A multi-objective Particle Swarm Optimization framework for defense logistics decision-making under dynamic and crisis conditions
Keywords:
Artificial Intelligence, Decision Support System, Defense Logistics, MO-PSO, Multiobjective OptimizationAbstract
The complexity of decision-making in defense logistics systems has increased significantly due to demands for cost efficiency, distribution speed, and operational resilience in dynamic and crisis conditions. Conventional optimization approaches generally fail to capture these conflicting objectives simultaneously. This study aims to develop and evaluate a multi-objective optimization framework based on Multi-Objective Particle Swarm Optimization (MO-PSO) to support adaptive and performance-based defense logistics decision-making. The proposed method optimizes three main objective functions, namely minimizing operational costs, minimizing distribution time, and maximizing logistics readiness levels, with numerical parameter adjustments designed for the defense environment. Simulation results show that MO-PSO is capable of producing a more convergent and evenly distributed Pareto Front compared to comparison methods such as NSGA-II and standard MOPSO, with a 12.4–18.7% increase in hypervolume and a 21.3% decrease in solution dominance error. These findings indicate that the proposed approach is more effective in simultaneously balancing multi-objective trade-offs. Practically, the research results provide policy implications for defense planners in designing logistics strategies that are more efficient, responsive, and resilient to operational uncertainty.
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