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中国管理科学 ›› 2025, Vol. 33 ›› Issue (5): 268-279.doi: 10.16381/j.cnki.issn1003-207x.2023.0926

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考虑家庭储备的应急设施选址与物资分配的分布鲁棒优化研究

李京, 车阿大()   

  1. 西北工业大学管理学院,陕西 西安 710072
  • 收稿日期:2023-06-04 修回日期:2023-11-23 出版日期:2025-05-25 发布日期:2025-06-04
  • 通讯作者: 车阿大 E-mail:ache@nwpu.edu.cn
  • 基金资助:
    国家自然科学基金项目(72271201);陕西省自然科学基金项目(2022JM-427)

Distributionally Robust Optimization on Emergency Facility Location and Relief Supply Distribution Considering Household Reserves

Jing Li, Ada Che()   

  1. School of Management,Northwestern Polytechnical University,Xi’an 710072,China
  • Received:2023-06-04 Revised:2023-11-23 Online:2025-05-25 Published:2025-06-04
  • Contact: Ada Che E-mail:ache@nwpu.edu.cn

摘要:

为了确保对突发自然灾害做出及时响应,通常需要提前建设应急设施并储备一定数量的应急物资。现有关于应急设施选址与物资分配的研究未考虑家庭储备对其决策的影响,而应急物资的家庭储备对于有效应对突发自然灾害至关重要。因此,本文研究了地震灾害背景下考虑家庭储备的应急设施选址与物资分配问题,综合考虑了灾害特征、物资供应、交通运输以及需求端的不确定性,建立了一个新颖的两阶段分布鲁棒优化模型。在灾害发生前,决策应急避难所和物资储备库的位置与容量、储备物资的数量和家庭储备试点地区;当灾害发生后,确定灾民转移和应急物资分发方案。通过对模型进行分析,设计了一个两阶段列和约束生成(column-and-constraint generation,C&CG)算法,实现了对所提出模型的有效求解。最后,以云南省某地区的历史地震为案例,验证了所提出模型和算法的有效性。

关键词: 应急设施选址, 家庭储备, 鲁棒优化, C&CG算法

Abstract:

Sudden natural disasters present substantial challenges that require swift and efficient response strategies. To adequately prepare for these events, it's essential to build emergency facilities and stockpile relief supplies in these facilities in advance. However, despite the significant role household reserves play in mitigating the impacts of unforeseen natural disasters, existing research on emergency facility location and relief supply distribution has not examined the impact of household reserves on decision-making. To address this research gap, an emergency facility location and relief supply distribution problem considering household reserves in the context of earthquake disasters is investigated. Before a disaster occurs, strategic decisions are made regarding the location and capacity of emergency shelters and relief warehouses, the amount of relief supplies pre-positioned, and the selection of areas for household reserves. Subsequently, tactical decisions for evacuating residents and distributing relief supplies are determined once a disaster occurs. The uncertainties encompassing disaster characteristics, relief supply, transportation and demand are considered. To make reliable decisions amid these uncertainties, a novel two-stage distributionally robust optimization model is introduced. This model aims to minimize the total expected costs of rescue in the worst case, including direct costs from pre-disaster preparation and post-disaster response, as well as penalty costs for unmet demands.To solve the proposed model effectively, a two-phase column-and-constraint generation (C&CG) algorithm is developed. The effectiveness of the proposed model and algorithm is verified through a case study based on historical earthquakes in a region of Yunnan Province, China. By highlighting the crucial role of household reserves in the emergency facility location and relief supply distribution problem, a novel perspective on emergency management is offered. In addition, the introduction of a two-stage distributionally robust optimization model offers a comprehensive framework for improving disaster preparedness and response strategies, providing a valuable tool for policymakers and emergency management practitioners. Based on the results of the case study, some important managerial insights are drawn as follows: Firstly, emergency management practitioners should promote the notion of self-responsibility among residents for their safety while ensuring their access to relief supplies after a disaster. Secondly, guiding and training residents to properly use household reserves to increase the post-disaster availability of supplies plays a crucial role in reducing disaster impact on people and lowering government expenses. Lastly, for the problem of emergency facility location and relief supply distribution, decision-makers need to balance the use of available data with the inherent uncertainties of such unpredictable disasters.

Key words: emergency facility location, household reserves, robust optimization, C&CG algorithm

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