Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demand

• Published in: Complex & intelligent systems Vol. 11; no. 2; pp. 136 - 22
• Main Authors: Cui, Tianxu, Shi, Ying, Wang, Jingkun, Ding, Rijia, Li, Jinze, Li, Kai
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2025; Springer Nature B.V; Springer
• Subjects: Algorithms; Carbon; Carbon emissions; Complexity; Computational Intelligence; Data Structures and Information Theory; Economic impact; Emissions; Engineering; Genetic algorithms; Green logistics; Many-objective; Multimodal transportation; Multimodal transportation systems; Multiple objective analysis; Operating costs; Optimization; Optimization algorithm; Optimization algorithms; Original Article; Route optimization; Route planning; Sorting algorithms; Optimization algorithm; Multimodal transportation; Many-objective; Green logistics; Carbon emissions
• ISSN: 2199-4536; 2198-6053; 2198-6053
• DOI: 10.1007/s40747-024-01725-4

In recent decades, multimodal transportation has played a crucial role in modern logistics and transportation systems because of its high capacity and low cost. However, multimodal transportation driven mainly by fossil fuels may result in significant carbon emissions. In addition, transportation costs, transportation efficiency, and customer demand are also key factors that constrain the development of multimodal transportation. In this paper, we develop, for the first time, a many-objective multimodal transportation route optimization (MTRO) model that simultaneously considers economic cost, carbon emission cost, time cost, and customer satisfaction, and we solve it via the nondominated sorting genetic algorithm version III (NSGA-III). Second, to further improve the convergence performance, we introduce a fuzzy decision variable framework to improve the NSGA-III algorithm. This framework can reduce the search range of the optimization algorithm in the decision space and make it converge better. Finally, we conduct numerous simulation experiments on test problems to verify the applicability and superiority of the improved algorithm and apply it to MTRO problems under uncertain demand. This work fills the research gap for MTRO problems and provides guidance for relevant departments in developing transportation and decarbonization plans.