PSO-Augmented NSGA-III Algorithm: A Combined Optimization Approach to Heterogeneous Vehicle Routing and Bin Packing Problems

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Abstract
This study presents an Integrated Particle Swarm Optimization and Nondominated Sorting Genetic Algorithm III (IPSO-NSGA-III) to address the combined optimization challenges in logistics enterprises of heterogeneous vehicle routing and bin packing. The algorithm is designed to optimize four critical performance metrics: travel distance, time penalty costs, vehicle loading efficiency, and fixed vehicle costs. By enhancing vehicle routing and bin packing strategies, the approach targets the reduction of logistics distribution costs and increases in vehicle loading rates, ultimately enhancing the cost-effectiveness of distribution operations. The performance of IPSO-NSGA-III is rigorously evaluated through multi-objective function testing and a detailed case study, where it is compared against other leading-edge algorithms. The case study results show that, the IPSO-NSGA-III outperforms the NSGA-III by delivering more effective solutions that consider the interplay between vehicle routing and bin packing. The optimized solutions facilitate a decrease in operational expenses and an improvement in logistics efficiency, which translates to higher customer satisfaction and stronger competitiveness for logistics companies.
Funding Information
  • Key project of the Natural Science Foundation of Fujian Province, China (2022J02053)
  • 2023 Graduate Science and Technology Innovation Program Project of Xiamen University of Technology (YKJCX2023035)

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