An epsilon-constraint method for integer-ordered bi-objective simulation optimization. Cooper, K., Hunter, S. R., & Nagaraj, K. In Chan, W. K. V., D'Ambrogio, A., Zacharewicz, G., Mustafee, N., Wainer, G., & Page, E., editors, Proceedings of the 2017 Winter Simulation Conference, pages 2303–2314, Piscataway, NJ, 2017. Institute of Electrical and Electronics Engineers, Inc..
![An epsilon-constraint method for integer-ordered bi-objective simulation optimization [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex
Paper doi abstract bibtex Consider the context of integer-ordered bi-objective simulation optimization, in which the feasible region is a finite subset of the integer lattice. We propose a retrospective approximation (RA) framework to identify a local Pareto set that involves solving a sequence of sample-path bi-objective optimization problems at increasing sample sizes. We apply the epsilon-constraint method to each sample-path bi-objective optimization problem, thus solving a sequence of constrained single-objective problems in each RA iteration. We solve each constrained single-objective optimization problem using the SPLINE algorithm, thus exploiting gradient-based information. In early RA iterations, when sample sizes are small and standard errors are relatively large, we provide only a rough characterization of the Pareto set by making the number of epsilon-constraint problems a function of the standard error. As the RA algorithm progresses, the granularity of the characterization increases until we solve as many epsilon-constraint problems as there are points in the (finite) image of the local Pareto set. Our algorithm displays promising numerical performance.
@inproceedings{2017coohunnagWSC,
Year = {2017},
Author = {K. Cooper and S. R. Hunter and K. Nagaraj},
Title = {An epsilon-constraint method for integer-ordered bi-objective simulation optimization},
Booktitle = {Proceedings of the 2017 Winter Simulation Conference},
Editor = {W. K. V. Chan and A. {D'Ambrogio} and G. Zacharewicz and N. Mustafee and G. Wainer and E. Page},
Publisher = {Institute of Electrical and Electronics Engineers, Inc.},
Address = {Piscataway, NJ},
doi = {10.1109/WSC.2017.8247961},
pages = {2303--2314},
url = {http://web.ics.purdue.edu/~hunter63/PAPERS/pre2017coohunnagWSC.pdf},
abstract = {Consider the context of integer-ordered bi-objective simulation optimization, in which the feasible region is a finite subset of the integer lattice. We propose a retrospective approximation (RA) framework to identify a local Pareto set that involves solving a sequence of sample-path bi-objective optimization problems at increasing sample sizes. We apply the epsilon-constraint method to each sample-path bi-objective