不确定时间下多式联运多目标路径优化模型与算法

doi:10.16097/j.cnki.1009-6744.2024.06.017

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (6): 193-205.DOI: 10.16097/j.cnki.1009-6744.2024.06.017

不确定时间下多式联运多目标路径优化模型与算法

周金龙¹，张英贵^*1，肖杨¹，王娟²

1. 中南大学，交通运输工程学院，长沙410075；2.中南林业科技大学，物流学院，长沙410004

收稿日期:2024-07-17 修回日期:2024-08-31 接受日期:2024-09-03 出版日期:2024-12-25 发布日期:2024-12-18
作者简介:周金龙(1995- )，男，湖南郴州人，博士生。
基金资助:
国家自然科学基金(71971220，71771218)。

Multi-objective Routing Optimization Model and Algorithm for Multimodal Transportation with Uncertain Time

ZHOUJinlong¹,ZHANGYinggui^*1,XIAOYang¹,WANG Juan²

1. School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China; 2. School of Logistics, Central South University of Forestry and Technology, Changsha 410004, China

Received:2024-07-17 Revised:2024-08-31 Accepted:2024-09-03 Online:2024-12-25 Published:2024-12-18
Supported by:
NationalNaturalScienceFoundation of China (71971220，71771218)。

摘要/Abstract

摘要： 多式联运能充分发挥各运输方式的优势，助力实现货运降本增效，其中，联运路径决策是关键。联运组织过程和外界环境变化均可导致运输时间发生波动，本文考虑多式联运过程中路段运输时间和节点转运时间随机性对路径优化的影响，引入梯形模糊数刻画时间不确定性，以最小化运输成本、碳排放量和运输时间为目标，构建带时间窗约束的多式联运路径优化模型，并基于模糊机会约束规划理论，将不确定性模型转化为较易求解的混合整数规划模型；结合种群实时进化状态，将进化过程划分为两个阶段，第1阶段旨在优化目标函数，第2阶段寻求目标优化与约束条件之间的平衡，在此基础上，设计一种多阶段多目标进化算法求解模型；最后，以某多式联运网络为背景开展算例分析。计算结果表明：所提方法能合理编制面向不确定时间的多式联运路径优化方案集，其机会约束满足概率均超过90%；且与当前最先进的约束多目标进化算法相比，其超体积指标值提升了2.11%~41.95%；所提方法的性能较为显著，能够为多式联运经营主体提供有效的路径决策支持。

关键词: 综合运输, 多目标路径优化, 进化算法, 多式联运, 不确定时间, 碳排放

Abstract: Multimodal transportation leverages the advantages of various transport modes, contributing to cost reduction and efficiency improvements in freight logistics, with route decision-making being a critical factor. The organization of multimodal operations and external environmental changes can cause fluctuations in transportation times. This study considers the impact of stochastic transportation times and transfer times on route optimization in multimodal transportation by introducing trapezoidal fuzzy numbers to represent time uncertainty. A time-window constrained multimodal transportation route optimization model is constructed with the objectives of minimizing transportation costs, carbon emissions, and transportation time. Based on fuzzy chance-constrained programming theory, the uncertainty model is transformed into a more tractable mixed-integer programming model. The evolutionary process is divided into two stages based on the real-time state of the population: the first stage focuses on optimizing the objective function, while the second stage objective optimization with constraint satisfaction. On this basis, a multi stage multi-objective evolutionary algorithm is designed to solve the model. Finally, a case study of a multimodal transportation network demonstrates that the proposed method effectively generates a set of route optimization solutions under uncertain transportation times, with chance constraint satisfaction probabilities exceeding 90% . Compared to the state-of-the-art constrained multi-objective evolutionary algorithms, the hypervolume indicator improves by 2.11% to 41.95%, showing significant performance gains and providing effective route decision-making support for multimodal transportation operators.

Key words: integrated transportation, multi-objective route optimization, evolutionary algorithms, multimodal transportation, uncertain times, carbon emissions

中图分类号:

周金龙, 张英贵, 肖杨, 王娟. 不确定时间下多式联运多目标路径优化模型与算法[J]. 交通运输系统工程与信息, 2024, 24(6): 193-205.

ZHOUJinlong, ZHANGYinggui, XIAOYang, WANG Juan. Multi-objective Routing Optimization Model and Algorithm for Multimodal Transportation with Uncertain Time[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(6): 193-205.

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不确定时间下多式联运多目标路径优化模型与算法

Multi-objective Routing Optimization Model and Algorithm for Multimodal Transportation with Uncertain Time

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