> </span><span class="dv">1</span> <span class="co">#The time required to make a print has an exponential distribution with a mean of 1 hour</span>
p <-<span class="st"> </span><span class="dv">8</span> <span class="co">#The cost of in-process inventory is estimated to be $8 per hour for each poster sheet at the presses or each print at the inspection station</span>
cost <-<span class="st"> </span><span class="dv">7</span> <span class="co">#the cost of the power for running each press $7.00</span>
s <-<span class="st"> </span><span class="kw">ceiling</span>(lam_f<span class="op">/</span>mu_f)<span class="op">+</span><span class="dv">1</span> <span class="co"># lam < s*mu in steady state, so rearrange to find min s </span>
n <-<span class="st"> </span><span class="dv">12</span> <span class="co"># Max number of servers</span>
p4_p <-<span class="st"> </span><span class="kw">vector</span>() <span class="co"># Create matrix to hold data</span>
<span class="co"># Loop to find low cost</span>
<span class="cf">for</span> (i <span class="cf">in</span> s<span class="op">:</span>n){
q1_fi <-<span class="st"> </span><span class="kw">NewInput.MMC</span>(lam_f, mu_f, i) <span class="co"># Set and check the inputs of the model</span>
q1_f <-<span class="st"> </span><span class="kw">QueueingModel.i_MMC</span>(q1_fi) <span class="co"># Solve the queueing model</span>
L <-<span class="st"> </span>q1_f<span class="op">$</span>L <span class="co"># Mean number of print in queue system</span>
Lq <-<span class="st"> </span>q1_f<span class="op">$</span>Lq <span class="co"># Mean number of print in queue</span>
W <-<span class="st"> </span>q1_f<span class="op">$</span>W<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue system</span>
Wq <-<span class="st"> </span>q1_f<span class="op">$</span>Wq<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue waiting for service</span>
CL <-<span class="st"> </span>Lq<span class="op">*</span>p <span class="co"># Cost of in-process inventory in queue</span>
CL2 <-<span class="st"> </span>L<span class="op">*</span>p <span class="co"># Cost of in-process inventory </span>
CS <-<span class="st"> </span>i<span class="op">*</span>cost <span class="co"># Cost of servers in queue system</span>
p4_p <-<span class="st"> </span><span class="kw">rbind</span>(p4_p, <span class="kw">round</span>(<span class="kw">c</span>(i, L, Lq, W, Wq, CS, CL, CL2, CL <span class="op">+</span><span class="st"> </span>CS, CL2 <span class="op">+</span><span class="st"> </span>CS), <span class="dv">2</span>)) <span class="co"># Organize in table</span>
}
<span class="kw">colnames</span>(p4_p) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Servers"</span>, <span class="st">"L"</span>, <span class="st">"Lq"</span>, <span class="st">"W"</span>, <span class="st">"Wq"</span>, <span class="st">"CS"</span>, <span class="st">"CLq"</span>, <span class="st">"CL"</span>, <span class="st">"TCq"</span>, <span class="st">"TC"</span>)
<span class="kw">as.table</span>(p4_p)</code></pre></div>
<pre><code>## Servers L Lq W Wq CS CLq CL TCq TC
## A 8.00 11.45 4.45 98.12 38.12 56.00 35.58 91.58 91.58 147.58
## B 9.00 8.35 1.35 71.55 11.55 63.00 10.78 66.78 73.78 129.78
## C 10.00 7.52 0.52 64.43 4.43 70.00 4.14 60.14 74.14 130.14
## D 11.00 7.21 0.21 61.82 1.82 77.00 1.70 57.70 78.70 134.70
## E 12.00 7.09 0.09 60.75 0.75 84.00 0.70 56.70 84.70 140.70</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Evaluating the status quo inspection station</span>
lam_f <-<span class="st"> </span><span class="dv">7</span> <span class="co">#the prints arrive randomly at an inspection station at the same mean rate as the sheets arrived at the presses (7 per hour)</span>
mu_f <-<span class="st"> </span><span class="dv">8</span> <span class="co">#Each inspection takes him 7.5 minutes, so he can inspect 8 prints per hour</span>
p <-<span class="st"> </span><span class="dv">8</span> <span class="co">#The cost of in-process inventory is estimated to be $8 per hour for each poster sheet at the presses or each print at the inspection station</span>
cost <-<span class="st"> </span><span class="dv">17</span> <span class="co"># the current inspector is in a lower job classification where the compensation is $17 per hour</span>
s <-<span class="st"> </span><span class="kw">ceiling</span>(lam_f<span class="op">/</span>mu_f) <span class="co"># lam < s*mu in steady state, so rearrange to find min s </span>
n <-<span class="st"> </span><span class="dv">3</span> <span class="co"># Max number of servers</span>
p4_i <-<span class="st"> </span><span class="kw">vector</span>() <span class="co"># Create matrix to hold data</span>
<span class="co"># Loop to find low cost</span>
<span class="cf">for</span> (i <span class="cf">in</span> s<span class="op">:</span>n){
q1_fi <-<span class="st"> </span><span class="kw">NewInput.MMC</span>(lam_f, mu_f, i) <span class="co"># Set and check the inputs of the model</span>
q1_f <-<span class="st"> </span><span class="kw">QueueingModel.i_MMC</span>(q1_fi) <span class="co"># Solve the queueing model</span>
<span class="co">#L is the all inventory (work in process) of the queue system while Lq is the portion of L that is idle (queue waiting)</span>
L <-<span class="st"> </span>q1_f<span class="op">$</span>L <span class="co"># Mean number of print in queue system </span>
Lq <-<span class="st"> </span>q1_f<span class="op">$</span>Lq <span class="co"># Mean number of print in queue</span>
W <-<span class="st"> </span>q1_f<span class="op">$</span>W<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue system</span>
Wq <-<span class="st"> </span>q1_f<span class="op">$</span>Wq<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue waiting for service</span>
CL <-<span class="st"> </span>Lq<span class="op">*</span>p <span class="co"># Cost of in-process inventory in queue</span>
CL2 <-<span class="st"> </span>L<span class="op">*</span>p <span class="co"># Cost of in-process inventory </span>
CS <-<span class="st"> </span>i<span class="op">*</span>cost <span class="co"># Cost of servers in queue system</span>
