Abstract

Introduction: The research focuses on cost optimization in the traditional vehicle routing optimization problem. However, in the actual optimization scenarios, such as port industrial parks, decision-makers often focus on service time and customer demands under uncertain environments.

Methods: Meanwhile, increasing environmental sustainability and effective resource utilization further drive the focus on eco-friendly transportation. In this context, this study refines a hazmat vehicle routing problem with fuzzy time windows under fuzzy demands for the port industrial park. Moreover, to address the above problems, the main contributions of this study are as follows. Firstly, inspired by the sharing economy theory, a novel mode is proposed through intermediate bulk container sharing as a transportation medium in the port industrial park. Secondly, a biobjective fuzzy chance-constrained programming model is proposed for transportation cost and service satisfaction, considering fuzzy demands and fuzzy time windows.

Results: Thirdly, the non-dominated sorting genetic algorithm II is used to solve the proposed problem. Additionally, the effectiveness of the model is validated using the Solomon benchmark with different scales, where transportation costs are 11545, 28611, and 61192, and average satisfaction is 0.885, 0.880, and 0.928.

Conclusion: The results indicate that the algorithm reduces transportation costs and enhances service satisfaction. The study enriches the relevant research on vehicle routing problems and provides a theoretical basis for practical distribution strategies.

Keywords: Vehicle routing problem, intermediate bulk container sharing, fuzzy demands, fuzzy time windows, non-dominated sorting genetic algorithm II, port industrial park.