针对客户时间窗变化而导致的物流配送难以顺利实施这一难题,运用干扰管理思想,以提高物流配送干扰管理决策过程的科学性为目标,结合行为科学中对人的行为感知的研究方法与运筹学中定量的研究手段,分析客户时间窗变化这类干扰事件对客户、物流配送运营商和配送业务员等受扰主体的影响,研究基于行为的扰动度量方法, 构建客户时间窗变化的字典序多目标干扰管理模型,并给出快速高效的求解方法。实例结果表明:本文方法比已有的全局重调度方法和局部重调度方法更实用——能够均衡各方的利益,获得扰动最小的调整方案。
It is difficult to generate the new plan effectively for minimizing the negative impact when the time window of customer changes in logistic distribution. Based on disruption management, this research aims to improve the science of the decision making of disruption management in logistic distribution by combining the behavioral perception in prospect theory with the quantitative analysis in operations research. At the beginning, the method to measure the deviation based on prospect theory is studied by analyzing the effects of the change of time window on customers, logistics providers and drivers. Then, the multi-objective model of disruption management is constructed and an improved ant colony optimization is demonstrated. The computational result of the model proves that, due to the tradeoff between all parties involved in logistic distribution, our approach is more practical than global rescheduling and local rescheduling. This research contributes to the theory and method of disruption management, which can be used in other fields, such as production planning problems, flight scheduling, supply chain management, etc.
[1] 杨文超, 胡祥培, 王征. 顾客时间窗变化的物流配送问题干扰管理方法研究[J]. 大连理工大学学报, 2012, 52(2): 290-296
[2] Yu Gang, Qi Xiantong. Disruption management: Framework, models and applications [M].Singapore: World Scientific Publishing, 2004.
[3] Clausen J, Larsen A, Larsen J, et al.. Disruption management in the airline industry-concepts, models and methods[J]. Computers & Operations Research, 2010, 37(5): 809-821.
[4] Jiang Yang, Sun Wei, Ding Qiulei, et al. A model of disruption management for solving machine disruption in single machine scheduling[J]. ICIC Express Letter, 2012, 6(12): 3143-3148.
[5] Qi Xiangtong, Bard J F, Yu Gang. Supply chain coordination with demand disruptions[J].Omega, 2004, 32(4): 301-312.
[6] Zhu Guidong, Bard J F, Yu Gang. Disruption management for resource-constrained project scheduling[J]. Journal of the Operational Research Society, 2005, 56(4): 365-381.
[7] Wang H F, Wen Yupin. Time-constrained Chinese postman problems[J]. Computers & Mathematics with Applications, 2002, 44(34): 375-387.
[8] 王明春, 高成修, 曾永廷. VRPTW的扰动恢复及其TABU SEARCH算法[J]. 数学杂志, 2006, 26(2): 231-236.
[9] 钟石泉, 杜纲, 贺国光. 有顾客时间窗和发货量变化的紧急车辆调度研究[J]. 管理工程学报, 2007, 21(4): 114-118.
[10] 王旭坪, 许传磊, 胡祥培. 有顾客时间窗和发货量变化的车辆调度干扰管理研究[J]. 管理科学, 2008, 21(5): 111-120.
[11] Hadjar A, Soumis F. Dynamic window reduction for the multiple depot vehicle scheduling problem with time windows[J]. Computers & Operations Research, 2009, 36(7): 2160-2172.
[12] 王征, 王建军, 杨文超. 顾客时间窗变化的多车场车辆调度干扰管理模型研究[J]. 管理科学, 2010, 23(3): 103-112.
[13] 王旭坪,阮俊虎,张凯,等. 有模糊时间窗的车辆调度组合干扰管理研究[J]. 管理科学学报, 2011, 14(6): 2-15.
[14] Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk[J]. Econometrica, 1979, 47(2): 263-291.
[15] Tversky A, Kahneman D. Advances in prospect theory: Cumulative representation of uncertainty[J]. Journal of Risk and uncertainty, 1992, 5(4): 297-323.
[16] Stützle T, Hoos H. Improvements on the ant system: Introducing MAX-MIN ant system [M]//Kurkova V,Steele N C,Neruda R,et al.Artificial Neural Networks and Genetic Algorithms, Vienna:Springer,1998.
[17] 王颖,谢剑英. 一种自适应蚁群算法及其仿真研究[J]. 系统仿真研究, 2002, 14(1): 31-33.
[18] Solomon M M. Algorithms for the vehicle routing and scheduling problems with time window constraints[J]. Operations research, 1987, 35(2): 254-265.
[19] Chen C H, Ting C J. A hybird ant colony system for vehicle routing problem with time windows[J]. Journal of the Eastern Asia Society for Transportation Studies, 2005, 6: 2822-2836.
[20] Balseiro S R, Loiseau I, Ramonet J. An ant colony algorithm hybridized with insertion heuristics for the time dependent vehicle routing problem with time windows[J]. Computers & Operations Research, 2011,38(6): 954-966.
[21] Thangiah S R, Osman I H, Sun Tong. Hybrid genetic algorithm, simulated annealing and tabu search methods for vehicle routing problems with time windows[R]. Technical Report, Institute of Mathematics & Statistics, University of Kent, Canterbury, UK, 1994.
[22] Tan K C, Lee L H, Ou K. Artificial intelligence heuristics in solving vehicle routing problems with time window constraints. Engineering Applications of Artificial intelligence, 2001, 14(6): 825-837.
