An Enhanced Adaptive Large Neighborhood Search Algorithm for the Capacitated Vehicle Routing Problem

Abstract

Capacitated Vehicle Routing Problem (CVRP) is a representative type of Vehicle Routing Problem (VRP) and it is NP-hard. With the increase of the scale of the problem, the existing method is easy to fall into a local optimal solution, and the solution time is too long. To overcome these problems, in this paper, we propose an Enhanced Adaptive Large Neighborhood Search algorithm (EALNS). The EALNS adds a new type of linear removal strategy and selects several adjacent nodes on a route to be removed so that the vehicle can serve more customers. In the ALNS decision-making stage, an adaptive mechanism that weighs the time factor is added, so that each strategy combination can adjust the weight according to the solved time. Experiments are performed through three internationally published benchmarks. Experimental results show that the EALNS is competitive and can obtain satisfactory results in most instances. We compare with the optimal results from the collective best results reported in the literature, EALNS improves 2.30% average accuracy and significantly reduces the average solution time.