p4_i <-<span class="st"> </span><span class="kw">rbind</span>(p4_i, <span class="kw">round</span>(<span class="kw">c</span>(i, L, Lq, W, Wq, CS, CL, CL2, CL <span class="op">+</span><span class="st"> </span>CS, CL2 <span class="op">+</span><span class="st"> </span>CS), <span class="dv">2</span>)) <span class="co"># Organize in table</span>
}
<span class="kw">colnames</span>(p4_i) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Servers"</span>, <span class="st">"L"</span>, <span class="st">"Lq"</span>, <span class="st">"W"</span>, <span class="st">"Wq"</span>, <span class="st">"CS"</span>, <span class="st">"CLq"</span>, <span class="st">"CL"</span>, <span class="st">"TCq"</span>, <span class="st">"TC"</span>)
<span class="kw">as.table</span>(p4_i)</code></pre></div>
<pre><code>## Servers L Lq W Wq CS CLq CL TCq TC
## A 1.00 7.00 6.12 60.00 52.50 17.00 49.00 56.00 66.00 73.00
## B 2.00 1.08 0.21 9.28 1.78 34.00 1.66 8.66 35.66 42.66
## C 3.00 0.90 0.03 7.73 0.23 51.00 0.21 7.21 51.21 58.21</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rbind</span>(p4_i[<span class="dv">1</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">2</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>], <span class="co">#using 9 presses and 1 inspector</span>
p4_i[<span class="dv">2</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">2</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]) <span class="co">#using 9 presses and 2 inspector</span></code></pre></div>
<pre><code>## TCq TC
## [1,] 139.78 202.78
## [2,] 109.44 172.44</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rbind</span>(p4_i[<span class="dv">1</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">3</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>], <span class="co">#using 10 presses and 1 inspector</span>
p4_i[<span class="dv">2</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">3</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]) <span class="co">#using 10 presses and 2 inspector</span></code></pre></div>
<pre><code>## TCq TC
## [1,] 140.14 203.14
## [2,] 109.80 172.80</code></pre>
<div id="current-status-report" class="section level3">
<h3>Current Status Report</h3>
<p>Table 1 breaks down our status quo. Our current <strong>total cost is $203.14 per hour</strong> (highlighted green). There is an average of 7 to 8 posters in Printing Press and 7 posters in the Inspection Station, which we can call work-in-process inventory (WIP). The inventory waiting in the queue (WIQ) for service is an average of 0 to 1 posters at Printing Press and 6 posters at the Inspection Station. The wait time of the inventory in the queue for service is in average 4 minutes 25 seconds at Printing Press and 52 minutes 30 seconds in the Inspection Station. We are already aware that there is a backlog at the Inspection Station. Excluding the Cost of servers, the result shows that the main impact on in-process inventory cost comes from the Inspection Station $49 per hour for inventory waiting to be serviced (highlighted red).</p>
<p><em>Table 1: Currently the Total Cost is $203.14 per hour </em></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">now <-<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">cbind</span>(p4_p[<span class="dv">3</span>,],<span class="co">#using 10 presses and 1 inspector</span>
p4_i[<span class="dv">1</span>,])) <span class="co">#using 10 presses and 1 inspector</span>
<span class="co">#L is the all inventory (work in process) of the queue system while Lq (ork To Process)is the portion of L that is idle (queue waiting)</span>
<span class="kw">rownames</span>(now) <-<span class="st"> </span><span class="kw">c</span>( <span class="st">"Servers"</span>,
<span class="st">"Average number of posters (WIP)"</span>,
<span class="st">"Average number of posters in queue (WIQ)"</span>,
<span class="st">"Average wait time (WIP) (in Minutes)"</span>,
<span class="st">"Average wait time in queue waiting for service (WIQ) (in Minutes)"</span>,
<span class="st">"Cost of servers"</span>,
<span class="st">"Cost of in-process inventory waiting for service (WIQ) ($ Per Hour)"</span>,
<span class="st">"Cost of all in-process inventory (WIP) ($ Per Hour)"</span>,
<span class="st">"Cost of in-process inventory in queue including server cost (WIQ) ($ Per Hour)"</span>,
<span class="st">"Total Cost ($ Per Hour)"</span>)
<span class="kw">colnames</span>(now)<-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Printing Press"</span>,<span class="st">"Inspection Station"</span>)
now<span class="op">$</span>Total =<span class="st"> </span><span class="kw">rowSums</span>(now)
<span class="kw">options</span>(<span class="dt">knitr.kable.NA =</span> <span class="st">''</span>)
now <span class="op">%>%</span>
<span class="st"> </span><span class="kw">kable</span>(<span class="st">"html"</span>, <span class="dt">row.names =</span> <span class="ot">TRUE</span>, <span class="dt">escape =</span> F) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">kable_styling</span>(<span class="dt">bootstrap_options =</span> <span class="st">"striped"</span>, <span class="dt">full_width =</span> F, <span class="dt">position =</span> <span class="st">"left"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">row_spec</span>(<span class="kw">nrow</span>(now), <span class="dt">bold =</span> T, <span class="dt">color =</span> <span class="st">"white"</span>, <span class="dt">background =</span> <span class="st">"green"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">row_spec</span>(<span class="kw">nrow</span>(now)<span class="op">-</span><span class="dv">3</span>, <span class="dt">bold =</span> T, <span class="dt">color =</span> <span class="st">"white"</span>, <span class="dt">background =</span> <span class="st">"darkred"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">column_spec</span>(<span class="dv">4</span>, <span class="dt">bold =</span> T) </code></pre></div>
<table class="table table-striped" style="width: auto !important; ">
<thead>
<tr>
<th style="text-align:left;">
</th>
<th style="text-align:right;">
Printing Press
</th>
<th style="text-align:right;">
Inspection Station
</th>
