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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Main Author:	Hashemzahi, Pooria
Format:	Thesis
Language:	English
Published:	2014
Subjects:	HD28 Management Industrial Management
Online Access:	http://eprints.utm.my/id/eprint/50737/25/PooriaHashemzahiMFKM2014.pdf
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spelling	my-utm-ep.507372020-07-10T11:02:23Z The green supplier selection and order allocation in the supply chain under stochastic conditions 2014-06 Hashemzahi, Pooria HD28 Management. Industrial Management 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. 2014-06 Thesis http://eprints.utm.my/id/eprint/50737/ http://eprints.utm.my/id/eprint/50737/25/PooriaHashemzahiMFKM2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:85184 masters Universiti Teknologi Malaysia, Faculty of Mechanical Engineering Faculty of Mechanical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	HD28 Management Industrial Management
spellingShingle	HD28 Management Industrial Management Hashemzahi, Pooria The green supplier selection and order allocation in the supply chain under stochastic conditions
description	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.
format	Thesis
qualification_level	Master's degree
author	Hashemzahi, Pooria
author_facet	Hashemzahi, Pooria
author_sort	Hashemzahi, Pooria
title	The green supplier selection and order allocation in the supply chain under stochastic conditions
title_short	The green supplier selection and order allocation in the supply chain under stochastic conditions
title_full	The green supplier selection and order allocation in the supply chain under stochastic conditions
title_fullStr	The green supplier selection and order allocation in the supply chain under stochastic conditions
title_full_unstemmed	The green supplier selection and order allocation in the supply chain under stochastic conditions
title_sort	green supplier selection and order allocation in the supply chain under stochastic conditions
granting_institution	Universiti Teknologi Malaysia, Faculty of Mechanical Engineering
granting_department	Faculty of Mechanical Engineering
publishDate	2014
url	http://eprints.utm.my/id/eprint/50737/25/PooriaHashemzahiMFKM2014.pdf
_version_	1747817524159840256

The green supplier selection and order allocation in the supply chain under stochastic conditions

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