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目前大多数的应急调度模型多以一层出救点来建立,而现实应急物资调度中,出救点不仅仅只有一层,而存在双层甚至是多层.本文以非线性连续供给与消耗应急物资调度为背景,以物资调度总成本最小和整体反应时间最早为目标,建立由受灾点、分配中心和储备库组成的双层应急物资调度模型.针对多目标多层级之间联动的应急物资调度模型的特点,采用基于反向学习策略和广泛学习策略的改进人工蜂群算法,得到Pareto最优解集,并分析Pareto前沿解集中解的个数与均匀性测度.通过仿真实验验证了该模型的合理性和算法的有效性,实验结果表明该算法只需较少的成本和较早整体反应时间.
At present, most of the emergency dispatching models are mostly built with a layer of rescue points, while in the real emergency dispatch, there are not only one layer but also double layers or even multiple layers.In this paper, nonlinear continuous supply and consumption Emergency supplies scheduling as the background, the minimum total cost of goods scheduling and the overall response time as the goal, the establishment of the disaster point, distribution center and reserve pool composed of two-tier emergency materials scheduling model for multi-target multi-level emergency response materials Scheduling model, we adopt the improved artificial bee colony algorithm based on the reverse learning strategy and the extensive learning strategy to get the Pareto optimal solution set, and analyze the measure of the number and uniformity of solutions of the Pareto frontier solution set.The simulation results show that The rationality of the model and the effectiveness of the algorithm, the experimental results show that the algorithm requires less cost and earlier overall response time.