<th style="text-align:right;">
Total
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Servers
</td>
<td style="text-align:right;">
10.00
</td>
<td style="text-align:right;">
1.00
</td>
<td style="text-align:right;font-weight: bold;">
11.00
</td>
</tr>
<tr>
<td style="text-align:left;">
Average number of posters (WIP)
</td>
<td style="text-align:right;">
7.52
</td>
<td style="text-align:right;">
7.00
</td>
<td style="text-align:right;font-weight: bold;">
14.52
</td>
</tr>
<tr>
<td style="text-align:left;">
Average number of posters in queue (WIQ)
</td>
<td style="text-align:right;">
0.52
</td>
<td style="text-align:right;">
6.12
</td>
<td style="text-align:right;font-weight: bold;">
6.64
</td>
</tr>
<tr>
<td style="text-align:left;">
Average wait time (WIP) (in Minutes)
</td>
<td style="text-align:right;">
64.43
</td>
<td style="text-align:right;">
60.00
</td>
<td style="text-align:right;font-weight: bold;">
124.43
</td>
</tr>
<tr>
<td style="text-align:left;">
Average wait time in queue waiting for service (WIQ) (in Minutes)
</td>
<td style="text-align:right;">
4.43
</td>
<td style="text-align:right;">
52.50
</td>
<td style="text-align:right;font-weight: bold;">
56.93
</td>
</tr>
<tr>
<td style="text-align:left;">
Cost of servers
</td>
<td style="text-align:right;">
70.00
</td>
<td style="text-align:right;">
17.00
</td>
<td style="text-align:right;font-weight: bold;">
87.00
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;color: white;background-color: darkred;">
Cost of in-process inventory waiting for service (WIQ) ($ Per Hour)
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;">
4.14
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;">
49.00
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;font-weight: bold;">
53.14
</td>
</tr>
<tr>
<td style="text-align:left;">
Cost of all in-process inventory (WIP) ($ Per Hour)
</td>
<td style="text-align:right;">
60.14
</td>
<td style="text-align:right;">
56.00
</td>
<td style="text-align:right;font-weight: bold;">
116.14
</td>
</tr>
<tr>
<td style="text-align:left;">
Cost of in-process inventory in queue including server cost (WIQ) ($ Per Hour)
</td>
<td style="text-align:right;">
74.14
</td>
<td style="text-align:right;">
66.00
</td>
<td style="text-align:right;font-weight: bold;">
140.14
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;color: white;background-color: green;">
Total Cost ($ Per Hour)
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;">
130.14
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;">
73.00
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;font-weight: bold;">
203.14
</td>
</tr>
</tbody>
</table>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">##Proposal 4: Take slightly longer to make the prints (which would increase their average time to make a print to 1.2 hours), so that the inspector can keep up with his output better. This also would reduce the cost of the power for running each press from $7.00 to $6.50 per hour. (By contrast, decreasing the time would increase this cost to $7.50 per hour while decreasing the average time to make a print to 0.8 hour.):
<span class="co"># Evaluating the status quo print reproductions of the paintings and photographs</span>
lam_f <-<span class="st"> </span><span class="dv">7</span> <span class="co"># The poster sheets arrive randomly to the group of presses at a mean rate of 7 per hour </span>
mu_f <-<span class="st"> </span><span class="dv">1</span><span class="op">/</span><span class="fl">1.2</span> <span class="co">#The time required to make a print has an exponential distribution with a mean of 1 hour; increase their average time to make a print to 1.2 hours</span>
p <-<span class="st"> </span><span class="dv">8</span> <span class="co">#The cost of in-process inventory is estimated to be $8 per hour for each poster sheet at the presses or each print at the inspection station</span>
cost <-<span class="st"> </span><span class="fl">6.5</span> <span class="co">#the cost of the power for running each press $7.00 to $6.50 per hour</span>
s <-<span class="st"> </span><span class="kw">ceiling</span>(lam_f<span class="op">/</span>mu_f) <span class="co"># lam < s*mu in steady state, so rearrange to find min s </span>
n <-<span class="st"> </span><span class="dv">12</span> <span class="co"># Max number of servers</span>
p4_p <-<span class="st"> </span><span class="kw">vector</span>() <span class="co"># Create matrix to hold data</span>
<span class="co"># Loop to find low cost</span>
<span class="cf">for</span> (i <span class="cf">in</span> s<span class="op">:</span>n){
q1_fi <-<span class="st"> </span><span class="kw">NewInput.MMC</span>(lam_f, mu_f, i) <span class="co"># Set and check the inputs of the model</span>
q1_f <-<span class="st"> </span><span class="kw">QueueingModel.i_MMC</span>(q1_fi) <span class="co"># Solve the queueing model</span>
L <-<span class="st"> </span>q1_f<span class="op">$</span>L <span class="co"># Mean number of print in queue system</span>
Lq <-<span class="st"> </span>q1_f<span class="op">$</span>Lq <span class="co"># Mean number of print in queue</span>
W <-<span class="st"> </span>q1_f<span class="op">$</span>W<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue system</span>
Wq <-<span class="st"> </span>q1_f<span class="op">$</span>Wq<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue waiting for service</span>
CL <-<span class="st"> </span>Lq<span class="op">*</span>p <span class="co"># Cost of in-process inventory in queue</span>
CL2 <-<span class="st"> </span>L<span class="op">*</span>p <span class="co"># Cost of in-process inventory </span>
CS <-<span class="st"> </span>i<span class="op">*</span>cost <span class="co"># Cost of servers in queue system</span>
p4_p <-<span class="st"> </span><span class="kw">rbind</span>(p4_p, <span class="kw">round</span>(<span class="kw">c</span>(i, L, Lq, W, Wq, CS, CL, CL2, CL <span class="op">+</span><span class="st"> </span>CS, CL2 <span class="op">+</span><span class="st"> </span>CS), <span class="dv">2</span>)) <span class="co"># Organize in table</span>
}
