一种求解带约束多式联运问题的群智能算法

摘要/Abstract

摘要：

摘要：针对如何有效运用群智能算法求解多式联运问题，设计了一种针对群智能优化算法的个体解码方式，提出了一个有效的个体编码与多式联运方案的映射模型. 在该映射模型中设计了基于比例的流量分配方式，实现了个体编码信息向初步流量分配方式的解码；同时构建了局部流量调整策略，进行不可行方案修复，提高了解码方案的有效性. 而后，提出了一种变邻域粒子群算法，将社会网络演化特征引入进行粒子群算法的种群拓扑和邻域调整，以改善个体在搜索过程中的交互模式. 基于解码策略，采用改进算法对多式联运问题进行求解，并与3种新型群智能算法进行对比. 通过实例分析，该编码策略可以有效应用于多式联运问题求解. 同时，变邻域粒子群优化算法的收敛效率和性能优于对比算法.

关键词: 解码策略, 群智能, 粒子群, 多式联运

Abstract:

Abstract： In order to apply the swarm intelligence (SI) algorithms effectively to solve the multimodal transportation planning problem, this paper proposed a decoding strategy to build a mapping modal between individual representation of SI and multimodal transportation schedule. In the modal, a method for traffic assignment in a multimodal transportation network was provided to decode each individual to an initial schedule. Then, a strategy for local traffic adjustment was applied to mend these initial schedules to improve the success rate of decoding. A developed particle swarm optimization (PSO) algorithm was also proposed to solve the planning problem compared with three other stateofart swarm intelligence optimization algorithms. A novel way that applies social network evolution behavior to adjust the swarm topology and individuals’ neighborhood and promote the interaction modal among individuals was introduced in the proposed algorithm. The numerical test of an operational problem shows that the decoding strategy is efficient in solving the multimodal transportation planning problem and the proposed algorithm has a superior performance on the terms of convergence speed and solution accuracy in comparison with the selected algorithms.

Key words: decoding strategy, swarm intelligence, particle swarm optimization, multimodal transportation

中图分类号:

U 169.62

梁晓磊，李文锋，张煜. 一种求解带约束多式联运问题的群智能算法[J]. 上海交通大学学报（自然版）, 2015, 49(08): 1220-1229.

LIANG Xiaolei，LI Wenfeng，ZAHNG Yu. A Novel Swarm Intelligence Optimization Algorithm for Solving Constrained Multimodal Transportation Planning[J]. Journal of Shanghai Jiaotong University, 2015, 49(08): 1220-1229.

参考文献

［1］Bravo J J, Vidal C J. Freight transportation function in supply chain optimization models: A critical review of recent trends［J］. Expert Systems with Applications, 2013, 40(17)：67426757.

［2］Steadieseifi M, Dellaert N P, Nuijten W, et al. Multimodal freight transportation planning: A literature review［J］. European Journal of Operational Research，2014，233(1)：115.

［3］熊桂武，王勇.带时间窗的多式联运作业整合优化算法［J］. 系统工程学报，2011，26(3)：379386.

XIONG Guiwu, WANG Yong. Optimization algorithm of multimodal transportation with time window and job integration of multiagent［J］. Journal of System Engineering，2011，26(3)：379386.

［4］Zhang Mo, Bart Wiegmans, Lori Tavasszy. Optimization of multimodal networks including environmental costs: A model and findings for transport policy ［J］. Computers in Industry，2013，64(2)：136145.

［5］Cho J, Kim H, Choi H. An intermodal transport network planning algorithm using dynamic programmingA case study: from Busan to Rotterdam in intermodal freight routing［J］. Applied Intelligence，2012，36(3)：529541.

［6］魏航，李军，魏洁. 时变条件下多式联运有害物品的路径选择［J］.系统管理学报，2007，17(6)： 644652.

WEI Hang, LI Jun, WEI Jie. Routing for multimodal hazardous materials transportation in timevarying network［J］. Journal of Systems and Management，2007，17(6)：644652.

［7］Bai R, Kendall G, Qu R, et al. Tabu assisted guided local search approaches for freight service network design［J］. Information Sciences，2012，189(4)：266281.

［8］Van Der Weijde A H, Verhoef E T, Van Den Berg V a C. Competition in multimodal transport networks: A dynamic approach［J］. Transportation Research Part B: Methodological，2013，53(6)：3144.

［9］Liu Linzhong, Mu Haibo, Luo Haiyan, et al. A simulated annealing for multicriteria network path problems［J］. Computer & Operations Research，2012，39(12)：33193135.

［10］Zeng T, Hu D, Huang G. The transportation mode distribution of multimodal transportation in automotive logistics［C］∥COTA International Conference of Transportation Professionals. Shenzhen：Elsevier，2013：405417.

［11］Takeyasu K, Kainosho M. Optimization technique by genetic algorithms for international logistics［J］. Journal of Intelligent Manufacturing，2014，25(5)：10431049.

［12］Hu Z H. A container multimodal transportation scheduling approach based on immune affinity model for emergency relief［J］. Expert Systems with Applications，2011，38(3)：26322639.

［13］Barcos L, Rodriguez V, Alvarez M, et al. Routing design for lessthantruckload motor carriers using ant colony optimization［J］. Transportation Research Part E，2010，46(3)：367383.

［15］Hedi A, Habbas Z, Khadraoui D. ACO for solving a multimodal transport problems using a transfer graph model［C］∥International Conference on Computers & Industrial Engineering. Troyes：IEEE， 2009：285290.

［14］Ayed H, GalvezFernandez C, Habbas Z, et al. Solving timedependent multimodal transport problems using a transfer graph model［J］. Computers & Industrial Engineering，2011，61(2)：391401.

［16］Liang Xiaolei, Li Wenfeng, Zhang Yu, et al. An adaptive particle swarm optimization method based on clustering［J］. Soft Computing，2015，19(2)：431448.

［17］Kennedy J, Eberhart R. Particle swarm optimization［C］∥International Conference on Neural Networks. Perth：IEEE，1995：19421948.

［18］Simon D. BiogeographyBased optimization［J］. IEEE Transactions on Evolutionary Computation, 2008，12(6)：702713.

［19］Yang X S. Firefly algorithm, stochastic test functions and design optimisation［J］. International Journal of BioInspired Computation，2010，2(2)：7884.

［20］Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm［J］. Journal of Global Optimization，2007，39(3)：459471.

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