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Supply chain network design under uncertainty with new insights from contracts

Supply chain network design under uncertainty with new insights from contracts

作     者:Mohammad Mohajer TABRIZI Behrooz KARIMI 

作者机构:Department of Industrial Engineering Amir Kabir University of Technology 

出 版 物:《Journal of Zhejiang University-Science C(Computers and Electronics)》 (浙江大学学报C辑(计算机与电子(英文版))

年 卷 期:2014年第15卷第12期

页      面:1106-1122页

核心收录:

学科分类:12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基  金:Project supported by the Faculty of Industrial Engineering and Management Systems Amir Kabir University of Technology Iran 

主  题:Supply chain network design Contracts Uncertainty Conditional value at risk 

摘      要:In this paper, the classical problem of supply chain network design is reconsidered to emphasize the role of contracts in uncertain environments. The supply chain addressed consists of four layers: suppliers, manufacturers, warehouses, and customers acting within a single period. The single owner of the manufacturing plants signs a contract with each of the suppliers to satisfy demand from downstream. Available contracts consist of long-term and option contracts, and unmet demand is satisfied by purchasing from the spot market. In this supply chain, customer demand, supplier capacity, plants and warehouses, transportation costs, and spot prices are uncertain. Two models are proposed here: a risk-neutral two-stage stochastic model and a risk-averse model that considers risk measures. A solution strategy based on sample average approximation is then proposed to handle large scale problems. Extensive computational studies prove the important role of contracts in the design process, especially a portfolio of contracts. For instance, we show that long-term contract alone has similar impacts to having no contracts, and that option contract alone gives inferior results to a combination of option and long-term contracts. We also show that the proposed solution methodology is able to obtain good quality solutions for large scale problems.

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