Multi-constraint distributed terminal distribution path planning for fresh agricultural products

A common combinatorial optimization issue in actual engineering is the vehicle routing problem (VRP). Examples of these problems include logistics distribution, solid waste recycling planning, and underwater routing planning. The optimization algorithms are important for the solution quality of the proposed VRP. As the scale of the vehicle routing problem increases, the problem becomes more difficult. It is hard for the traditional algorithm to obtain the optimal solution to the problem in an acceptable computing time. In this paper, an adaptive large neighborhood water wave optimization (ALNSWWO) algorithm is designed to solve multi-depot capacitated vehicle routing problems with time windows (MDCVRPTW). Aimed at addressing the main problems of the original algorithm, an improvement strategy is designed. In the breaking operation, variable neighborhood search (VNS) and large neighborhood search (LNS) local search strategies are added. In the refinement operation, the learning operator based on the genetic algorithm and the adaptive large neighborhood search (ALNS) search mechanism is added. The above mechanism solves the problems that the original algorithm is prone to falling into local optima. The experimental results demonstrate that the distribution path scheme of fresh agricultural products (FAP) can be optimized through the ALNSWWO. The proposed ALNSWWO can reduce the distribution distance, time, cost, carbon emissions, and improve customer satisfaction.