Two evolutionary approaches with objective-specific variation operators for vehicle routing problem with time windows and quality of service objectives

This paper addresses a variant of the vehicle routing problem with time windows where the goal is to maximize the quality of service delivered to the customer. In the literature, this problem contains three objectives targeted at improving the quality of service. In this paper, we have proposed two evolutionary approaches, viz., a steady-state grouping genetic algorithm and a discrete differential evolution algorithm, to address this problem. The crossover and mutation operators are designed by considering the characteristics of each objective. The proposed approaches are incorporated with various heuristics that provide a set of better initial solutions in comparison to purely random initial solutions. We have also proposed two bounds for each objective. The approaches presented in this paper are tested on the Solomon instances which are considered as the standard benchmark instances for the vehicle routing problem with time windows in the literature. The proposed approaches are compared with the state-of-the-art approach available in the literature. The computational results demonstrate that our approaches are better in terms of solution quality and execution time than the state-of-the-art approach.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper addresses a variant of the vehicle routing problem with time windows and proposes two evolutionary approaches to maximize the quality of service delivered to the customer. The proposed approaches incorporate various heuristics and provide better initial solutions compared to random solutions. Experimental results show that the proposed approaches outperform the state-of-the-art approach in terms of solution quality and execution time. (c) 2022 Elsevier B.V. All rights reserved.