<span class="kw">colnames</span>(p4_p) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Servers"</span>, <span class="st">"L"</span>, <span class="st">"Lq"</span>, <span class="st">"W"</span>, <span class="st">"Wq"</span>, <span class="st">"CS"</span>, <span class="st">"CLq"</span>, <span class="st">"CL"</span>, <span class="st">"TCq"</span>, <span class="st">"TC"</span>)
<span class="kw">as.table</span>(p4_p)</code></pre></div>
<pre><code>## Servers L Lq W Wq CS CLq CL TCq TC
## A 9.00 19.36 10.96 165.94 93.94 58.50 87.68 154.88 146.18 213.38
## B 10.00 11.05 2.65 94.69 22.69 65.00 21.18 88.38 86.18 153.38
## C 11.00 9.41 1.01 80.62 8.62 71.50 8.05 75.25 79.55 146.75
## D 12.00 8.83 0.43 75.68 3.68 78.00 3.43 70.63 81.43 148.63</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Evaluating the status quo inspection station</span>
lam_f <-<span class="st"> </span><span class="dv">7</span> <span class="co">#the prints arrive randomly at an inspection station at the same mean rate as the sheets arrived at the presses (7 per hour)</span>
mu_f <-<span class="st"> </span><span class="dv">8</span> <span class="co">#Each inspection takes him 7.5 minutes, so he can inspect 8 prints per hour</span>
p <-<span class="st"> </span><span class="dv">8</span> <span class="co">#The cost of in-process inventory is estimated to be $8 per hour for each poster sheet at the presses or each print at the inspection station</span>
cost <-<span class="st"> </span><span class="dv">17</span> <span class="co"># the current inspector is in a lower job classification where the compensation is $17 per hour</span>
s <-<span class="st"> </span><span class="kw">ceiling</span>(lam_f<span class="op">/</span>mu_f) <span class="co"># lam < s*mu in steady state, so rearrange to find min s </span>
n <-<span class="st"> </span><span class="dv">3</span> <span class="co"># Max number of servers</span>
p4_i <-<span class="st"> </span><span class="kw">vector</span>() <span class="co"># Create matrix to hold data</span>
<span class="co"># Loop to find low cost</span>
<span class="cf">for</span> (i <span class="cf">in</span> s<span class="op">:</span>n){
q1_fi <-<span class="st"> </span><span class="kw">NewInput.MMC</span>(lam_f, mu_f, i) <span class="co"># Set and check the inputs of the model</span>
q1_f <-<span class="st"> </span><span class="kw">QueueingModel.i_MMC</span>(q1_fi) <span class="co"># Solve the queueing model</span>
<span class="co">#L is the all inventory (work in process) of the queue system while Lq is the portion of L that is idle (queue waiting)</span>
L <-<span class="st"> </span>q1_f<span class="op">$</span>L <span class="co"># Mean number of print in queue system </span>
Lq <-<span class="st"> </span>q1_f<span class="op">$</span>Lq <span class="co"># Mean number of print in queue</span>
W <-<span class="st"> </span>q1_f<span class="op">$</span>W<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue system</span>
Wq <-<span class="st"> </span>q1_f<span class="op">$</span>Wq<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue waiting for service</span>
CL <-<span class="st"> </span>Lq<span class="op">*</span>p <span class="co"># Cost of in-process inventory in queue</span>
CL2 <-<span class="st"> </span>L<span class="op">*</span>p <span class="co"># Cost of in-process inventory </span>
CS <-<span class="st"> </span>i<span class="op">*</span>cost <span class="co"># Cost of servers in queue system</span>
p4_i <-<span class="st"> </span><span class="kw">rbind</span>(p4_i, <span class="kw">round</span>(<span class="kw">c</span>(i, L, Lq, W, Wq, CS, CL, CL2, CL <span class="op">+</span><span class="st"> </span>CS, CL2 <span class="op">+</span><span class="st"> </span>CS), <span class="dv">2</span>)) <span class="co"># Organize in table</span>
}
<span class="kw">colnames</span>(p4_i) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Servers"</span>, <span class="st">"L"</span>, <span class="st">"Lq"</span>, <span class="st">"W"</span>, <span class="st">"Wq"</span>, <span class="st">"CS"</span>, <span class="st">"CLq"</span>, <span class="st">"CL"</span>, <span class="st">"TCq"</span>, <span class="st">"TC"</span>)
<span class="kw">as.table</span>(p4_i)</code></pre></div>
<pre><code>## Servers L Lq W Wq CS CLq CL TCq TC
## A 1.00 7.00 6.12 60.00 52.50 17.00 49.00 56.00 66.00 73.00
## B 2.00 1.08 0.21 9.28 1.78 34.00 1.66 8.66 35.66 42.66
## C 3.00 0.90 0.03 7.73 0.23 51.00 0.21 7.21 51.21 58.21</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rbind</span>(p4_i[<span class="dv">1</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">1</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>], <span class="co">#using 9 presses and 1 inspector</span>
p4_i[<span class="dv">2</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">1</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]) <span class="co">#using 9 presses and 2 inspector</span></code></pre></div>
<pre><code>## TCq TC
## [1,] 212.18 286.38
## [2,] 181.84 256.04</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rbind</span>(p4_i[<span class="dv">1</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">2</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>], <span class="co">#using 10 presses and 1 inspector</span>
p4_i[<span class="dv">2</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">2</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]) <span class="co">#using 10 presses and 2 inspector</span></code></pre></div>
<pre><code>## TCq TC
## [1,] 152.18 226.38
## [2,] 121.84 196.04</code></pre>
</div>
<div id="proposed-decreasing-print-time" class="section level3">
<h3>Proposed: Decreasing print time</h3>
<p>If we were to take slightly longer time to make the prints, so that the inspector can keep up with the output and reduce the cost of the power for running each press from $7.00 to $6.50 per hour, the <strong>total cost would increase by $23.24 per hour</strong>, from current total cost of $203.14 per hour to $226.38 per hour (highlighted green in table 2). This increase is due to the cost of in-process inventory, by decreasing the print time the average WIP inventory goes from 7-8 posters to about 11 posters in printing press. Although the cost of printing press gets reduced by $5, the result shows that cost of in-process inventory WIQ increases from $4.14 per hours to $21.18 per hour (highlighted red in table 2).</p>
