2025 年 16 巻 論文ID: PP4140
Logistics becomes more important to the global supply chain after COVID-19. To achieve net zero in 2050, companies realized that it is imperative to reduce the fuel consumption and carbon emissions. The traditional vehicle routing problem is to minimize total travel time or cost. Th fuel consumption and carbon emissions were not considered. As the fuel consumption or carbon emission is impacted by the truck load and their travel distance, the load dependent vehicle routing problem (LDVRP) becomes more important. The LDVRP with time windows (LDVRPTW) which is the topic of this research is related to Practical consideration. Due to the NP-hardness of the LDVRPTW, most researches developed metaheuristics to solve the problem. In this research, we propose a multi-start tabu search hybrid with variable neighborhood descent (VND) to improve the search space and computation efficiency. We test our algorithm with benchmark instance of LDVRP and Soloman VRPTW instances.
