The green supplier selection and order allocation in the supply chain under stochastic conditions
The complexity of the competitive marketing in recent decades has forced the firms, industrial groups and researchers to have more attention towards the supply chain. The supply chain is a network that changes the raw materials to the finished products by passing them through the linked entities. Th...
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| Format: | Thesis |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/50737/25/PooriaHashemzahiMFKM2014.pdf http://eprints.utm.my/id/eprint/50737/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:85184 |
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| Summary: | The complexity of the competitive marketing in recent decades has forced the firms, industrial groups and researchers to have more attention towards the supply chain. The supply chain is a network that changes the raw materials to the finished products by passing them through the linked entities. This network consists of suppliers, industrial groups, warehouses, distribution centers, and retailers. Suppliers as a part of a supply chain play a functional role in this network. Since preserving the environment has been a paramount criterion for researchers in recent years, considering the green environment factors in the supply chain could be more favorable. This study was conducted to select the best suppliers among the existing suppliers, and allocate an appropriate order quantity to each of them. The input data have an uncertainty and fuzziness in the real problem, and this study considered this fact in the different levels of supplier selection. Firstly, this study defined the cost, lead time and green environment factors as the qualitative factors, and used fuzzy analytic hierarchy process (FAHP) to weigh each of the existed supplier regarding these criteria. Secondly, a mathematical multi-objective nonlinear model formed with three different objective functions under the stochastic conditions including the demand quantity and the demand timing. Total cost of purchasing, total value of purchasing, and supplier flexibility were considered in this mathematical model as the objective functions, simultaneously. The genetic algorithm was utilized to solve this mathematical method using MATLAB software. Finally, the best suppliers and their optimum order quantity ratio was obtained, and the optimum purchasing fitness function was calculated. |
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