A Decomposition-Based Local Search for Large-Scale Many-Objective Vehicle Routing Problems With Simultaneous Delivery and Pickup and Time Windows

Abstract

Vehicle routing problem with simultaneous delivery and pickup and time windows (VRPSDPTW) is an important logistics problem in supply chain network optimization. However, at present, existing approaches are only applied to solve small-scale many-objective VRPSDPTW. For the large-scale problem, to obtain high-quality solutions (effectively) and solve the problem in a short running time (efficiently), we focus on two issues: 1) how to handle many-objective property and 2) how to tackle large-scale property of the problem. For the first issue, to enhance the convergence property, a novel local search algorithm utilizing seven neighborhood operators is proposed. Meanwhile, to maintain the diversity property, the concept of decomposition is adopted. The problem is first decomposed into multiple single-objective problems, then local search is utilized to optimize these subproblems. For the second issue, since optimizing a subproblem by local search is time consuming for large-scale problems, the key point is how to allocate the computational resources and balance the convergence and diversity. To handle this problem, a weight space partition strategy is introduced. Finally, a decomposition-based local search algorithm is proposed for many-objective VRPSDPTW. Experimental results on the large-scale instances provided by Chinese logistic company show the effectiveness of the proposed algorithm.