A Hybrid Ant Colony Optimization Algorithm for Multi-Compartment Vehicle Routing Problem

The multi-compartment vehicle routing problem (MCVRP) has been applied in fuel or food delivery, waste collection, and livestock transportation. Ant colony optimization algorithm (ACO) has been recognized as an efficient method to solve the VRP and its variants. In this paper, an improved hybrid ant colony optimization algorithm (IHACO) is proposed to minimize the total mileage of the MCVRP. First, a probabilistic model is designed to guide the algorithm search towards high-quality regions or solutions by considering both similar blocks of customers and customer permutations. Then, a heuristic rule is presented to generate initial individuals to initialize the probabilistic model, which can drive the search to the high-quality regions faster. Moreover, a new local search using the geometry optimization is developed to execute exploitation from the promising regions. Finally, two types of variable neighborhood descent (VND) techniques based on the speed-up search strategy and the first move strategy are devised to further enhance the local exploitation ability. Comparative numerical experiments with other algorithms and statistical analyses are carried out, and the results show that IHACO can achieve better solutions.