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dc.contributor.author何飛en_US
dc.contributor.authorJorge Ricardo Alania Montoyaen_US
dc.contributor.author張永佳en_US
dc.contributor.authorJasmine Changen_US
dc.date.accessioned2014-12-12T02:42:51Z-
dc.date.available2014-12-12T02:42:51Z-
dc.date.issued2013en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT070153359en_US
dc.identifier.urihttp://hdl.handle.net/11536/75251-
dc.description.abstractNowadays many companies are adapting the make-to-order (MTO) business model in order to reduce their inventory and increase their net profit. The success of this model is based on finding a good balance between reducing the transportation costs and at the same time increasing the customer satisfaction. This study considers an integrated production and distribution scheduling problem in which jobs, ordered by different customers, are processed by one of the identical parallel machines and then delivered by a third-party logistics service provider with unlimited number of capacitated vehicles within a pre-specified soft time windows to different geographical areas. The objective of this problem is to find a joint production and vehicle routing schedule, so that the total earliness penalty, the total tardiness penalty and the distribution costs can be minimized. This problem has been shown to be NP-hard. This study provided a mathematical formulation to describe the problem and designed a genetic algorithm to find near optimal solutions. This new genetic algorithm with two-layers chromosome was proven to be effective for solving the Integrated Production and Distribution Scheduling with Soft Time Windows. Also some factors, such as number of jobs, population size and number of populations’ generations were shown to have a significant impact on the final solution. This study integrated model was proven to be more effective in finding the best solution rather than the sequential model, but the execution time was longer.zh_TW
dc.description.abstractNowadays many companies are adapting the make-to-order (MTO) business model in order to reduce their inventory and increase their net profit. The success of this model is based on finding a good balance between reducing the transportation costs and at the same time increasing the customer satisfaction. This study considers an integrated production and distribution scheduling problem in which jobs, ordered by different customers, are processed by one of the identical parallel machines and then delivered by a third-party logistics service provider with unlimited number of capacitated vehicles within a pre-specified soft time windows to different geographical areas. The objective of this problem is to find a joint production and vehicle routing schedule, so that the total earliness penalty, the total tardiness penalty and the distribution costs can be minimized. This problem has been shown to be NP-hard. This study provided a mathematical formulation to describe the problem and designed a genetic algorithm to find near optimal solutions. This new genetic algorithm with two-layers chromosome was proven to be effective for solving the Integrated Production and Distribution Scheduling with Soft Time Windows. Also some factors, such as number of jobs, population size and number of populations’ generations were shown to have a significant impact on the final solution. This study integrated model was proven to be more effective in finding the best solution rather than the sequential model, but the execution time was longer.en_US
dc.language.isoen_USen_US
dc.subject因演算法zh_TW
dc.subject軟時窗zh_TW
dc.subject生產排程zh_TW
dc.subject配送排程zh_TW
dc.subjectIntegrated Production and Distribution Schedulingen_US
dc.subjectGenetic Algorithmen_US
dc.subjectMake-to-order modelen_US
dc.subjectVehicle Routing Problemen_US
dc.subjectProduction Schedulingen_US
dc.subjectSoft Time Windowsen_US
dc.title運用基因演算法求解考慮提早/延遲成本與軟時窗限制之整合生產與配送排程問題zh_TW
dc.titleA Genetic Algorithm for Integrated Production and Distribution Scheduling with Earliness/Tardiness Penalty with Soft Time Windowsen_US
dc.typeThesisen_US
dc.contributor.department工業工程與管理系所zh_TW
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