optimization problem, thus solving a sequence of constrained single-objective problems in each RA iteration. We solve each constrained single-objective optimization problem using the SPLINE algorithm, thus exploiting gradient-based information. In early RA iterations, when sample sizes are small and standard errors are relatively large, we provide only a rough characterization of the Pareto set by making the number of epsilon-constraint problems a function of the standard error. As the RA algorithm progresses, the granularity of the characterization increases until we solve as many epsilon-constraint problems as there are points in the (finite) image of the local Pareto set. Our algorithm displays promising numerical performance.},
keywords = {simulation optimization > multi-objective > integer-ordered}}Downloads: 0
{"_id":"zP7EhFJQ4cW6H9PZa","bibbaseid":"cooper-hunter-nagaraj-anepsilonconstraintmethodforintegerorderedbiobjectivesimulationoptimization-2017","downloads":2,"creationDate":"2017-08-03T21:20:57.586Z","title":"An epsilon-constraint method for integer-ordered bi-objective simulation optimization","author_short":["Cooper, K.","Hunter, S. R.","Nagaraj, K."],"year":2017,"bibtype":"inproceedings","biburl":"http://web.ics.purdue.edu/~hunter63/PAPERS/srhunterweb.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","year":"2017","author":[{"firstnames":["K."],"propositions":[],"lastnames":["Cooper"],"suffixes":[]},{"firstnames":["S.","R."],"propositions":[],"lastnames":["Hunter"],"suffixes":[]},{"firstnames":["K."],"propositions":[],"lastnames":["Nagaraj"],"suffixes":[]}],"title":"An epsilon-constraint method for integer-ordered bi-objective simulation optimization","booktitle":"Proceedings of the 2017 Winter Simulation Conference","editor":[{"firstnames":["W.","K.","V."],"propositions":[],"lastnames":["Chan"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["D'Ambrogio"],"suffixes":[]},{"firstnames":["G."],"propositions":[],"lastnames":["Zacharewicz"],"suffixes":[]},{"firstnames":["N."],"propositions":[],"lastnames":["Mustafee"],"suffixes":[]},{"firstnames":["G."],"propositions":[],"lastnames":["Wainer"],"suffixes":[]},{"firstnames":["E."],"propositions":[],"lastnames":["Page"],"suffixes":[]}],"publisher":"Institute of Electrical and Electronics Engineers, Inc.","address":"Piscataway, NJ","doi":"10.1109/WSC.2017.8247961","pages":"2303–2314","url":"http://web.ics.purdue.edu/~hunter63/PAPERS/pre2017coohunnagWSC.pdf","abstract":"Consider the context of integer-ordered bi-objective simulation optimization, in which the feasible region is a finite subset of the integer lattice. We propose a retrospective approximation (RA) framework to identify a local Pareto set that involves solving