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Design of a multi-echelon supply chain network using multi-objective decision making approach

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dc.contributor.advisor Hasin, Dr. M. Ahsan Akhtar
dc.contributor.author Ahmed, Sayem
dc.date.accessioned 2018-09-25T06:16:41Z
dc.date.available 2018-09-25T06:16:41Z
dc.date.issued 2017-08-31
dc.identifier.uri http://lib.buet.ac.bd:8080/xmlui/handle/123456789/4993
dc.description.abstract A supply chain is a set of facilities, suppliers, customers, products and methods of controlling inventory, purchasing, and distribution. In a supply chain, the flow of goods between a supplier and customer passes through several stages, and each stage may consist of many facilities. The planning is designed to integrate supplier selection, product assembly, distribution center (DC) location selection as well as the logistic distribution system of the supply chain in order to meet market demands. In this research, the supplier selection problem is integrated with production decision and distributor location problems and a new mathematical model is proposed. While the most successful companies are aiming for total customer satisfaction, it is important to quantify service quality of suppliers so Taguchi loss function is considered in decision making process for calculating the loss in monetary terms, in case of poor supply performance. With multiple suppliers and multiple customer needs, the assembly model can be divided into several sub-assembly steps by applicable sequence. Considering three evaluation criteria, namely costs, delivery time, and quality a multi-objective optimizationmathematical model is established in this study. The multi-objective problems usually have no unique optimal solution, and the Pareto genetic algorithm (PaGA) can find good tradeoffs among all the objectives. Therefore, this study proposes a modified Pareto genetic algorithm (mPaGA) to improve the solution quality through revision of crossover and mutation operations.The results show that the proposed algorithm gives high quality solutions as well as better computational times. en_US
dc.language.iso en en_US
dc.publisher Department of Industrial Production Engineering en_US
dc.subject Production management-Genetic algorithm en_US
dc.title Design of a multi-echelon supply chain network using multi-objective decision making approach en_US
dc.type Thesis-MSc en_US
dc.contributor.id 1015082019 en_US
dc.identifier.accessionNumber 115940
dc.contributor.callno 658.5/SAY/2017 en_US


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