<p><em>Table 2: Decreasing print time by 10 mins would increase Total Cost by $23.24 per hour </em></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">p4a <-<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">cbind</span>(p4_p[<span class="dv">2</span>,],<span class="co">#using 10 presses and 1 inspector</span>
p4_i[<span class="dv">1</span>,])) <span class="co">#using 10 presses and 1 inspector</span>
<span class="co">#L is the all inventory (work in process) of the queue system while Lq (ork To Process)is the portion of L that is idle (queue waiting)</span>
<span class="kw">rownames</span>(p4a) <-<span class="st"> </span><span class="kw">c</span>( <span class="st">"Servers"</span>,
<span class="st">"Average number of posters (WIP)"</span>,
<span class="st">"Average number of posters in queue (WIQ)"</span>,
<span class="st">"Average wait time (WIP) (in Minutes)"</span>,
<span class="st">"Average wait time in queue waiting for service (WIQ) (in Minutes)"</span>,
<span class="st">"Cost of servers"</span>,
<span class="st">"Cost of in-process inventory waiting for service (WIQ) ($ Per Hour)"</span>,
<span class="st">"Cost of all in-process inventory (WIP) ($ Per Hour)"</span>,
<span class="st">"Cost of in-process inventory in queue including server cost (WIQ) ($ Per Hour)"</span>,
<span class="st">"Total Cost ($ Per Hour)"</span>)
<span class="kw">colnames</span>(p4a)<-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Printing Press"</span>,<span class="st">"Inspection Station"</span>)
p4a<span class="op">$</span>Total =<span class="st"> </span><span class="kw">rowSums</span>(p4a)
now1 <-<span class="st"> </span><span class="kw">cbind</span>(now, p4a)
<span class="kw">options</span>(<span class="dt">knitr.kable.NA =</span> <span class="st">''</span>)
now1 <span class="op">%>%</span>
<span class="st"> </span><span class="kw">kable</span>(<span class="st">"html"</span>, <span class="dt">row.names =</span> <span class="ot">TRUE</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">kable_styling</span>(<span class="dt">bootstrap_options =</span> <span class="st">"striped"</span>, <span class="dt">full_width =</span> F, <span class="dt">position =</span> <span class="st">"left"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">row_spec</span>(<span class="kw">nrow</span>(now1), <span class="dt">bold =</span> T, <span class="dt">color =</span> <span class="st">"white"</span>, <span class="dt">background =</span> <span class="st">"green"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">row_spec</span>(<span class="kw">nrow</span>(now1)<span class="op">-</span><span class="dv">3</span>, <span class="dt">bold =</span> T, <span class="dt">color =</span> <span class="st">"white"</span>, <span class="dt">background =</span> <span class="st">"darkred"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">column_spec</span>(<span class="dv">4</span>, <span class="dt">bold =</span> T) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">column_spec</span>(<span class="dv">7</span>, <span class="dt">bold =</span> T) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">add_header_above</span>(<span class="kw">c</span>(<span class="st">" "</span>, <span class="st">"Current"</span> =<span class="st"> </span><span class="dv">3</span>, <span class="st">"Proposed: Decreasing print time"</span> =<span class="st"> </span><span class="dv">3</span>))</code></pre></div>
<table class="table table-striped" style="width: auto !important; ">
<thead>
<tr>
<th style="border-bottom:hidden" colspan="1">
</th>
<th style="border-bottom:hidden; padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px;">
Current
</div>
</th>
<th style="border-bottom:hidden; padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px;">
Proposed: Decreasing print time
</div>
</th>
</tr>
<tr>
<th style="text-align:left;">
</th>
<th style="text-align:right;">
Printing Press
</th>
<th style="text-align:right;">
Inspection Station
</th>
<th style="text-align:right;">
Total
</th>
<th style="text-align:right;">
Printing Press
</th>
<th style="text-align:right;">
Inspection Station
</th>
<th style="text-align:right;">
Total
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Servers
</td>
<td style="text-align:right;">
10.00
</td>
<td style="text-align:right;">
1.00
</td>
<td style="text-align:right;font-weight: bold;">
11.00
</td>
<td style="text-align:right;">
10.00
</td>
<td style="text-align:right;">
1.00
</td>
<td style="text-align:right;font-weight: bold;">
11.00
</td>
</tr>
<tr>
<td style="text-align:left;">
Average number of posters (WIP)
</td>
<td style="text-align:right;">
7.52
</td>
<td style="text-align:right;">
7.00
</td>
<td style="text-align:right;font-weight: bold;">
14.52
</td>
<td style="text-align:right;">
11.05
</td>
<td style="text-align:right;">
7.00
</td>
<td style="text-align:right;font-weight: bold;">
18.05
</td>
</tr>
<tr>
<td style="text-align:left;">
Average number of posters in queue (WIQ)
</td>
<td style="text-align:right;">
0.52
</td>
<td style="text-align:right;">
6.12
</td>
<td style="text-align:right;font-weight: bold;">
6.64
</td>
<td style="text-align:right;">
2.65
</td>
<td style="text-align:right;">
6.12
</td>
<td style="text-align:right;font-weight: bold;">
8.77
</td>
</tr>
<tr>
<td style="text-align:left;">
Average wait time (WIP) (in Minutes)
</td>
<td style="text-align:right;">
64.43
</td>
<td style="text-align:right;">
60.00
</td>
<td style="text-align:right;font-weight: bold;">
124.43
</td>
<td style="text-align:right;">
94.69
</td>
<td style="text-align:right;">
60.00
</td>
<td style="text-align:right;font-weight: bold;">
154.69
</td>
</tr>
<tr>
<td style="text-align:left;">
Average wait time in queue waiting for service (WIQ) (in Minutes)
</td>
<td style="text-align:right;">
4.43
</td>
<td style="text-align:right;">
52.50
</td>
<td style="text-align:right;font-weight: bold;">
56.93
</td>
