Abstract
This paper addresses the two-dimensional loading open vehicle routing problem with time window (2L-OVRPTW). We propose a learning whale optimization algorithm (LWOA) to minimize the total distance; an improved skyline filling algorithm (ISFA) is designed to solve the two-dimensional loading problem. In LWOA, the whale optimization algorithm is used to search the solution space and get the high-quality solution. Then, by learning and accumulating the block structure and customer location information in the high-quality solution individuals, a three-dimensional matrix is designed to guide the updating of the population. Finally, according to the problem characteristics, the local search method based on fleet and vehicle is designed and performed on the high-quality solution region. IFSA is used to optimize the optimal individual. The computational results show that the proposed algorithm can effectively solve 2L-OVRPTW.
Highlights
Vehicle routing problem (VRP), as a classical combinatorial optimization problem, was first proposed by Dantzig and Ramser [1]
Open vehicle routing problem (OVRP) is a variant of VRP, which widely exists in the third-party logistics distribution. is type of problem is generating a set of vehicle routes that meet vehicle load conditions, compartment volume, and customer service demand. e vehicles do not need to return to the central depot
2L-OVRPTW comprises a vehicle routing problem and a two-dimensional loading problem, so 2L-OVRPTW is an NP-hard problem with high complexity
Summary
Vehicle routing problem (VRP), as a classical combinatorial optimization problem, was first proposed by Dantzig and Ramser [1]. Zhang et al [14] took the total cost minimization as the optimization objective, studied the heterogeneous vehicle routing problem with loading constraints, designed a hybrid particle swarm optimization algorithm combining artificial bee colony and artificial immune to solve the problem, and proposed several strategies to avoid the algorithm falling into local optimum. Dominguez et al [16] proposed a hybrid algorithm based on neighborhood search and loading heuristic algorithm to solve the two-dimensional vehicle routing problem with cluster backhaul and used randomization technology to guide the search process. E intelligent optimization algorithm only needs to spend a short time searching part of the solution space of the scheduling model to find a satisfactory solution so that it can effectively solve all kinds of complex scheduling problems.
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