Dynamic Constraint-Aware Particle Swarm Optimization for Resource Allocation in Logistics and Transportation

Xijing Ou

Abstract


With the rapid expansion of the logistics and transportation industry, effective resource allocation and scheduling have become critical to operational efficiency. This study proposes a Constraint-Aware Particle Swarm Optimization model for Logistics Resource Allocation and Scheduling (LRAS-PSO), which incorporates three core innovations: (1) a Transportation Scenario Complexity Index (TSCI) to enable adaptive parameter tuning, (2) a real-time monitoring module utilizing IoT data, and (3) a constraint-handling mechanism to address emergencies like vehicle failure and route blockage. The model is empirically evaluated on the widely used Solomon benchmark dataset. Compared with traditional linear programming, genetic algorithms, and ant colony optimization, LRAS-PSO demonstrates a minimum 25.5% reduction in transportation cost and approximately 15–20% improvement in transportation efficiency across multiple logistics scenarios. These results underscore the practical value of LRAS-PSO in enabling intelligent and adaptive logistics management.


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DOI: https://doi.org/10.31449/inf.v49i29.9209

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