a sequence of sample-path bi-objective optimization problems at increasing sample sizes. We apply the epsilon-constraint method to each sample-path bi-objective optimization problem, thus solving a sequence of constrained single-objective problems in each RA iteration. We solve each constrained single-objective optimization problem using the SPLINE algorithm, thus exploiting gradient-based information. In early RA iterations, when sample sizes are small and standard errors are relatively large, we provide only a rough characterization of the Pareto set by making the number of epsilon-constraint problems a function of the standard error. As the RA algorithm progresses, the granularity of the characterization increases until we solve as many epsilon-constraint problems as there are points in the (finite) image of the local Pareto set. Our algorithm displays promising numerical performance.","keywords":"simulation optimization > multi-objective > integer-ordered","bibtex":"@inproceedings{2017coohunnagWSC,\n\tYear = {2017},\n\tAuthor = {K. Cooper and S. R. Hunter and K. Nagaraj},\n\tTitle = {An epsilon-constraint method for integer-ordered bi-objective simulation optimization},\n\tBooktitle = {Proceedings of the 2017 Winter Simulation Conference},\n\tEditor = {W. K. V. Chan and A. {D'Ambrogio} and G. Zacharewicz and N. Mustafee and G. Wainer and E. Page},\n\tPublisher = {Institute of Electrical and Electronics Engineers, Inc.},\n Address = {Piscataway, NJ},\n\tdoi = {10.1109/WSC.2017.8247961},\n\tpages = {2303--2314},\n\turl = {http://web.ics.purdue.edu/~hunter63/PAPERS/pre2017coohunnagWSC.pdf},\n\tabstract = {Consider the context of integer-ordered bi-objective simulation optimization, in which the feasible region is a finite subset of the integer lattice. We propose a retrospective approximation (RA) framework to identify a local Pareto set that involves solving a sequence of sample-path bi-objective optimization problems at increasing sample sizes. We apply the epsilon-constraint method to each sample-path bi-objective optimization problem, thus solving a sequence of constrained single-objective problems in each RA iteration. We solve each constrained single-objective optimization problem using the SPLINE algorithm, thus exploiting gradient-based information. In early RA iterations, when sample sizes are small and standard errors are relatively large, we provide only a rough characterization of the Pareto set by making the number of epsilon-constraint problems a function of the standard error. As the RA algorithm progresses, the granularity of the characterization increases until we solve as many epsilon-constraint problems as there are points in the (finite) image of the local Pareto set. Our algorithm displays promising numerical performance.