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《山东大学学报(理学版)》 ›› 2025, Vol. 60 ›› Issue (6): 128-140.doi: 10.6040/j.issn.1671-9352.0.2024.067

• • 上一篇    

多重不确定环境下带有模糊软时间窗的多式联运路径优化与仿真

孙岩1,张正1,张夏然2*,刘耘麟1,孙国华1   

  1. 1.山东财经大学管理科学与工程学院, 山东 济南 250014;2.山东中医药大学管理学院, 山东 济南 250355
  • 发布日期:2025-06-20
  • 通讯作者: 张夏然(1986— ),女,讲师,博士,研究方向为物流与供应链管理. E-mail:zhangxiaran@126.com
  • 作者简介:孙岩(1990— ),男,副教授,博士,研究方向为运输系统优化. E-mail:sunyanbjtu@163.com*通信作者:张夏然(1986— ),女,讲师,博士,研究方向为物流与供应链管理. E-mail:zhangxiaran@126.com
  • 基金资助:
    山东省自然科学基金面上项目(ZR2023MG020);山东省高等学校优秀青年创新团队(2022RW084)

Optimization and simulation for an intermodal routing problem with fuzzy soft time window under multiple uncertainty

SUN Yan1, ZHANG Zheng1, ZHANG Xiaran2*, LIU Yunlin1, SUN Guohua1   

  1. 1. School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, Shandong, China;
    2. School of Management, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong, China
  • Published:2025-06-20

摘要： 为了解决多式联运在长距离、大运量运输中存在运输费用高、运输时效低的问题,以运输费用最小化为目标,研究了带有模糊软时间窗的多式联运路径优化问题。同时,为了提升多式联运路径优化在实际运输中的可靠性,对客户货物需求量的不确定性进行了规划,进而研究了需求不确定性所导致包括运输费用与运输时间不确定性、服务水平约束与能力约束不确定性在内的多重不确定环境。在采用梯形模糊数刻画不确定性的基础上,构建多重不确定环境下多式联运路径优化的模糊规划模型,采用基于可信性测度的模糊机会约束规划对模糊规划模型进行清晰化处理使优化问题可解,并设计基于网络转换的蚁群算法对清晰化模型进行高效求解。算例结果验证了机会约束规划模型和蚁群算法的可行性,通过敏感性分析反映了提高服务水平和置信水平对多式联运运输费用的影响。算例仿真实验表明了置信水平与路径可靠性之间的关系,即路径可靠性随置信水平的提高而呈现提升的趋势,但是两者并非等价的,提高置信水平不会带来路径优化可靠性的必然提升。同时,算例仿真实验也验证了规划不确定性能够显著提高路径优化在实际运输中的可靠性,并进一步揭示了路径优化经济性目标与可靠性目标是矛盾对立的。客户和多式联运经营人可据此对运输经济性、时效性和可靠性进行折中处理,有效提升多式联运的综合水平。

关键词: 多式联运, 路径优化, 模糊软时间窗, 多重不确定环境, 模糊机会约束规划, 蚁群算法

Abstract: To solve the high cost and low efficiency of the intermodal transportation applied in long-distance and bulk transportation, this study explores an intermodal routing problem with fuzzy soft time window whose aim is to minimize the transportation costs. Meanwhile to improve the reliability of the intermodal routing in the actual transportation, this study formulates the uncertainty of the goods demand of the customer, and further explore the multiple uncertainty introduced by the uncertain demand that includes the uncertainty of transportation costs and time and of the service level constraints and capacity constraints. Based on the utilization of the trapezoidal fuzzy number to describe the uncertainty, this study establishes a fuzzy programming model to deal with the intermodal routing problem under multiple uncertainty, and utilizes the fuzzy chance-constrained programming method based on credibility measure to realize the crisp reformulation of the model to make the problem solvable. This study further develops an Ant Colony Optimization algorithm based on network transformation to solve the crisp model efficiently. The results of the numerical case demonstrate the feasibility of the chance-constrained programming model and Ant Colony Optimization algorithm. The influence of improving the service level and the confidence level on the costs of the intermodal transportation is clarified by using the sensitivity analysis. The experimental simulation of the numerical case further indicates the relationship between the confidence level and the reliability of the route that the reliability of the route trends to enhance with the improvement of the confidence level, however, they are not equivalent, and improving the confidence level will not lead to an absolute reliability enhancement. The numerical case simulation also verifies that considering demand uncertainty significantly improves the reliability of the routing in the actual transportation, and further reveals that the economy and reliability objectives of the routing are in conflict with each other. The customer and intermodal transportation operator can accordingly make tradeoffs among the economy, timeliness and reliability of the transportation to effectively improve the comprehensive level of the intermodal transportation.

Key words: intermodal transportation, routing, fuzzy soft time window, multiple uncertainty, fuzzy chance-constrained programming, Ant Colony Optimization algorithm

中图分类号: 

  • F542
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