<td style="text-align:right;">
22.69
</td>
<td style="text-align:right;">
52.50
</td>
<td style="text-align:right;font-weight: bold;">
75.19
</td>
</tr>
<tr>
<td style="text-align:left;">
Cost of servers
</td>
<td style="text-align:right;">
70.00
</td>
<td style="text-align:right;">
17.00
</td>
<td style="text-align:right;font-weight: bold;">
87.00
</td>
<td style="text-align:right;">
65.00
</td>
<td style="text-align:right;">
17.00
</td>
<td style="text-align:right;font-weight: bold;">
82.00
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;color: white;background-color: darkred;">
Cost of in-process inventory waiting for service (WIQ) ($ Per Hour)
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;">
4.14
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;">
49.00
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;font-weight: bold;">
53.14
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;">
21.18
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;">
49.00
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;font-weight: bold;">
70.18
</td>
</tr>
<tr>
<td style="text-align:left;">
Cost of all in-process inventory (WIP) ($ Per Hour)
</td>
<td style="text-align:right;">
60.14
</td>
<td style="text-align:right;">
56.00
</td>
<td style="text-align:right;font-weight: bold;">
116.14
</td>
<td style="text-align:right;">
88.38
</td>
<td style="text-align:right;">
56.00
</td>
<td style="text-align:right;font-weight: bold;">
144.38
</td>
</tr>
<tr>
<td style="text-align:left;">
Cost of in-process inventory in queue including server cost (WIQ) ($ Per Hour)
</td>
<td style="text-align:right;">
74.14
</td>
<td style="text-align:right;">
66.00
</td>
<td style="text-align:right;font-weight: bold;">
140.14
</td>
<td style="text-align:right;">
86.18
</td>
<td style="text-align:right;">
66.00
</td>
<td style="text-align:right;font-weight: bold;">
152.18
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;color: white;background-color: green;">
Total Cost ($ Per Hour)
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;">
130.14
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;">
73.00
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;font-weight: bold;">
203.14
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;">
153.38
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;">
73.00
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;font-weight: bold;">
226.38
</td>
</tr>
</tbody>
</table>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">##Proposal 4: Take slightly longer to make the prints (which would increase their average time to make a print to 1.2 hours), so that the inspector can keep up with his output better. This also would reduce the cost of the power for running each press from $7.00 to $6.50 per hour. (By contrast, decreasing the time would increase this cost to $7.50 per hour while decreasing the average time to make a print to 0.8 hour.):
<span class="co"># Evaluating the status quo print reproductions of the paintings and photographs</span>
lam_f <-<span class="st"> </span><span class="dv">7</span> <span class="co"># The poster sheets arrive randomly to the group of presses at a mean rate of 7 per hour </span>
mu_f <-<span class="st"> </span><span class="dv">1</span><span class="op">/</span>.<span class="dv">8</span> <span class="co">#The time required to make a print has an exponential distribution with a mean of 1 hour; decrease their average time to make a print to .8 hours</span>
p <-<span class="st"> </span><span class="dv">8</span> <span class="co">#The cost of in-process inventory is estimated to be $8 per hour for each poster sheet at the presses or each print at the inspection station</span>
cost <-<span class="st"> </span><span class="fl">7.5</span> <span class="co">#the cost of the power for running each press $7.00 to $7.50 per hour</span>
s <-<span class="st"> </span><span class="kw">ceiling</span>(lam_f<span class="op">/</span>mu_f) <span class="co"># lam < s*mu in steady state, so rearrange to find min s </span>
n <-<span class="st"> </span><span class="dv">12</span> <span class="co"># Max number of servers</span>
p4_p <-<span class="st"> </span><span class="kw">vector</span>() <span class="co"># Create matrix to hold data</span>
<span class="co"># Loop to find low cost</span>
<span class="cf">for</span> (i <span class="cf">in</span> s<span class="op">:</span>n){
q1_fi <-<span class="st"> </span><span class="kw">NewInput.MMC</span>(lam_f, mu_f, i) <span class="co"># Set and check the inputs of the model</span>
q1_f <-<span class="st"> </span><span class="kw">QueueingModel.i_MMC</span>(q1_fi) <span class="co"># Solve the queueing model</span>
L <-<span class="st"> </span>q1_f<span class="op">$</span>L <span class="co"># Mean number of print in queue system</span>
Lq <-<span class="st"> </span>q1_f<span class="op">$</span>Lq <span class="co"># Mean number of print in queue</span>
W <-<span class="st"> </span>q1_f<span class="op">$</span>W<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue system</span>
Wq <-<span class="st"> </span>q1_f<span class="op">$</span>Wq<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue waiting for service</span>
CL <-<span class="st"> </span>Lq<span class="op">*</span>p <span class="co"># Cost of in-process inventory in queue</span>
CL2 <-<span class="st"> </span>L<span class="op">*</span>p <span class="co"># Cost of in-process inventory </span>
CS <-<span class="st"> </span>i<span class="op">*</span>cost <span class="co"># Cost of servers in queue system</span>
p4_p <-<span class="st"> </span><span class="kw">rbind</span>(p4_p, <span class="kw">round</span>(<span class="kw">c</span>(i, L, Lq, W, Wq, CS, CL, CL2, CL <span class="op">+</span><span class="st"> </span>CS, CL2 <span class="op">+</span><span class="st"> </span>CS), <span class="dv">2</span>)) <span class="co"># Organize in table</span>
}
<span class="kw">colnames</span>(p4_p) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Servers"</span>, <span class="st">"L"</span>, <span class="st">"Lq"</span>, <span class="st">"W"</span>, <span class="st">"Wq"</span>, <span class="st">"CS"</span>, <span class="st">"CLq"</span>, <span class="st">"CL"</span>, <span class="st">"TCq"</span>, <span class="st">"TC"</span>)
