Research on Multi-Center Path Optimization for Emergency Events Based on an Improved Particle Swarm Optimization Algorithm
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| Publicado en: | Mathematics vol. 13, no. 4 (2025), p. 654 |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| Resumen: | Emergency events pose critical challenges to national and social stability, requiring efficient and timely responses to mitigate their impact. In the initial stages of an emergency, decision-makers face the dual challenge of minimizing transportation costs while adhering to stringent rescue time constraints. To address these issues, this study proposes a two-stage optimization model aimed at ensuring the equitable distribution of disaster relief materials across multiple distribution centers. The model seeks to minimize the overall cost, encompassing vehicle dispatch expenses, fuel consumption, and time window penalty costs, thereby achieving a balance between efficiency and fairness. To solve this complex optimization problem, a hybrid algorithm combining genetic algorithms and particle swarm optimization was designed. This hybrid approach leverages the global exploration capability of genetic algorithms and the fast convergence of particle swarm optimization to achieve superior performance in solving real-world logistics challenges. Case studies were conducted to evaluate the feasibility and effectiveness of both the proposed model and the algorithm. Results indicate that the model accurately reflects the dynamics of emergency logistics operations, while the hybrid algorithm exhibits strong local optimization capabilities and robust performance in handling diverse and complex scenarios. Experimental findings underscore the potential of the proposed approach in optimizing emergency response logistics. The hybrid algorithm consistently achieves significant reductions in total cost while maintaining fairness in material distribution. These results demonstrate the algorithm’s applicability to a wide range of disaster scenarios, offering a reliable and efficient tool for emergency planners. This study not only contributes to the body of knowledge in emergency logistics optimization but also provides practical insights for policymakers and practitioners striving to improve disaster response strategies. |
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| ISSN: | 2227-7390 |
| DOI: | 10.3390/math13040654 |
| Fuente: | Engineering Database |