Optimization Method of Knapsack Problem Based on BPSO-SA in Logistics Distribution


Yan Zhang, Tengyu Wu, Xiaoyue Ding, Journal of Information Processing Systems Vol. 18, No. 5, pp. 665-676, Oct. 2022  

10.3745/JIPS.01.0090
Keywords: Binary Particle Swarm, Knapsack Problem, Logistics Distribution, Logistics Simulation, simulated annealing
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Abstract

In modern logistics, the effective use of the vehicle volume and loading capacity will reduce the logistic cost. Many heuristic algorithms can solve this knapsack problem, but lots of these algorithms have a drawback, that is, they often fall into locally optimal solutions. A fusion optimization method based on simulated annealing algorithm (SA) and binary particle swarm optimization algorithm (BPSO) is proposed in the paper. We establish a logistics knapsack model of the fusion optimization algorithm. Then, a new model of express logistics simulation system is used for comparing three algorithms. The experiment verifies the effectiveness of the algorithm proposed in this paper. The experimental results show that the use of BPSO-SA algorithm can improve the utilization rate and the load rate of logistics distribution vehicles. So, the number of vehicles used for distribution and the average driving distance will be reduced. The purposes of the logistics knapsack problem optimization are achieved.


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Cite this article
[APA Style]
Yan Zhang, Tengyu Wu, & Xiaoyue Ding (2022). Optimization Method of Knapsack Problem Based on BPSO-SA in Logistics Distribution. Journal of Information Processing Systems, 18(5), 665-676. DOI: 10.3745/JIPS.01.0090.

[IEEE Style]
Y. Zhang, T. Wu and X. Ding, "Optimization Method of Knapsack Problem Based on BPSO-SA in Logistics Distribution," Journal of Information Processing Systems, vol. 18, no. 5, pp. 665-676, 2022. DOI: 10.3745/JIPS.01.0090.

[ACM Style]
Yan Zhang, Tengyu Wu, and Xiaoyue Ding. 2022. Optimization Method of Knapsack Problem Based on BPSO-SA in Logistics Distribution. Journal of Information Processing Systems, 18, 5, (2022), 665-676. DOI: 10.3745/JIPS.01.0090.