},\n\tkeywords = {simulation optimization > multi-objective > integer-ordered}}\n\n","author_short":["Cooper, K.","Hunter, S. R.","Nagaraj, K."],"editor_short":["Chan, W. K. V.","D'Ambrogio, A.","Zacharewicz, G.","Mustafee, N.","Wainer, G.","Page, E."],"key":"2017coohunnagWSC","id":"2017coohunnagWSC","bibbaseid":"cooper-hunter-nagaraj-anepsilonconstraintmethodforintegerorderedbiobjectivesimulationoptimization-2017","role":"author","urls":{"Paper":"http://web.ics.purdue.edu/~hunter63/PAPERS/pre2017coohunnagWSC.pdf"},"keyword":["simulation optimization > multi-objective > integer-ordered"],"metadata":{"authorlinks":{"hunter, s":"https://web.ics.purdue.edu/~hunter63/"}}},"search_terms":["epsilon","constraint","method","integer","ordered","objective","simulation","optimization","cooper","hunter","nagaraj"],"keywords":["simulation optimization > multi-objective > integer-ordered"],"authorIDs":["2PAn3MHZBPgt7AWCw","2Z97GEaPa4YXAS8ny","2fb2S2XHAJ2t96GqF","2zh9DyNaWkygD5248","365LawuHG2j9DJRFe","3R63vuFS9Bfnm4SAW","3aJ4h5X54uMKyds8C","3dTRPZMyK6NQnkBNd","3fJLfDjoSLWT7fdsH","3wDvcSHgfLWgWtiCx","44WAdmxsAix3xNMiS","47j5kNH4LLRdyaGrh","4zadZEFLgF8nnfqBc","5456ff108b01c819300000f2","545708a08b01c81930000130","59NY6KQiaNfh8vHLK","5FBFKWvbGTDfY2rBw","5de6efe7eab4b7de010000cb","5de71da297054edf01000027","5de77dc6021482de010000f3","5de79fcfd9b46bde01000187","5de8f619978afbdf010001b9","5de95e87d574c6de0100012d","5de9cb9e5e5ac8de010000a8","5deac7d5cd660bde0100013b","5debba9fca1cdddf0100001f","5debc23aca1cdddf010000a3","5dec31c1d39dc7de01000079","5dec95d412c53edf01000100","5decfeeb3d02efdf010000c3","5dee96590ceb4cdf01000042","5deef47fdca291de010000b6","5def2332e83f7dde01000033","5defaf90d6a2dcde01000049","5defbb05d6a2dcde0100015e","5df0afd18367c8de0100005d","5df0b2dd8367c8de0100009d","5df0f25545b054df01000141","5df26ad663aac8df01000200","5df27fc327cd2fde0100013e","5df28f16cf8320de01000079","5df2f405b91ab0de01000180","5df3217b3b310cde010000ca","5df32ee83b310cde01000188","5df33739683178f301000067","5df36544d617e5de0100016e","5df385252b1f8ade01000008","5df396762b1f8ade0100011e","5df3df3eccbb4fde0100007a","5df4333396bfa7de01000047","5df4741a95416ade0100008e","5df4acfaa50a84df01000090","5df4c91a55b997de0100008a","5df4d56755b997de01000116","5df50a85daf598df01000126","5df516b7ea1457de01000068","5df5360afd245cde010000ba","5df5d11964d1d0df010000cf","5df631ba38e915de01000064","5df67c7a72bbd4df01000001","5df68f5a72bbd4df010000d6","5df7b85592a8e4df0100006b","5df7e5d6dc100cde010001b8","5df7f520b9bb17df010000bd","5df814fcd74ee7df010000df","5df85a56b99bcff301000001","5df88ca4db7d9ddf01000110","5df89f2610b1d1de010000a2","5df8f42b277e45de01000150","5df919f22e8c31df010001d6","5