运筹与管理 ›› 2011, Vol. 20 ›› Issue (5): 50-56.

• 理论分析与方法探讨 • 上一篇    下一篇

重大突发事件应急设施多重覆盖选址模型及算法

葛春景, 王霞, 关贤军   

  1. 同济大学,经济与管理学院 上海 201804
  • 收稿日期:2010-04-02 出版日期:2011-10-25
  • 作者简介:葛春景(1980-),男,河北保定人,博士研究生,研究方向:应急设施布局和应急管理;王霞(1966-),女,教授,博士生导师,研究方向:城市发展与管理;关贤军(1972-),男,讲师,研究方向:城市应急管理。
  • 基金资助:
    国家“985”二期“城市建设与防灾”子项目(985-Ⅱ-CJF-10)

A Multi-Covering Model and Its Algorithm for Facility Location Response for Large-Scale Emergencies

GE Chun-jing, WANG Xia, GUAN Xian-jun   

  1. School of Economics & Management, Tongji University, Shanghai 201804, China
  • Received:2010-04-02 Online:2011-10-25

摘要: 为了解决应对重大突发事件过程中应急需求的多点同时需求和多次需求问题,本文研究了应对重大突发事件的应急服务设施布局中的覆盖问题:针对重大突发事件应急响应的特点,引入最大临界距离和最小临界距离的概念,在阶梯型覆盖质量水平的基础上,建立了多重数量和质量覆盖模型。模型的优化目标是满足需求点的多次覆盖需求和多需求点同时需求的要求条件下,覆盖的人口期望最大,并用改进的遗传算法进行求解;最后给出的算例证明了模型和算法的有效性,从而应急设施的多重覆盖选址模型能够为有效应对重大突发事件的应急设施选址决策提供参考依据。

关键词: 设施选址, 多重覆盖模型, 改进的遗传算法, 应急设施

Abstract: In order to satisfy the multi-requirements for emergency facilities in response for large-scale emergencies, this paper mainly focuses on the covering location problem. Considering the special characteristics of large-scale emergency response, two concepts are introduced in this paper, that is, the minimum critical covering distance and the maximum critical covering distance for demand point. A multi-covering location model for facility response for large-scale emergencies is proposed based on the multi-quantity and quality service for demand. The objective of this model is to maximize the population covered by facilities as much as possible, addressing the demand uncertainty and multi-time coverage at the same time. The improved genetic algorithm is designed for solving the problem and a computational experiment illustrates how the proposed model works on this problem. the results show the effects of the proposed model and the algorithm. So, this proposed model can give some advise for the facility location decision response for large-scale emergencies.

Key words: facility location, multi-covering location model, improved genetic algorithm, emergency facility

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