NSGA-II with objective-specific variation operators for multiobjective vehicle routing problem with time windows

Vehicle routing problem with time windows (VRPTW) is a pivotal problem in logistics domain as it possesses multiobjective characteristics in real-world applications. Literature contains a general multiobjective VRPTW (MOVRPTW) with five objectives along with MOVRPTW benchmark instances that are derived from real-world data. In this paper, we have proposed a nondominated sorting genetic algorithm II (NSGA-II) based approach with objective-specific variation operators to address the MOVRPTW. In the proposed NSGA-II approach, the crossover and mutation operators are designed by exploiting the problem characteristics as well as the attributes of each objective. The performance of the proposed approach is evaluated on the standard benchmark instances of the problem and compared with the state-of-the-art approach available in literature. The computational results demonstrate the superiority of our approach over the state-of-the-art approach for the MOVRPTW.

Automated summary

This paper proposes a nondominated sorting genetic algorithm II approach to address the vehicle routing problem with time windows, known for its multiobjective characteristics. The performance of this approach is evaluated on standard benchmark instances, showing its superiority over the state-of-the-art approach for the problem.