基于风险管理的动态供应链超网络均衡模型

doi:10.12005/orms.2015.0001

摘要/Abstract

摘要： 基于风险管理的动态供应链超网络均衡模型的研究有助于供应链超网络节点厂商在动态环境下优化其风险管理,降低风险损失,提升供应链网络在风险管理下的竞争优势。本文以三层供应链超网络为研究对象,采用风险发生概率和损失函数表达供应链超网络中节点厂商中断风险的特征,构建了基于风险管理的动态供应链超网络均衡模型。模型中包括三种类型的节点,产品生产商、零售商和需求市场,生产商考虑风险损失的情况下,基于动态变化的风险、需求和成本追求个体期望效益最优化。接着,通过进化变分不等式来表达动态供应链超网络风险管理下的均衡解,并采用投影动态系统求解进化变分不等式,通过数值算例验证方法的可靠性和合理性,通过投影动态系统解释某一个厂商趋近均衡解的过程。通过单一厂商趋近均衡解的过程,阐述其他厂商相应的最优决策。

关键词: 超网络, 进化变分不等式, 投影动态系统, 个体优化, 全局优化

Abstract: The research on supply chain network equilibrium model based on risk management helps the firms in supply chain network optimize their risk management, reduce risk loss and promote the competitive advantage of supply chain network under risk management. First of all, this paper develops a three-layer supply chain network equilibrium model by using probability of risk occurrence and risk loss function to express the characteristics of risk management in supply chain network. This model includes three types of nodes: products manufacturers, products retailers and demand markets. Manufacturers optimize their individual profits based on risk loss, cost and revenue. Then, evolutional variational inequalities are used to express the equilibrium solution of dynamic supply chain supernetwork based on risk management. The projected dynamic system is utilized to solve the evolutional variational inequalities, and a numerical example is used to prove this model is reliable and reasonable. Other manufacturers’ optimized decision making is described when one manufacturer selects a nonoptimized decision. The projected dynamic system is used to illustrate the process of a firm approaching the optimized solution. Sensitivity analysis is compared in this example.

Key words: supernetwork, evolution variational inequalities, projected dynamical system, user optimization, system optimization

中图分类号:

F406.2

马军, 董琼, 杨德礼. 基于风险管理的动态供应链超网络均衡模型[J]. 运筹与管理, 2015, 24(1): 1-9.

MA Jun, DONG Qing, YANG De-Li. Dynamic Supply Chain Supernetwork Equilibrium Model Based on Risk Management[J]. Operations Research and Management Science, 2015, 24(1): 1-9.

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