针对突发灾害情况下需求不确定的选址问题,构建最小化经济成本和最大化满意度的应急物资中心选址模型.首先,将选址问题划分为初期和后期的两阶段问题;其次,对物资需求量进行模糊需求预测,并使用可信性模糊机会约束规划将其转化为确定型约束;最后,设计改进灰狼优化(Improved Grey Wolf Optimization,IGWO)算法求解问题.IGWO算法采用佳点集初始化种群,对收敛因子进行余弦规律的非线性变化,并在粒子群优化(Particle Swarm Optimization,PSO)算法个体记忆的启发下,设计个体位置更新公式.在用10个标准函数验证IGWO有效性的基础上,通过湖北省新型冠状病毒应急物资中心选址案例分析,表明IGWO算法能有效求解多目标选址问题,在提高满意度的基础上降低经济成本,且多阶段模型在平衡满意度和经济成本方面结果更优.
Abstract
This paper studies and provides the location optimization scheme of the emergency material center to improve the response-ability of the emergency system in case of sudden disasters.According to the disaster situation,the location problem is divided into the early stage of immediate response and the late stage of sufficient resources.Analysis and comparison show that the multi-stage model has better results in balancing satisfaction and economic costs.In this paper,the demand for emergency supplies is an uncertain problem,and the fuzzy variables are analyzed using triangular fuzzy numbers.A double objective location model of emergency material centers is established to maximize the satisfaction of disaster victims and minimize the comprehensive cost.The fuzzy demand forecast is made for the material demand and the uncertain constraints are transformed into deterministic constraints by using the credibility fuzzy chance constraint programming.Aiming at the limitation of Grey Wolf Optimization(GWO)algorithm in solving location problems,this paper proposes an Improved Grey Wolf Optimization(IGWO)algorithm.Using the best point set strategy to effectively generate the initial population instead of the random generation method.Secondly,the nonlinear change of the cosine rule is applied to the convergence factor to enhance the global search ability of the algorithm to balance the global search ability and local search ability of the algorithm.Finally,inspired by Particle Swarm Optimization(PSO)algorithm,an individual memory strategy is introduced to design individual location update formula.Through ten benchmark test function simulation experiments,it is proved that the IGWO algorithm enhances the global search ability and avoids falling into local optimal solution.Therefore,the IGWO algorithm is used to solve the actual problems.The analysis of the location of the novel coronavirus emergency center in Hubei Province shows that the IGWO algorithm improves the effiiciency,speed,and robustness of the solution.The algorithm reduces the economic cost based on improving satisfaction,and can effectively solve the multi-objective location problem.
关键词
公共安全/应急救援选址/改进灰狼优化算法/多目标优化/模糊需求/个体记忆
Key words
public safety/emergency rescue site selection/Improved Grey Wolf Optimization(IGWO)algorithm/multi-objective optimization/fuzzy demand/individual memory
维普
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