前景理论下的末端物流干扰管理方法研究
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摘要
针对末端物流配送过程中可能出现的配送时间窗更改、送货地址变化等动态扰动问题,提出了基于前景理论的价值函数度量策略和基于量子理论的多目标优化求解方法.首先,对末端物流干扰管理问题进行了描述;然后,为度量用户对货物期待的心理感知度,建立了末端物流配送干扰管理模型,并设计了基于前景理论的价值函数曲线和扰动度量策略;在此基础上,为优化干扰管理模型和寻求用户满意度、配送成本以及配送效率等多目标优化问题的较优解,提出了一种融入量子理论的改进细菌觅食方法;最后,将所提出的方法与已经存在的经典算法进行比较,验证了该方法的可行性和有效性.
In view of the dynamic disturbance problem in terminal logistics distribution such as delivery time window change, delivery address change, etc., a value function measurement strategy based on prospect theory and multi-objective solving method based on quantum theory are proposed. Firstly, the concept of terminal logistics interference management problem is described. Then, in order to measure the psychological perception of user's expectations, a model of the terminal logistics interference management is established, moreover, the value function curve based on prospect theory and the strategy of disturbance measure are designed. In order to optimize the interference management model and the optimal solution of multi-objective optimization problem which include user satisfaction, delivery costs and delivery efficiency,etc., a kind of improved bacterial foraging optimization algorithm with quantum theory is proposed. Finally, the proposed method and the existing classic algorithms are compared to verify the feasibility and effectiveness of the proposed method.
In view of the dynamic disturbance problem in terminal logistics distribution such as delivery time window change, delivery address change, etc., a value function measurement strategy based on prospect theory and multi-objective solving method based on quantum theory are proposed. Firstly, the concept of terminal logistics interference management problem is described. Then, in order to measure the psychological perception of user's expectations, a model of the terminal logistics interference management is established, moreover, the value function curve based on prospect theory and the strategy of disturbance measure are designed. In order to optimize the interference management model and the optimal solution of multi-objective optimization problem which include user satisfaction, delivery costs and delivery efficiency,etc., a kind of improved bacterial foraging optimization algorithm with quantum theory is proposed. Finally, the proposed method and the existing classic algorithms are compared to verify the feasibility and effectiveness of the proposed method.
引文
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[16] Chatzis S P, Koukas S. Numerical optimization using synergetic swarms of foraging bacterial populations[J].Expert Systems with Applications, 2011, 38(12):15332-15343.
[17] Xu J J, Hong L. Impact factors of choosing willingness for picking up service[J]. Engineering and Technology,2013, 6(14):2509-2513.
[18] Ning T, Jin H, Song X D, et al. An improved quantum genetic algorithm based on MAGTD for dynamic FJSP[J].Journal of Ambient Intelligence and Humanized Computing, 2018, 9:931-940.
[19] Wang W. Research on logistics delivery networks of online retailers[J]. Journal of System and Management Science, 2013, 3(2):51-59.
[20] Solomon M. Solomon benchmark problems[EB/OL].[2009-2-28]. http://www.idsia.ch/luca/macs-vrptw/problems/welcome.htm.
[21]宁涛,郭晨,陈荣,等.一种动态车辆路径问题解决策略仿真研究[J].系统仿真学报,2015, 27(12):2942-2947.Ning T, Guo C, Chen R, et al. Simulation study on scheduling strategy of dynamic vehicle routing problem[J].Journal of System Simulation, 2015, 27(12):2942-2947.
[22] Do J Y, Kim D K. AHP-based evaluation model for optimal selection process of patching materials for concrete repair:Focused on quantitative requirements[J]. International Journal of Concrete Structures and Materials,2012, 6(2):87-100.
[23] Sheng X Y, Xi M L, Sun J, et al. Quantum-behaved particle swarm optimization with novel adaptive strategies[J].Journal of Algorithm and Computational Technology, 2015, 9(2):143-162.
[2] Al-Nawayseh M K, Alnabhan M M, Al-Debei M M, et al. An adaptive decision support system for last mile logistics in e-commerce:A study on online grocery shopping[J]. International Journal of Decision Support System Technology, 2013, 5(1):40-65.
[3] Guiffrida A L. Carbon emissions comparison of last mile delivery versus customer pickup[J]. International Journal of Logistics, 2014, 17(6):20-29.
[4] Ehmke J, Campbell A. Customer acceptance mechanisms for home deliveries in metropolitan areas[J]. European Journal of Operational Research, 2014, 233:193-207.
[5] Gevaers R, Voorde E V, de Vanelslander T. Cost modeling and simulation of last-mile characteristics in an innovative B2C supply chain environment with implications on urban areas and cities[J]. Procedia Social andBehavioral Sciences, 2014, 125(20):398-411.
