Vehicle routing optimization based on heuristic algorithm

With expanding market scale and intensified competition, logistics companies must seek the optimal service distribution sequence. This paper aims to assist logistics enterprises in conducting reasonable vehicle routing planning, quickly completing distribution services, and enhancing enterprise competitiveness. Here, the Vehicle Routing Problem with Capacity constraints (CVRP) and the Vehicle Routing Problem with Multi-Objective Time Windows (MOVRPTW) are investigated. Firstly, for the CVRP problem, a mathematical profit maximization model is formulated and the Simulated Annealing algorithm (SA) is utilized to solve it. Secondly, focusing on the MOVRPTW problem, a multi-objective optimization model with maximum profit and highest customer satisfaction is established. The penalty function and customer satisfaction function are introduced, and the Pareto frontier set is obtained using the multi-objective genetic algorithm (NSGA-II). Results indicate that in the case of using Solomon's R112 example, SA and NSGA-II can find a satisfactory local optimal solution. The experimental results of this paper can help decision makers choose the most suitable vehicle distribution route for the enterprise based on their decision preferences and priorities.