df92fe1d04b27df01000113","5df9503dccc001de0100013d","5dfbb3f6f6f0aede010000c8","5dfc0137b371afde0100005a","5dfd0d06ea1680de010000f3","5dfe83cc26fad1de0100001b","5dfedcd95dd8e7df01000011","5e015a766afa18de010000ae","5e02bf6319da8edf01000065","5e036b4cb1544ef2010000ab","5e04163d0fe3b7df01000099","5e07ed2cf1089ddf0100009a","5e08084ccdee3adf01000080","5e08ac387dc1dcdf0100006f","5e090c9079e131f3010000e7","5e093fc4934cacdf01000096","5e0a052252efb3de010000df","5e0a22ef52fbd9de010000e2","5e0acf4427625ede010000ec","5e0cd62c6762d1de01000093","5e0cf51b5631a6de01000094","5e0de73145d2fdde01000029","5e0e27de2b8028de0100007a","5e0e567fac7d11df01000039","5e0e86caa0f484df010000f1","5e0eb3bc03f891de01000054","5e0f244896e707df0100007a","5e0f4b622c4a31df01000129","5e0fbb33f350a2de010000ba","5e101e502ef76bdf0100004c","5e10701e71c264df0100003c","5e10b66fcfb06ddf010000a8","5e10d48b0192c6df010000ca","5e114ffcb59632f20100001f","5e1181d77da100de010001ab","5e119e1b91bc7ade0100013f","5e11ecc13e1c29de010000b5","5e120d52e9f185de01000132","5e122ffbc196d3de01000035","5e12599a31427bdf01000085","5e12b0303f181ade01000099","5e13249156233bde0100012b","5e139165a212e1de0100014d","5e13c558280ddede010000e2","5e13fa50f8aa5dde0100005b","5e149a2d830852de0100005f","5e14c260e55ed8de01000116","5e154833edfb1ede010000e4","5e15647d1e2528de01000046","5e15798df1f31adf01000019","5e1600f6efa1cddf01000257","5e168ec10ba191df010001ec","5e16af67dc7739de01000036","5e1764e44df69dde01000172","5e1770aa4df69dde0100021a","5e17893acf35a4de010001ad","5e17d5f74ba003df010000c7","5e187dc267b9ebde010000f1","5e18e6518fcbc2df010000a8","5e194b1e86b4aade010000c8","5e19adbf078978df0100004c","5e19fdebcde53bde01000028","5e1a11f1cde53bde0100011e","5e1aeaab5f3d2cdf01000100","5e1b901f7c0fe1df010000b8","5e1bb3c961cb16df010000b2","5e1c88124c9abfde010000ba","5e1cb0ab7723aadf010000f3","5e1d275afeb115df01000185","5e1dc86e8d71ddde01000151","5e1df7292cced5de010000af","5e1e0a93f6dca7f20100001a","5e1e11fdf6dca7f20100009c","5e1e2060f6dca7f2010001ba","5e1e38ab407a20de010001b9","5e1e3c44407a20de0100020c","5e1f6435e8f5ddde01000105","5e1f9b324fcd1cde01000079","5e20c5105c2065de010001aa","5e21011cc63e88df01000004","5e215a7e5a651cdf0100001c","5e217a0bc7842fde01000095","5e21ea7959a877de01000087","5e221004024c69df01000155","5e232f4f327a15de010000ce","5e24791636283cde010000a6","5e2488578c3885df01000036","5e2491cf8c3885df0100009d","5e250c0a2e79a1f2010000ea","5e25bd5af299d4de0100003d","5e25ec2aa6f19fde01000233","5e2621f3408641df01000124","5e2677d0581147f2010000f1","5e26af11f3bb7ddf010000d1","5e272f59557b88de010000c2","5e276b0758994fde010000f9","5e27854655fc50df0100000c","5e2790304c3b0dde010000db","5e27d25c68d625de01000019","5e28623f6ae365de01000052","5e28abc988416fde010001ee","5e28bbeb6acacbdf010000b4","5e28d839a3df5bdf01000092","5e2b1f0927ed83df01000022","5e2b3a70b9f2cade01000019","5e2b44c7b9f2cade0100009b","5e2ca0f8061fbfde010000d9","5e2d92a6481fd6de01000044","5e2e37b0185844df01000050","5e2f0b91e374eede010002d5","5e2f55e826e5cadf01000148","5e2f8afc48b7a4df0100010a","5e3056d557a222df01000151","5e30953bcb949bdf01000187","5e3334baa5c1fdde01000190","5e334d47e0067bde0100014d","5e33873f7a676dee010000e6","5e339d1717f2c9de0100008c","5e3481b9fae8b9de0100011d","5e35205e89e3d9de01000013","5e35dd2876dd53de0100010d","5e389020030bcadf010000f9","5e38adce645ed2de010000ff","5e38d7f581a46ade01000007","5e3944a39d05f2df0100016f","5e3a050baa2adade0100013e","5e3bf9a4ea028bde010000ed","5e3c7b17feacaede01000034","5e3d8b0c96e576de010001aa","5e3dbc1807ca74de010000ce","5e3dcf30d51253de0100005d","5e3ddfacd51253de01000142","5e3e00c4a4cc0ede0100014a","5e3ef14a382e41df010000ce","5e3f20d47da304de01000038","5e3f5f61cd8fe2de01000099","5e4071b8b531d7de01000038","5e416c4ed9f47bee0100003d","5e4189e08491fadf01000072","5e4193d78491fadf0100012d","5e41fdceebe241de01000085","5e437aceb5e412df01000117","5e44441ee5a34dde010001fb","5e446de3084293df010000d8","5e45623049667cde01000047","5e45678b49667cde010000aa","5e45be2e0920e8de010000ea","5e45f90fa5737fde01000093","5e4768d445a735de01000097","5e4770dc45a735de010000da","5e4776a127a0c8de0100001b","5e49d2feb63120f2010000a3","5e49ecd0885ce9df0100005b","5e4c2ef7c1eb51df0100000e","5e4c3272c1eb51df01000054","5e4c725ff2c6ddde010000f6","5e4c86cc5cc521f20100002f","5e4c89d75cc521f20100005d","5e4d3a95d43139de010000ce","5e4d673c08a8e5de0100007d","5e4e10f1d116fbde01000042","5e4f888342a908de01000070","5e4f8c7042a908de010000a1","5e4f8d9942a908de010000b2","5e4ffa78f5b214df01000102","5e500a19933046de0100003b","5e5023168c3a2cde0100000b","5e50725dcb6c3ede01000120","5e50e8199f7a6dde01000005","5e515d9aa04830de0100004c","5e54534d88d190df01000040","5e5481967f0f44de0100011b","5e555acee89e5fde010000d1","5e565ec06f0b61df01000107","5e56a5afe177dede0100008e","5e56afb3e177dede01000129","5e56c3da96127bde010000cf","5e56fe107840dfde010001e4","5e5700387840dfde010002e1","5e5771bf16d3d2f3010000be","5e57ffa6a38020de0100013b","5e583cdb1f3fc8de01000153","5e599a85ad6c7fde0100006d","5e5b0be96e568ade0100004f","5e5b7186502fdadf010000a9","5e5c6589f4282ddf010000c0","5e5cb1f5a9598ddf0100004e","5e5d36f373eb2edf01000035","5e5d4dc973eb2edf01000228","5e5de8f4863279df01000088","5e5e052b5c89fadf0100006d","5e5e8bcec0a53dde0100007e","5e5f802d5766d9df01000192","5e600a0913e3aede01000190","5e601becc064fcde010001b2","5e6080039119f0de0100012a","5e60a33bc28c0fde0100004a","5e61098731c7d3de010001fc","5e61223c1cc34ede010001f4","5e62afbe08ebcade010001a0","5e6443fdb8c607de01000034","5e651c7a9eed46de01000116","5e656cdede41b9df01000142","5e66