[6] Yap M D, Correia G, van Arem B. Preferences of travelers for using automated vehicles as last mile public transport of multimodal train trips[J]. Transportation Research Part A, 2016, 94:1-16.
[7]丁秋雷,胡祥培,姜洋.基于前景理论的物流配送干扰管理模型研究[J].管理科学学报,2014,17(11):1-9.Ding Q L, Hu X P, Jiang Y. Study on logistice distribution interference management model based on prospect theory[J]. Journal of Management Sciences in China, 2014, 17(11):1-9.
[8]蒋丽,丁斌,臧晓宁.基于干扰管理的车辆故障救援模型[J].系统工程,2010, 28(6):111-116.Jiang L, Ding B, Zang X N. Vehicle fault rescue model based on interference management[J]. Systems Engineering,2010, 28(6):111-116.
[9]樊治平,刘洋,沈荣鉴.基于前景理论的突发事件应急响应的风险决策方法[J].系统工程理论与实践,2012, 32(5):977-984.Fan Z P, Liu Y, Chen R J. Risk decision analysis method for emergency response based on prospect theory[J].Systems Engineering—Theory&Practice, 2012, 32(5):977-984.
[10]宁涛,王旭坪,胡祥培,等.基于前景理论的物流配送干扰管理优化调度方法[J].控制与决策,2018, 33(11):2064-2068.Ning T, Wang X P, Hu X P, et al. Disruption management optimal scheduling for logistics distribution based on prospect theory[J]. Control and Decision, 2018, 33(11):2064-2068.
[11]胡祥培,孙玉姣,曾庆成.集装箱码头同贝同步装卸作业的序列优化模型[J].系统工程理论与实践,2016, 36(3):81-92.Hu X P, Sun Y J, Zeng Q C. A sequence optimization model of dual-cycling operations in container terminals[J].Systems Engineering—Theory&Practice, 2016, 36(3):81-92.都牧,胡祥培,周宽久,等.基于物联网的蔬果网上直销'农-宅'配送系统[J].系统工程学报,2014, 29(2):215-222.Du M, Hu X P, Zhou K J, et al. IoT-based farm-to-door delivery system for fruit and vegetable online retail[J].Journal of Systems Engineering, 2014, 29(2):215-222.
[12]王征,胡祥培,王旭坪.行驶时间延迟下配送车辆调度的干扰管理模型与算法[J].系统工程理论与实践,2013, 33(2):378-387.Wang Z, Hu X P, Wang X P. Disruption management model and algorithm for distribution vehicle scheduling problems under accidental travel time delay[J]. Systems Engineering—Theory&Practice, 2013, 33(2):378-387.
[13] Berechman J. Transportation-economic aspects of Roman highway development:The case of Via Appia[J].Transportation Research Part A:Policy and Practice, 2003, 37(5):453-478.
[14] Lee H, Yang H M. Strategies for a global logistics and economic hub:Incheon International Airport[J]. Journal of Air Transport Management, 2003, 9(2):113-121.
[15] Ning T, Huang M, Liang X, et al. A novel dynamic scheduling strategy for solving flexible job-shop problems[J].Journal of Ambient Intelligence and Humanized Computing, 2016, 7(5):1-9.
[16] Chatzis S P, Koukas S. Numerical optimization using synergetic swarms of foraging bacterial populations[J].Expert Systems with Applications, 2011, 38(12):15332-15343.
[17] Xu J J, Hong L. Impact factors of choosing willingness for picking up service[J]. Engineering and Technology,2013, 6(14):2509-2513.
[18] Ning T, Jin H, Song X D, et al. An improved quantum genetic algorithm based on MAGTD for dynamic FJSP[J].Journal of Ambient Intelligence and Humanized Computing, 2018, 9:931-940.
[19] Wang W. Research on logistics delivery networks of online retailers[J]. Journal of System and Management Science, 2013, 3(2):51-59.
[20] Solomon M. Solomon benchmark problems[EB/OL].[2009-2-28]. http://www.idsia.ch/luca/macs-vrptw/problems/welcome.htm.
[21]宁涛,郭晨,陈荣,等.一种动态车辆路径问题解决策略仿真研究[J].系统仿真学报,2015, 27(12):2942-2947.Ning T, Guo C, Chen R, et al. Simulation study on scheduling strategy of dynamic vehicle routing problem[J].Journal of System Simulation, 2015, 27(12):2942-2947.
[22] Do J Y, Kim D K. AHP-based evaluation model for optimal selection process of patching materials for concrete repair:Focused on quantitative requirements[J]. International Journal of Concrete Structures and Materials,2012, 6(2):87-100.
[23] Sheng X Y, Xi M L, Sun J, et al. Quantum-behaved particle swarm optimization with novel adaptive strategies[J].Journal of Algorithm and Computational Technology, 2015, 9(2):143-162.