Multi-Depot Heterogeneous Vehicle Routing Optimization for Hazardous Materials Transportation

Abstract

This paper considers a multi-depot heterogeneous vehicle routing problem (MDHVRP) with time windows, which is very crucial for hazardous materials transportation. For this reason, we formalize this problem as a multi-objective MDHVRP optimization model, where the actual load dependent risk of hazardous materials transportation is considered. To solve the optimization problem, we propose a hybrid multi-objective evolutionary algorithm (HMOEA) and a two-stage algorithm (TSA). In addition, we verify the performance of the proposed algorithms by experiments on the modified Solomon’s VRPTW examples. In the experiment, it can be seen from the distribution of Pareto solution sets and the convergence distribution of IGD values that HMOEA is significantly superior to the other three algorithms in searching for Pareto solutions, as well as in the convergence and diversity of the algorithm. HMOEA and TSA were compared, and the minimum cost obtained by TSA was 13.38% lower than HMOEA, while the minimum risk was 81.69% higher than HMOEA. The advantages of each algorithm in finding solutions in reality were analyzed. A comparison was made between multi-depots heterogeneous VRP and multi-depots homogeneous VRP in the C101 instance, and the results showed that scheduling heterogeneous vehicles would reduce risk and cost.

Keywords

• Hazardous materials transportation
• actual load
• robust multi-depot heterogeneous vehicle routing problem
• hybrid multi-objective evolutionary optimization algorithm
• two-stage algorithm