快件配送地址变化的干扰管理模型研究

doi:10.12005/orms.2017.0285

运筹与管理 ›› 2017, Vol. 26 ›› Issue (12): 46-52.DOI: 10.12005/orms.2017.0285

快件配送地址变化的干扰管理模型研究

丁秋雷^1,2, 胡祥培³, 姜洋⁴, 阮俊虎^3,5

1.东北财经大学工商管理学院,辽宁大连 116025;
2.东北财经大学供应链分析研究院,辽宁大连 116025;
3.大连理工大学管理与经济学部,辽宁大连 116023;
4.大连工业大学轻工与化学工程学院,辽宁大连 116034;
5.西北农林科技大学经济管理学院,陕西杨凌 712100

收稿日期:2017-05-18 出版日期:2017-12-25
作者简介:丁秋雷(1980-),男,山东汶上人,副教授,博士,研究方向:干扰管理、物流管理和行为运作管理;胡祥培(1962-),男,安徽绩溪人,教授,博士,研究方向:智能运筹学、电子商务与物流管理。
基金资助:
国家自然科学基金资助项目(71272093,71301020,71531002);教育部人文社会科学研究青年基金(16YJC630102);中国博士后科学基金(2016M600209);东北财经大学校级科研项目(DUFE2017Y02)

Disruption Management Model for Location Change in Express Delivery

DING Qiu-lei^1,2, HU Xiang-pei³, JIANG Yang⁴, RUAN Junhu^3,5

1.School of Business Administration, Dongbei University of Finance and Economics, Dalian 116025, China;
2.Institute of Supply Chain Analytics, Dongbei University of Finance and Economics, Dalian 116025, China;
3.Faculty of Management and Economics, Dalian University of Technology, Dalian 116023, China;
4.School of Light Industry & Chemical Engineering, Dalian Polytechnic University, Dalian 116034, China;
5.College of Economics and Management, Northwest A&F University, Yangling 712100, China

Received:2017-05-18 Online:2017-12-25

摘要/Abstract

摘要： 快件配送过程中客户的配送地址发生变化后,继续按初始方案进行配送将不再最优甚至不可行,此时如何快速产生应对扰动的新方案,是当前研究的难点。结合客户消费行为的相关研究手段,将客户分为敏感型客户、容忍型客户和抱怨型客户,在考虑每类客户特点的基础上,将问题的处理分为两个阶段;采用干扰管理和运筹学的相关理论与方法,在每一阶段构建干扰管理子模型并进行求解。算例实验表明:在考虑客户行为及偏好的情况下,与其他重调度方法相比,本文方法生成的应对方案更为有效。

关键词: 快件配送, 干扰管理, 地址变化, 蚁群算法

Abstract: It is difficult to generate a new plan when the location changes in express delivery. In such cases, continuing with the original plan may not be optimal or feasible. First of all, based on the approach of consumption behavior, the customers are divided into sensitive customers, tolerant customers, and complaining customers. Secondly, the problem of location change is divided into two stages by taking the customer characteristics into consideration. Then, the sub-model of disruption management is formed at each stage and the solution approach is demonstrated by using the method of disruption management and operations research. Finally, the computational result proves that our approach is more effective than existing rescheduling.

Key words: express delivery, disruption management, location change, ant colony optimization

中图分类号:

C93
TP18

丁秋雷, 胡祥培, 姜洋, 阮俊虎. 快件配送地址变化的干扰管理模型研究[J]. 运筹与管理, 2017, 26(12): 46-52.

DING Qiu-lei, HU Xiang-pei, JIANG Yang, RUAN Junhu. Disruption Management Model for Location Change in Express Delivery[J]. Operations Research and Management Science, 2017, 26(12): 46-52.

