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

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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Publication Details

Journal
Applied Sciences
Published
2026-03-23
DOI
https://doi.org/10.3390/app16063105
Primary Topic
Facility Location and Emergency Management
Type
article
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article

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

Futai Liang, M. F. Liu, Guocheng Wang, Lintao Liu
Applied Sciences
Facility Location and Emergency Management
article

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

Futai Liang, M. F. Liu, Guocheng Wang, Lintao Liu
article en

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.

Applied SciencesVol. 16(6)
Chinese Academy of Sciences (CN), Institute of Geodesy and Geophysics (CN), PLA Army Engineering University (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 12%
Facility Location and Emergency Management
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Research on Multi-Objective Optimization Model and Algorithm for Reliability Location of Emergency Facilities — Futai Liang, M. F. Liu, et al. · Applied Sciences (2026) | TGRS Research Map | TGRS