基于仿生算法的多式联运路径规划方法综述  

Bio-inspired optimization-based path planning algorithms in multimodal transportation:A survey

作　　者：孙哲[1] 马胜男 解相朋 孙知信[1] SUN Zhe;MA Sheng-nan;XIE Xiang-peng;SUN Zhi-xin(School of Modern Post,Nanjing University of Posts And Telecommunications,Nanjing 210003,China;School of Internet of Things,Nanjing University of Posts And Telecommunications,Nanjing 210003,China)

机构地区：[1]南京邮电大学现代邮政学院,南京210003 [2]南京邮电大学物联网学院,南京210003

出　　处：《控制与决策》2025年第2期375-386,共12页Control and Decision

基　　金：国家自然科学基金青年科学基金项目(62303214)。

摘　　要：多式联运可有效提高物流企业的运作效率并降低经营成本,是现代物流的未来发展趋势之一.然而,其路径规划问题常常存在许多非线性约束,传统的精确算法在求解时也面临着模糊性、特殊性、动态性、高维性等挑战.鉴于仿生算法模拟生物系统时的智能优势在解决这一类复杂组合优化问题时具备广泛性和高效性,研究近年来基于多式联运路径规划的仿生算法,并将其分为3类:群智能算法、进化算法和基于物理的仿生算法,分别罗列了多式联运路径规划问题涉及到的特殊背景、关键特征和未来研究方向,广泛对比、分析了该问题下各仿生算法的原理、改进、优点和局限性,并为不同问题下的特殊场景提供了合适的仿生算法.最后,讨论了多式联运路径规划问题目前面临的挑战和未来的研究方向.Multimodal transportation can effectively improve the operation efficiency of logistics enterprises and reduce the operating costs,which is one of the future development trends of modern logistics.However,there are many nonlinear constraints on the path planning problems,and the traditional accurate algorithm also faces challenges such as ambiguity,particularity,dynamism and high dimensionality.Given the intelligent advantages of bio-inspired algorithms in simulating biological systems,they are extensive and highly efficient in solving this kind of complex combinatorial optimization problems.This paper studies the bio-inspired algorithms based on multimodal transportation path planning in recent years,and divides it into three categories:swarm intelligent algorithms,evolution algorithms and the physics-based bio-inspired algorithms,summarizes the special background,key features and future research direction,widely compares,analyzes the principle,improvement,advantages and limitations,and provides the appropriate bio-inspired algorithms for the special scenarios.Finally,the challenges and future research trends are discussed.

关 键 词：多式联运 路径规划 仿生算法 不确定性 绿色运输 可持续性 

分 类 号：U15[交通运输工程] TP18[自动化与计算机技术—控制理论与控制工程]