<span class="kw">as.table</span>(p4_p)</code></pre></div>
<pre><code>## Servers L Lq W Wq CS CLq CL TCq TC
## A 6.00 17.12 11.52 146.73 98.73 45.00 92.15 136.95 137.15 181.95
## B 7.00 7.54 1.94 64.66 16.66 52.50 15.55 60.35 68.05 112.85
## C 8.00 6.23 0.63 53.41 5.41 60.00 5.05 49.85 65.05 109.85
## D 9.00 5.83 0.23 50.00 2.00 67.50 1.87 46.67 69.37 114.17
## E 10.00 5.69 0.09 48.76 0.76 75.00 0.71 45.51 75.71 120.51
## F 11.00 5.63 0.03 48.28 0.28 82.50 0.26 45.06 82.76 127.56
## G 12.00 5.61 0.01 48.10 0.10 90.00 0.10 44.90 90.10 134.90</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Evaluating the status quo inspection station</span>
lam_f <-<span class="st"> </span><span class="dv">7</span> <span class="co">#the prints arrive randomly at an inspection station at the same mean rate as the sheets arrived at the presses (7 per hour)</span>
mu_f <-<span class="st"> </span><span class="dv">8</span> <span class="co">#Each inspection takes him 7.5 minutes, so he can inspect 8 prints per hour</span>
p <-<span class="st"> </span><span class="dv">8</span> <span class="co">#The cost of in-process inventory is estimated to be $8 per hour for each poster sheet at the presses or each print at the inspection station</span>
cost <-<span class="st"> </span><span class="dv">17</span> <span class="co"># the current inspector is in a lower job classification where the compensation is $17 per hour</span>
s <-<span class="st"> </span><span class="kw">ceiling</span>(lam_f<span class="op">/</span>mu_f) <span class="co"># lam < s*mu in steady state, so rearrange to find min s </span>
n <-<span class="st"> </span><span class="dv">3</span> <span class="co"># Max number of servers</span>
p4_i <-<span class="st"> </span><span class="kw">vector</span>() <span class="co"># Create matrix to hold data</span>
<span class="co"># Loop to find low cost</span>
<span class="cf">for</span> (i <span class="cf">in</span> s<span class="op">:</span>n){
q1_fi <-<span class="st"> </span><span class="kw">NewInput.MMC</span>(lam_f, mu_f, i) <span class="co"># Set and check the inputs of the model</span>
q1_f <-<span class="st"> </span><span class="kw">QueueingModel.i_MMC</span>(q1_fi) <span class="co"># Solve the queueing model</span>
<span class="co">#L is the all inventory (work in process) of the queue system while Lq is the portion of L that is idle (queue waiting)</span>
L <-<span class="st"> </span>q1_f<span class="op">$</span>L <span class="co"># Mean number of print in queue system </span>
Lq <-<span class="st"> </span>q1_f<span class="op">$</span>Lq <span class="co"># Mean number of print in queue</span>
W <-<span class="st"> </span>q1_f<span class="op">$</span>W<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue system</span>
Wq <-<span class="st"> </span>q1_f<span class="op">$</span>Wq<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue waiting for service</span>
CL <-<span class="st"> </span>Lq<span class="op">*</span>p <span class="co"># Cost of in-process inventory in queue</span>
CL2 <-<span class="st"> </span>L<span class="op">*</span>p <span class="co"># Cost of in-process inventory </span>
CS <-<span class="st"> </span>i<span class="op">*</span>cost <span class="co"># Cost of servers in queue system</span>
p4_i <-<span class="st"> </span><span class="kw">rbind</span>(p4_i, <span class="kw">round</span>(<span class="kw">c</span>(i, L, Lq, W, Wq, CS, CL, CL2, CL <span class="op">+</span><span class="st"> </span>CS, CL2 <span class="op">+</span><span class="st"> </span>CS), <span class="dv">2</span>)) <span class="co"># Organize in table</span>
}
<span class="kw">colnames</span>(p4_i) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Servers"</span>, <span class="st">"L"</span>, <span class="st">"Lq"</span>, <span class="st">"W"</span>, <span class="st">"Wq"</span>, <span class="st">"CS"</span>, <span class="st">"CLq"</span>, <span class="st">"CL"</span>, <span class="st">"TCq"</span>, <span class="st">"TC"</span>)
<span class="kw">as.table</span>(p4_i)</code></pre></div>
<pre><code>## Servers L Lq W Wq CS CLq CL TCq TC
## A 1.00 7.00 6.12 60.00 52.50 17.00 49.00 56.00 66.00 73.00
## B 2.00 1.08 0.21 9.28 1.78 34.00 1.66 8.66 35.66 42.66
## C 3.00 0.90 0.03 7.73 0.23 51.00 0.21 7.21 51.21 58.21</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rbind</span>(p4_i[<span class="dv">1</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">1</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>], <span class="co">#using 9 presses and 1 inspector</span>
p4_i[<span class="dv">2</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">1</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]) <span class="co">#using 9 presses and 2 inspector</span></code></pre></div>
<pre><code>## TCq TC
## [1,] 203.15 254.95
## [2,] 172.81 224.61</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rbind</span>(p4_i[<span class="dv">1</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">2</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>], <span class="co">#using 10 presses and 1 inspector</span>
p4_i[<span class="dv">2</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">2</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]) <span class="co">#using 10 presses and 2 inspector</span></code></pre></div>
<pre><code>## TCq TC
## [1,] 134.05 185.85
## [2,] 103.71 155.51</code></pre>
</div>
<div id="proposed-increasing-print-time" class="section level3">
<h3>Proposed: Increasing print time</h3>
<p>By contrast, if we were increasing the print time, it would increase the cost of the power for running each press by $7.50 per hour but the total cost would <strong>decrease by $9.63 per hour</strong>, from current total cost of $203.14 per hour to $193.51.38 per hour (highlighted green in table 3). This decrease is due to the decrease in cost of in-process inventory, by increasing the print time the average WIP inventory goes from 7-8 posters to about 5-6 posters in printing press. Although the cost of printing press increases by $5, the result shows that the cost of in-process inventory WIP decreases from $60.14 per hours to $45.51 per hour (highlighted red in table 3). This proposed solution should be considered in our final decision.</p>
