A Hybrid Brain Storm Optimization Algorithm for Dynamic Vehicle Routing Problem With Time Windows

Abstract

The vehicle routing problem (VRP) holds significant applications in logistics and distribution scenarios. This paper presents a hybrid brain storm optimization (BSO) algorithm for solving the dynamic vehicle routing problem with time windows (DVRPTW). The proposed hybrid BSO algorithm effectively addresses the dynamic emergence of new customers and minimizes the number of unserved customers by utilizing the repeated insertion algorithm. Furthermore, the algorithm uses BSO clustering operations to classify vehicle routes and facilitates mutual learning within and between classes through $\lambda $ -interchange. The intra-class similarity expedites solution convergence, while the inter-class difference expands the search space to avoid local optima. Finally, the quality of the solution is enhanced through the application of the 2-opt operation. To evaluate its performance, we compare the proposed algorithm with state-of-the-art algorithms using Lackner’s benchmark. The experimental results demonstrate that our algorithm significantly reduces the number of unserved customers.

Keywords

• Brain storm optimization
• dynamic vehicle routing problem with time windows
• repeated insertion