eb4a85689bf301000069","5e679bbcd527f0de010001ad","5e67f5f00e29d3de0100030a","5e68f31f1a389bdf010003f3","5e69a88d23ebccde010000de","5e6a8bd10e8744de010000cf","5e6b7b12c024f9de01000161","5vvrLHqsgH9E2QPjP","5wgZYSuyAMqRBppg9","69Gb9G7uXuGC9CqrT","6BSaqwxscteLBtEJv","6XixHSsWpd8nxzP7K","6o7DA2usY7FENyPRa","6oAq3TGhqimHY2ber","6rFcASaST7YgKdxHc","6sGcbuXnEYZ8Wmuej","74SNnigtjBX48iG3J","77kA4icgJBShuH63W","7A7q37F5APXNrBhxq","7AjGyhK3iuwzff65n","7BAciBd9hbBEkJefR","7CKGZqSpvcj9u5Fsr","7TpbYvGuRf5yRNuMy","7XBRX4AfHzxd8Yhai","7bRE2A4kCENz7Y5qp","83kyS3bRfQKny6SY5","8BBCAgWbd5QXXgsKi","8D7teT2RWr8xuZFD7","8YF4BGcrFpTcZeybA","8ZTszaF4sYucLJTae","8csKr3DgDfXLDRe8a","9FWArSc8hhQ3zvuZp","9Lo5KJEgpgs2rS6tb","9bvDsXr3AkbNMTj3v","9eA3jvWTYH8FSkeBT","9oBgKFdzYg3aEn5oz","A3kcMtBKeBkRzJ4To","AxHmaYpiG48A4vPpB","AzCZNMFjGr5ZYibXh","B2khThQvkeNjzTrJo","B8jEGwLatXm2dJm4r","BD5J3ZmoyvJJJibjZ","BFZEw6tWHhQ29NZhW","BP7gWobLA4F44fwyS","BYCMaBqwqZrJaH8E6","CRGYszpgrsRcosfhy","CidHtLPczZxb4Zopq","CoPYFNeBaL9REPBcJ","Cy28TS23ZijFMJLGR","DD2ZkDRBYmppLTXZN","DJh92bpFSACASgRZj","DWqxxAYWrgCbhbye5","DbNtGNp7Q4SPb66vp","DbSgAa6dd5m5PeeM8","DyKSBkzDbpr7iNQuQ","ESLhYXSuLXx4bYQoE","EWoH3ji5LgDxk4rez","EXYASwpLGtAbmfwQn","EhtcjnZvWJEn8jsNp","EnATfkZY9bxYYQc2D","EsQJ3szXcmtN6EzF5","F2HtNNL3tjydiQNGC","FDv7QksjBPSGwx34t","FK4tgksyCD6q9EXmD","FRJc6p3hR43yvMLya","FkRCFp3njhTn9oH2q","FwmskpEqEi5e22No6","GcnKnvyQRvm7ufjvG","GmkdwRpnsTbRnGPon","Gx2aaKwAwexRyq922","H5iuFjLuqh9oBbyy3","HRhyNRNQq76ox2aZq","HSRa9AAqRKaduw928","HjG3JeRSfsFsFHFge","HqKgkHBhXPQiSz6Ax","HrnA7bqRRPp5yfKCW","J4DA5MLHaQaff2ivR","J7Kj5xZj7gvxGpGdR","JZX9c6cixBcSCcRGS","JwMCBP4YSNrfrxXFx","K4A22AsSSfeqBFrpT","KNXv3J5frDgBs4hzt","KPGd3pWTc37z2ezKa","KRS6anSw2zkcvuCZF","KSmJtfMob3tCnxBEH","KZPKXjxEb8DCTSPsm","KdDhHf9MgFpueLw4h","KoB4o2BiACqj5wZpu","KpRatkH36PEhxzWRh","KqYTsXZ2jaNM5xu48","KwBBk7q2r6thvDaEh","KyrmE4w34odibeaja","LY3g34CKzH8c4GLcn","LoEuQXkNFLqmk4q2D","LpJ8vbMGQRoiDYtTA","LvMApwCWwFjQuA9xu","MSPPZ5YXozD5hjb32","MXoqkWkTXa3FNWnb3","N8xvQbfJTTeDhmem4","NLvKfJtnLhY27LX8G","NWBGWbitviQJ7PMGx","NisS4NB2Z8XsoPiop","NqpDrTcQZStBfH7GP","NzRRX2tf5vZJTwTAT","P2qZ2h7fnEgXhP8wZ","PHoZQDYbZyCnFk4qx","PLA7e5v7eqSpa2srh","Pfyj9gd9vixBdW8Mz","Phrpv7TxDneNK9ZD6","PnP7X6JmJoortQxaE","Ps7nv28bsn83ovgJ5","PuxwagkSyCKBaaJjD","QFQkLLBNXhKq7zX3p","QFggoBXHygtZ9mrgA","QSRMyDaD5AMhsJoiT","QWoxEn67eLEERCcri","QckZeqhHc6NzEWjGQ","Qx2KiCGn5ZQj3Tm6D","R2bqLqzH37LSk3dEN","R9wEkBEHQhvitAeFK","RQPtRvHp3CHJYhx82","RWdXiPog4qrpiQhXM","RXbY8jQiFTwhFpHn5","RdcJRWAbJ6di8XL34","RmERscPN9dpBehM2r","RsPEvg4f4xNkZFiDz","S5rLyt5xWqri4Bm3B","SDXNeyucEqZJYzJPP","SMyENBGLwed9bg9dn","SPB7xjcD7dK5nag5i","SjPdLifsNLFiSPJnX","SonBkYjLbzfPdue6b","TqngjqzjEatA5mESz","WMMj2X9HmEfTPF4o6","WddAKna5uDTBSTL