<p><em>Table 3: Increasing print time by 15 mins would decrease the Total Cost by $9.63 per hour </em></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">p4b <-<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">cbind</span>(p4_p[<span class="dv">5</span>,],<span class="co">#using 10 presses and 1 inspector</span>
p4_i[<span class="dv">1</span>,])) <span class="co">#using 10 presses and 1 inspector</span>
<span class="co">#L is the all inventory (work in process) of the queue system while Lq (ork To Process)is the portion of L that is idle (queue waiting)</span>
<span class="kw">rownames</span>(p4b) <-<span class="st"> </span><span class="kw">c</span>( <span class="st">"Servers"</span>,
<span class="st">"Average number of posters (WIP)"</span>,
<span class="st">"Average number of posters in queue (WIQ)"</span>,
<span class="st">"Average wait time (WIP) (in Minutes)"</span>,
<span class="st">"Average wait time in queue waiting for service (WIQ) (in Minutes)"</span>,
<span class="st">"Cost of servers"</span>,
<span class="st">"Cost of in-process inventory waiting for service (WIQ) ($ Per Hour)"</span>,
<span class="st">"Cost of all in-process inventory (WIP) ($ Per Hour)"</span>,
<span class="st">"Cost of in-process inventory in queue including server cost (WIQ) ($ Per Hour)"</span>,
<span class="st">"Total Cost ($ Per Hour)"</span>)
<span class="kw">colnames</span>(p4b)<-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Printing Press"</span>,<span class="st">"Inspection Station"</span>)
p4b<span class="op">$</span>Total =<span class="st"> </span><span class="kw">rowSums</span>(p4b)
now2 <-<span class="st"> </span><span class="kw">cbind</span>(now, p4b)
<span class="kw">options</span>(<span class="dt">knitr.kable.NA =</span> <span class="st">''</span>)
now2 <span class="op">%>%</span>
<span class="st"> </span><span class="kw">kable</span>(<span class="st">"html"</span>, <span class="dt">row.names =</span> <span class="ot">TRUE</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">kable_styling</span>(<span class="dt">bootstrap_options =</span> <span class="st">"striped"</span>, <span class="dt">full_width =</span> F, <span class="dt">position =</span> <span class="st">"left"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">row_spec</span>(<span class="kw">nrow</span>(now2), <span class="dt">bold =</span> T, <span class="dt">color =</span> <span class="st">"white"</span>, <span class="dt">background =</span> <span class="st">"green"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">row_spec</span>(<span class="kw">nrow</span>(now2)<span class="op">-</span><span class="dv">2</span>, <span class="dt">bold =</span> T, <span class="dt">color =</span> <span class="st">"white"</span>, <span class="dt">background =</span> <span class="st">"darkred"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">column_spec</span>(<span class="dv">4</span>, <span class="dt">bold =</span> T) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">column_spec</span>(<span class="dv">7</span>, <span class="dt">bold =</span> T) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">add_header_above</span>(<span class="kw">c</span>(<span class="st">" "</span>, <span class="st">"Current"</span> =<span class="st"> </span><span class="dv">3</span>, <span class="st">"Proposed: Increasing print time"</span> =<span class="st"> </span><span class="dv">3</span>))</code></pre></div>
<table class="table table-striped" style="width: auto !important; ">
<thead>
<tr>
<th style="border-bottom:hidden" colspan="1">
</th>
<th style="border-bottom:hidden; padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px;">
Current
</div>
</th>
<th style="border-bottom:hidden; padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px;">
Proposed: Increasing print time
</div>
</th>
</tr>
<tr>
<th style="text-align:left;">
</th>
<th style="text-align:right;">
Printing Press
</th>
<th style="text-align:right;">
Inspection Station
</th>
<th style="text-align:right;">
Total
</th>
<th style="text-align:right;">
Printing Press
</th>
<th style="text-align:right;">
Inspection Station
</th>
<th style="text-align:right;">
Total
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Servers
</td>
<td style="text-align:right;">
10.00
</td>
<td style="text-align:right;">
1.00
</td>
<td style="text-align:right;font-weight: bold;">
11.00
</td>
<td style="text-align:right;">
10.00
</td>
<td style="text-align:right;">
1.00
</td>
<td style="text-align:right;font-weight: bold;">
11.00
</td>
</tr>
<tr>
<td style="text-align:left;">
Average number of posters (WIP)
</td>
<td style="text-align:right;">
7.52
</td>
<td style="text-align:right;">
7.00
</td>
<td style="text-align:right;font-weight: bold;">
14.52
</td>
<td style="text-align:right;">
5.69
</td>
<td style="text-align:right;">
7.00
</td>
<td style="text-align:right;font-weight: bold;">
12.69
</td>
</tr>
<tr>
<td style="text-align:left;">
Average number of posters in queue (WIQ)
</td>
<td style="text-align:right;">
0.52
</td>
<td style="text-align:right;">
6.12
</td>
<td style="text-align:right;font-weight: bold;">
6.64
</td>
<td style="text-align:right;">
0.09
</td>
<td style="text-align:right;">
6.12
</td>
<td style="text-align:right;font-weight: bold;">
6.21
</td>
</tr>
<tr>
<td style="text-align:left;">
Average wait time (WIP) (in Minutes)
</td>
<td style="text-align:right;">
64.43
</td>
<td style="text-align:right;">
60.00
</td>
<td style="text-align:right;font-weight: bold;">
124.43
</td>
<td style="text-align:right;">
48.76
</td>
<td style="text-align:right;">
60.00
</td>
<td style="text-align:right;font-weight: bold;">
108.76
</td>
</tr>
<tr>
<td style="text-align:left;">
Average wait time in queue waiting for service (WIQ) (in Minutes)
</td>
<td style="text-align:right;">
4.43
</td>
<td style="text-align:right;">
52.50
</td>
<td style="text-align:right;font-weight: bold;">