7664

7136

كارشناسي ارشد

صنايع

اصفهان: دانشگاه صنعتي اصفهان، دانشكده صنايع و سيستم ها

1391

سيزده،90ص.: مصور،جدول،نمودار

ص.ع.به فارسي و انگليسي

علي شاهنده

ناصر ملاوردي

28/1/92

مهدي بيجاري، مهدي علينقيان

مهندسي صنايع و سيستم ها

ID7136

به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي

Integrated supply chain planning under uncertainty with mode selection and capacity constraint at distribution centers and communication paths Hadi Fathi hadi fathi@in iut ac ir Ali Shahandeh Supervisor ali nook@cc iut ac ir Department of Industrial and Systems Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Persian Date 2013 01 16 Abstract In this study comprehensive study of supply chain and it s components have been done in order to better understand significance of supply chain network design Then a mixed integer non linear mathematical model is presented for designing a three level multi commodity and multi site supply chain with uncertainties The purpose of this three level model are determine number and location of distribution centers and determine suitable capacity for keeping products at these centers and assign distribution centers to demand centers and suppliers to distribution centers in order to satisfy customer products requirement determine number and type of required transport vehicle to carry products from suppliers to distribution centers and determine order amount of products at each distribution centers so that total supply chain cost is minimized and capacity constraint at distribution centers warehouse and communication paths between supply distribution centers and distribution demand centers are satisfied According to proposed mathematical model the problem is NP hard and to solve this problem in large scale needs too much time So two meta heuristic solving methods based on genetic algorithm and simulated annealing algorithm in order to solve proposed model had been used and their efficiency had been studied In small scales for two first problem sets obtained results from GAMS software compare with obtained results from GA and SA algorithms Solving average time in these sets by GAMS is quite high while this amount for GA and SA is negligible Low running average time and error amount average at these problems has been shown high proposed algorithm efficiency In medium and large scale and two problem sets of small scales two GA and SA approach had been used and their efficiency had been studied The obtained results from solving different problems showed that GA just in 8 instances performs better than SA and in other instances SA obtained better or at least equal than GA Furthermore results showed SA solving average time less than GA and SA solution quality is better than GA Keywords supply chain design uncertainty multi commodity capacity constraint genetic and simulated annealing algorithm

علي شاهنده

ناصر ملاوردي

مهدي بيجاري، مهدي علينقيان