文献类型：期刊文献

英文题名：Supply Chain Risk Management in Chinese Chemical Industry based on Stochastic Chance-Constrained Programming Model

作者：Liu, Liping[1];Li, Shuxia[1];Wu, Yifan[1]

机构：[1]E China Univ Sci & Technol, Sch Business, Inst Operat & Supply Chain Management, Res Ctr Virtual Business, Shanghai 200237, Peoples R China

年份：2014

卷号：8

期号：3

起止页码：1201

外文期刊名：APPLIED MATHEMATICS & INFORMATION SCIENCES

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000331387600033)】；

基金：This research was supported by the National Natural Science Foundation of China (NSFC) for the projects of "Transportation mode and transportation route optimization of hazardous materials multimodal transportation based on dynamic risk (No. 71302043), "Dynamic Environmental Risk Assessment and its Evolution Forecast of Hazardous Chemicals Accidents" (No. 71001039), "Cross-docking-based Scheduling Optimization Method for Emergency supplies" (No. 71101051) and "Production Strategy and Scheduling Problem for Hybrid Production System with Outsourcing Allowed" (No. 71202053), and also by Humanities and Social Sciences Youth Fund from Ministry of Education for the project of "Research on dynamic risk assessment for transport of dangerous chemicals" (No. 12YJC630120) and National Key Technology R&D Program "R&D and Application Demonstration of Supply Chain Collaborative E-commerce Technology" (No. 2013BAH11F00).

语种：英文

外文关键词：Chemical industry supply chain risk risk management; stochastic chance-constrained programming model

摘要：It is necessary to research on how to manage risks in Chinese chemical industry supply chain effectively. After analyzing supply chain risks and mitigation strategies in Chinese chemical industry supply chain, firstly this paper defines the supply chain risk management problem in Chinese chemical industry. Then this paper develops a stochastic chance-constrained programming model to select optimal mitigation strategies effectively to achieve risk reduction goals without exceeding the expenses budget. Finally, this paper gives a numerical example and discusses how the change of model parameter affects the optimal solution.