参考文献

[1] Yu G, Qi X. Disruption management: framework, models and applications[M]. Singapore: World Scientific Publishing, 2004. 16-17.
[2] Mcalister J. The disruption management model[J]. Journal of Business Continuity & Emergency Planning, 2011, 5(3): 231.
[3] 王旭坪,杨德礼,许传磊.有顾客需求变动的车辆调度干扰管理研究[J].运筹与管理,2009,18(4):16-24.
[4] Mu Q, Eglese R. Disrupted capacitated vehicle routing problem with order release delay[J]. Annals of Operations Research, 2011, 207(1): 1-16.
[5] Yoo T. Disruption management framework in a global logistics[J]. Lecture Notes in Electrical Engineering, 2012, 182: 153-158.
[6] Wang X, Ruan J, Shi Y. A recovery model for combinational disruptions in logistics delivery: considering the real-world participators[J]. International Journal of Production Economics, 2012, 140(1): 508-520.
[7] Liu W, Liu Y, Zhu D, et al. The influences of demand disruption on logistics service supply chain coordination: a comparison of three coordination modes[J]. International Journal of Production Economics, 2016, 179: 59-76.
[8] Mohammadi M, Tavakkoli-Moghaddam R, Siadat A, et al. Design of a reliable logistics network with hub disruption under uncertainty[J]. Applied Mathematical Modelling, 2016, 40: 5621-5642.
[9] 丁秋雷,胡祥培,姜洋,胡润波.考虑客户消费行为的易逝品物流配送干扰管理模型[J].运筹与管理,2016,25(6):68-74.
[10] Tversky A, Kahneman D. Advances in prospect theory: cumulative representation of uncertainty[J]. Journal of Risk and Uncertainty, 1992, 5(4): 297-323.
[11] 姜洋,孙伟,丁秋雷,张旭.考虑行为主体的单机调度干扰管理模型[J].机械工程学报,2013,49(14):191-198.
[12] 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):1-9,19.
[13] 丁秋雷.客户时间窗变化的物流配送干扰管理模型研究——基于行为的视角[J].中国管理科学,2015,23(5):89-97.
[14] 丁秋雷,姜洋.基于行为运筹的生产调度干扰管理模型. 系统工程理论与实践,2016,36(3):664-673.
[15] Solomon M M. Algorithms for the vehicle routing and scheduling problems with time window constraints[J]. Operations Research, 1987, 35(2): 254-265.
[16] Thangiah S R, Osman I H, Sun T. Hybrid genetic algorithm, simulated annealing and tabu search methods for vehicle routing problems with time windows[R]. Technical Report UKC/OR94/4, Institute of Mathematics & Statistics, University of Kent, Canterbury, UK, 1994.
[17] Tan K C, Lee L H, Ou K. Artificial intelligence heuristics in solving vehicle routing problems with time window constraints[J]. Engineering Applications of Artificial intelligence, 2001, 14: 825-837.
[18] 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.
[19] 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.
[20] Abumaizar R J, Svestka J A. Rescheduling job shops under random disruptions[J]. International Journal of Production Research, 1997, 35(7): 2065-2082.

快件配送地址变化的干扰管理模型研究

Disruption Management Model for Location Change in Express Delivery

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	唐颖峰, 陈世平. 分布式数据流处理系统管理中负载均衡问题建模与求解[J]. 运筹与管理, 2021, 30(4): 155-162.
[2]	刘长石, 王松, 罗亮, 邓胜前. 时变路网条件下联合配送的开放式车辆路径问题[J]. 运筹与管理, 2021, 30(11): 26-33.
[3]	丁秋雷, 胡祥培, 齐飞, 姜洋. 行驶时间延迟下物流配送干扰管理模型——基于客户终身价值的视角[J]. 运筹与管理, 2021, 30(1): 6-12.
[4]	任剑锋, 叶春明, 杨枫. 带时间窗的车间搬运机器人路径优化建模及算法研究[J]. 运筹与管理, 2020, 29(5): 52-60.
[5]	陈萍, 董文哲, 于信尧. 新能源移动充电车路径优化问题研究[J]. 运筹与管理, 2020, 29(2): 12-18.
[6]	刘春来, 王建军. 具有学习和退化效应的单机干扰管理问题[J]. 运筹与管理, 2019, 28(1): 94-100.
[7]	刘艳秋,徐世达,蔡超. 部分联合运输策略下的物流车辆路径优化问题研究[J]. 运筹与管理, 2018, 27(8): 10-19.
[8]	张建同, 杨文娟. 基于优先级的进离港航班排序优化问题研究[J]. 运筹与管理, 2018, 27(6): 115-121.
[9]	王书勤, 黄茜. 军事定向越野路径优化问题建模及混合蚁群算法求解[J]. 运筹与管理, 2018, 27(4): 105-111.
[10]	韩文民, 陈婷, 高龙龙, 李正义, 翁红兵. 急件订单干扰下虚拟单元重调度[J]. 运筹与管理, 2018, 27(2): 68-78.
[11]	李杰, 赵旭东, 王玉霞, CHU Chao-hsien. 面向电商终端物流配送的电动车配置与路径集成优化[J]. 运筹与管理, 2018, 27(10): 23-30.
[12]	王道平, 徐展, 杨岑. 基于两阶段启发式算法的物流配送选址-路径问题研究[J]. 运筹与管理, 2017, 26(4): 70-75.
[13]	蒙秋男, 白雪, 赵聪. 混流无等待流水线批次批量生产计划方法[J]. 运筹与管理, 2017, 26(3): 178-186.
[14]	吴暖, 王诺, 刘忠波, 卢月. 基于人机交互—蚁群算法的港口疏船调度优化[J]. 运筹与管理, 2017, 26(10): 34-41.
[15]	杨华龙, 叶迪, 张倩, 曾庆成. 时间窗变动的车辆调度干扰管理模型与算法[J]. 运筹与管理, 2017, 26(10): 56-64.