Multi-Dimensional Ant Colony Algorithm for Multi-Compartment Vehicle Routing Problem with Fuzzy Demand

To deal with the multi-compartment vehicle routing problem with fuzzy demand (MCVRPFD), a mathematical model is established with the goal of minimizing transportation costs. Based on the fuzzy characteristics of the problem, the idea of pre-optimization followed by reoptimization is adopted. A multi-dimensional ant colony optimization algorithm with a rescheduling strategy (MDACO_RS) is proposed to solve this problem. Firstly, in the pre-optimization stage, the multi-dimensional ant colony optimization algorithm (MDACO) is used to obtain the preoptimized transportation scheme: In order to enhance the global search capability of MDACO, a multi-dimensional pheromone matrix is designed to record the sequence information of highquality individuals and the positional information of elements within those individuals, helping the algorithm quickly identify high-quality areas in the solution space; in order to enhance the local search capability of MDACO, multiple variable neighborhood operations are designed to perform an in-depth search of high-quality areas in the solution space. Then, in the re-optimization phase, a rescheduling strategy (RS) is proposed to adjust the pre-optimization scheme, which can effectively improve transportation efficiency and vehicle utilization while reducing additional transportation costs. Finally, the effectiveness of MDACO_RS in solving the MCVRPFD is verified through simulation experiments and algorithm comparisons.