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March 25, 2026Applied Sciences0 citationsOpen Access

Research on Multi-Objective Optimization Model and Algorithm for Reliability Location of Emergency Facilities

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MLM. F. LiuChinese Academy of SciencesLLLintao LiuChinese Academy of SciencesFLFutai LiangPLA Army Engineering University

Key Points

  • The research aims to create a multi-objective optimization model that considers various decision-making factors for emergency facility locations.
  • Developed a multi-objective optimization model focusing on cost, service quality, and material shortages.
  • Utilized the Non-Dominated Sorting Genetic Algorithm (NSGA-II) to obtain Pareto frontier solutions.
  • Conducted sensitivity analysis on the model under different disruption risks to evaluate reliability.
  • Achieved optimal locations for emergency facilities that minimize costs and service quality disparities.
  • Established a solution set that illustrates a balance between competing objectives.
  • Identified topological structures for facility allocations based on varying disruption scenarios.

Abstract

The issue of emergency facility location is a long-term strategic issue, and the complexity and diversity of the decision-making environment force decision-makers to focus on multiple objectives when making location decisions. We develop a multi-objective optimization system centered on cost-effectiveness, service balance, and fairness, targeting three core objectives: minimizing total costs, minimizing differences in service quality among demand points, and minimizing material shortage gaps between demand points. To address the issue of limited facility service capacity induced by material shortages, we establish a multi-objective optimization model for the reliable location of emergency facilities. By combining the model’s characteristics with the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and an elite retention strategy, the Pareto frontier solution set of the multi-objective model is obtained, and the model’s feasibility is verified through various examples of different scales. Finally, sensitivity analysis was conducted on the reliability location model of emergency facilities under different disruption risks using the control variable method, and the topology structure of the reliability location allocation network for emergency facilities under different disruption situations is obtained. The research findings provide decision-makers with actionable references and technical support for selecting reliable locations for emergency facilities amid disruption risks.

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Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/69c37be2b34aaaeb1a67eb95https://doi.org/10.3390/app16063105
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