94","Wret9iQc82sz2AY32","WxkMEB7EpNhDhnDBb","WzzR4fueCTpsbR2e6","XhoedZd4vYHegRmzZ","Xk6BmXSsoNW6gQteS","YEDQuaQoMxfrEpZi3","YWG2Cg3nQeSruic2j","YioYfkr22LZsF4Bj4","ZAFn76SXremTQfSnC","ZGgpqGFXoZgyvbx3X","ZMQfYQC2HGHJpNDSW","ZcpScSR7rNarBsSKM","ZgNmEX9rJfSr3Bueo","ZorESQjDDeNSt2vqd","ZspbD9bauzj84c8bY","aZpJsemKeCgjXbTGe","aj9xZEAY5ehNe9wAv","anZ7x9gcmg7ZwnBy4","bHxtEXL9pz5BbQNWX","bQ7n7SYYoGvTu2BqW","bfbe5r9Efrvh3Abe9","c7gnKGitCDDFYXLu2","cJkKeEvMNpbG696wB","cPdTM2nhmScQHoDtd","cojpq7debEkuiAMKD","d8uKbua5DCY5YWL45","dCen7dYEkvh5gzLBu","dKbmMcMaBaMmHXs7R","dMH3mH6h7RBoq7n5r","dXDH3shpYKxXxiXjv","dwEz5g8oMXoQejtCY","eDH5nop7nDFFYbDyB","ev3EDPEzFJB99CmzN","f7FxbjPn83vKCNwNk","f8gt8jWwjAtdSpSmi","fMuYsPwdXuaHmhxsb","fMybk9pFkPsYfu2pm","fNbLXe22X3xL5Zyth","fgAXke2pPhQrFqn2v","fuQXWK67Sdygy9tFe","fzcyEogMMEjWWo9qh","gPqmewhWtsTPbgNPx","gd4XDSpYd52DJSLQz","ggitmEfdT5fdksrZR","h4qsJcBP4TfAyHrET","hN38yNSNKhwBpjoWY","hTSRyxPMhxL3PazAr","hvCmfbQcbGXvwgZdQ","iQ8pM7cN9k9ayFEgw","iS7zLee83q7RKyECX","iaeBcQcRdCBy6xkoB","icGnxiSs3QxJHPS4s","idQTxDet42qC3Jd2x","ioDPa62Yt5HtqzWBu","iu7X2yovAZ5LEffrz","jFyTMgvSAnuwd3Me5","jKxBw8nYpeW6HLL3c","jWS7s6FfkEdYCrXxx","jv7LiD4CaQ4tRPqme","kEmEjTDBS3hhFziWL","kKdYBKCnkJskTLAEq","kYiaRyZRtgDmgu88o","kZLyMfsQmTCHFo9LC","kdqbWGpRQqFBREEbt","m2qsJXBg6utwwaZc4","mXueAvNYgyizg5WYf","msQNMu4Jo4n23S4hR","mwxyQE8C3Y9Kzu34X","nEShNQfkQDLKnm8FJ","nHggQLJ5rWfWtTAMc","nbZJFgwHuyua9ZMzN","nwgMcwNRBJkaaSMYA","nz5DrStoskLqxcz3j","o5W2wMaBXz7AsMoFD","oEHyQe2wbwZNhgMfe","oS4CBDmTdRZ6zaBzN","oaXfRQgd56s8mLizu","ozRp6sPHyXbrvuGxz","pEn65TZJdJPSjfJ6o","pnn9hoHGt9ZB7Yh3C","ppTXsix8PiTptHPTv","pvwyq2FsvHLSD2mnu","pxX3fvGrCK6CfFdxR","qAEGfBZSyYw5g44aB","qARqJvwid5k5E4KbL","qG4DGzjdabcMZA6ZK","qaXjiXZy56D7eRK8s","qmTXWT4PjGCaFf3Qf","rKgbsFjQYxr4brzA9","rRmNDMhwxWb922jFP","rgiWPKgbncLaJAqdh","rw6eycCoLApNdcEK3","s26YgqwunsDmsFZ3d","s72o6CrDptDYGTpg7","sNaWwoyt3MQZcLBvN","sPWSimL3rqxPPbFmT","sPfFWugdNbn4tjcLK","saYcRnQds6rEnJcN6","sdNwxguoZiGQuzNnQ","shGSTLbNCQ2mQ7C6B","snGb73RqNP9yhN4MD","t9ndRegAnj4uBmXvf","tLxKsv6xZTqs7ZZfE","tY7P35MLkWM2aHYhn","u2tNnhyZqjS9d7iDG","uF8fHSMEGz6tMYypJ","uJ8Rrdfthg7CzYvvi","uSN2tqTyg3s86P9bD","uWvJWjog6W8qix2qB","uXozimonaRGCzaADW","uuyFnjBtxca5zh9Ky","uvrun9f6iXENKDt3n","uzhXQMSBLEiWwaSrF","vCGw9rPG9d8XMggop","vE5RpwhAoKjbjmx8s","vEnjk9djxnuHbCyTa","vLvEkskwie7Xk8dps","vMQgcRGs3htxFRYco","vkirKDjhjYWTQZj4m","wDoGhd6RWWE4o9CeX","wnMj9SHXnBYWtKQZp","wuPh3vGzrJ69cD83u","wxJH73fuSn9HdLMj9","xBYEKZNGX3wfQdTcT","xEm7baDDyTm33FzCK","xX3BFvy85tSBTuinW","xZC6uifPzee5wRWLB","xtey9QmYxZQ5cqYgy","xuM8ocmp3RXK6CQmE","xupeg6dJBotE9GBsm","xvKzadizesMZZntjF","yBPsWrbvZRvrQ9v3w","yggMy2DdHyArMfEP2","zeq65d2AxoMPqqqcE"],"dataSources":["PkcXzWbdqPvM6bmCx","ZEwmdExPMCtzAbo22"]}