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Two-stage mean-risk stochastic mixed integer optimization model for location-allocation problems under uncertain environment
Journal of Industrial and Management Optimization ( IF 1.3 ) Pub Date : 2020-05-19 , DOI: 10.3934/jimo.2020094
Zhimin Liu , Shaojian Qu , Hassan Raza , Zhong Wu , Deqiang Qu , Jianhui Du

The problem of the optimal location-allocation of processing factory and distribution center for supply chain networks under uncertain transportation cost and customer demand are studied. We establish a two-stage mean-risk stochastic 0-1 mixed integer optimization model, by considering the uncertainty and the risk measure of the supply chain. Given the complexity of the model this paper proposes a modified hybrid binary particle swarm optimization algorithm (MHB-PSO) to solve the resulting model, yielding the optimal location and maximal expected return of the supply chain simultaneously. A case study of a bread supply chain in Shanghai is then presented to investigate the specific influence of uncertainties on the food factory and distribution center location. Moreover, we compare the MHB-PSO with hybrid particle swarm optimization algorithm and hybrid genetic algorithm, to validate the proposed algorithm based on the computational time and the convergence rate.

中文翻译:

不确定环境下位置分配问题的两阶段均值风险随机混合整数优化模型

研究了在运输成本和客户需求不确定的情况下供应链网络加工厂和配送中心的优化选址问题。通过考虑供应链的不确定性和风险测度,我们建立了一个两阶段均值风险随机0-1混合整数优化模型。鉴于模型的复杂性,本文提出了一种改进的混合二元粒子群优化算法 (MHB-PSO) 来求解所得模型,同时产生供应链的最佳位置和最大预期回报。然后介绍了上海面包供应链的案例研究,以调查不确定性对食品工厂和配送中心位置的具体影响。而且,
更新日期:2020-05-19
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