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Solving a Two-Stage Stochastic Capacitated Location-Allocation Problem with an Improved PSO in Emergency Logistics

机译:在紧急物流中用改进的PSO解决两阶段随机电容定位问题

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摘要

A stochastic expected value model and its deterministic conversion are developed to formulate a two-stage stochastic capacitated location-allocation (LA) problem in emergency logistics; that is, the number and capacities of supply centers are both decision variables. To solve these models, an improved particle swarm optimization algorithm with the Gaussian cloud operator, the Restart strategy, and the adaptive parameter strategy is developed. The algorithm is integrated with the interior point method to solve the second-stage model. The numerical example proves the effectiveness and efficiency of the conversion method for the stochastic model and the proposed strategies that improve the algorithm.
机译:开发了一种随机预期值模型及其确定性转换,以在应急物流中制定双级随机电容定位(LA)问题;也就是说,供应中心的数量和能力都是决策变量。为了解决这些模型,开发了一种改进的粒子群优化算法,具有高斯云运算符,重启策略和自适应参数策略。该算法与内部点法集成在求解第二阶段模型。数值示例证明了随机模型转换方法的有效性和效率及改进算法的拟议策略。

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