Multi-compartment vehicle selection and delivery strategy with time windows under multi-objective optimization

The main research topic of this article is the distribution strategy problem for multi-compartment and single cabin vehicles under multi-objective conditions, namely the time dependence of multi-compartment vehicle selection problem TDMCVRPTWs. This article uses NSGA-II algorithm and improved Large Scale Domain Search (ILNS) to fuse, and uses the fusion algorithm to experiment with Solomon dataset. In order to verify the advantages of improved large-scale domain search (ILNS), this article uses 100 customers from Solomon to verify. The ILNS algorithm will be compared with algorithms such as ACS, HAC, HVANS, and HABC. Experiments have shown that the improved large-scale domain search (ILNS) has good computational performance. The experimental data of three modes adapted by 100 customers of Solomon show that multi-compartment vehicle fleets have significant advantages under transportation costss, while the deterioration costs advantages of single compartment vehicle and multi-compartment vehicle combined fleets are prominent. In terms of total cost, a mixed fleet is more advantageous than a single multi-compartment vehicle fleet.

Automated summary

This article focuses on the distribution strategy problem for multi-compartment and single cabin vehicles under multi-objective conditions. It uses the NSGA-II algorithm and improved Large Scale Domain Search (ILNS) to experiment with Solomon dataset. The experiments show that multi-compartment vehicle fleets have significant advantages in transportation costs and a mixed fleet is more advantageous in terms of total costs.