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Scalable Multi-Objective Optimization for Facility Location Using a Metaheuristic Technique
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AI
DOI：10.31436/iiumej.v27i2.4072.png)
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Abstract
En 中文
Rapid growth in e-commerce and service expectations forces large-scale logistics networks to balance facility operating cost, service coverage, and delivery equity simultaneously. This study aims to develop a scalable discrete multi-objective particle swarm optimization (MOPSO-FLP) framework for constraint-rich facility location problems, where traditional exact approaches become impractical. The proposed method integrates discrete encoding, feasibility repair, adaptive parameter control, and an archive-based leader selection strategy, and it is evaluated using benchmark instances and a national postal logistics network case. Across 30 independent runs, MOPSO-FLP achieves superior convergence and diversity compared with representative multi-objective metaheuristics (e.g., NSGA-II and related baselines), and the deployment validation yields a Pareto set of 47 non-dominated solutions that clearly exposes cost-coverage-equity trade-offs. Overall, the results demonstrate that the proposed framework provides decision makers with interpreted alternatives and actionable policies for large-scale logistics planning.
Keywords:
Facility location
Logistics systems
Multi-objective Optimization
Particle swarm optimization
Metaheuristics