随机需求的越库调度建模和算法
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摘要
本文研究随机需求的越库(Cross Docking)调度的建模和算法。越库调度是指物流的任何中间点只实现收发货的功能而没有货物存储与订单获取的做法。越库调度一般分为两个阶段,从供应商取货到越库中心的过程为第一阶段,从越库中心(Cross Docking Center)将货物配送到客户的过程为第二阶段。越库调度按照运输方式划分,主要有直送(Direct Delivery)和循环取料(Milk Run)两类。越库调度问题属于车辆路径问题的一种。
需求的随机性,是实际调度中最经常发生,而且也最难以避免的,也是随机调度问题中较常见和被研究得最多的一种,但是在越库调度方面,需求的随机性还未得到较多的研究。本文在对越库调度、运输方式和随机车辆路径问题进行分析研究后,结合越库调度问题的特性和运输方式的区别,对不同运输方式情况下的越库调度的随机需求进行了分析并提出了合理的假设,并给出了详细的需求生成方法。
根据不同的运输模式,本文研究了两类随机需求的越库调度问题: (1)两阶段直送的随机需求越库调度的建模与算法。首先对问题进行描述并提出问题的假设,并在此基础上提出了两阶段补偿模型及求解问题的样本均值近似算法(SAA算法),同时提出了两阶段的启发式算法。最后通过在小规模情况下进行决策准确性和平稳性的数值实验验证了SAA算法和启发式算法的有效性。
(2)直送-循环取料的随机需求混合越库调度的建模和算法。首先描述问题并提出问题的假设,提出了两阶段补偿模型及求解问题的SAA算法,并提出了基于预留容量的两阶段启发式算法。小规模数值实验的结果表明了算法的有效性,而大规模的数值实验给出了启发式算法的最佳参数选择策略。
本文的研究旨在可以为企业越库物流的实际运作提供决策支持,同时希望可以将研究成果真正运用到实际中,为越库物流的调度管理提供理论基础和有用的算法。
This thesis studies the modeling and heuristics of cross docking scheduling problem with stochastic demand. Cross docking is an operation that moves goods directly from receiving point to shipping point without storage. There are two stages in cross docking scheduling: the first stage in which goods are collected from suppliers to cross docking center by vehicles, the second stage in which goods are sent from cross docking center to customers. Cross docking scheduling can be classed into two classes by the transportation model: direct delivery and milk run. Cross docking scheduling problem is a kind of vehicle routing problem.
The randomness of demand always occurs in practical scheduling and is difficult to avoid. Also it’s widely studied in stochastic scheduling. However, it’s seldom studied in cross docking scheduling. This thesis analyzes cross docking, transportation models and vehicle routing problems. The stochastic demands in cross docking systems with different transportation models are analyzed. Reasonable assumptions and data generating methods are introduced.
According to different transportation model, this thesis studied two kinds of cross docking scheduling problems with stochastic demand:
(1) The modeling and heuristics of two-stage direct directly cross docking scheduling with stochastic demand. First, the problem is described and assumptions are raised. A two-stage recourse model, a SAA based heuristic and a two-stage heuristic are presented. According to the numerical experiment about solution effectiveness and steady in small scale, the SAA heuristic and two-stage heuristic are proved to be effective.
(2) Modeling and heuristics of direct directly - milk run mixed cross docking with stochastic demand. First, the problem is described and assumptions are raised. A two-stage recourse model, a SAA based heuristic and a capacity reservation based two-stage heuristic are presented. According to the numerical experiment about solution effectiveness and steady in small scale, the SAA heuristic and two-stage heuristic are proved to be effective. And the best parameter settings are given by the large scale numerical experiment.
This thesis aims to provide decision support for the practical cross docking operation in logistics companies. Also, the research aims to be applied in the practical operation and provide the theory support and useful heuristic for the scheduling management in cross docking system.
需求的随机性,是实际调度中最经常发生,而且也最难以避免的,也是随机调度问题中较常见和被研究得最多的一种,但是在越库调度方面,需求的随机性还未得到较多的研究。本文在对越库调度、运输方式和随机车辆路径问题进行分析研究后,结合越库调度问题的特性和运输方式的区别,对不同运输方式情况下的越库调度的随机需求进行了分析并提出了合理的假设,并给出了详细的需求生成方法。
根据不同的运输模式,本文研究了两类随机需求的越库调度问题: (1)两阶段直送的随机需求越库调度的建模与算法。首先对问题进行描述并提出问题的假设,并在此基础上提出了两阶段补偿模型及求解问题的样本均值近似算法(SAA算法),同时提出了两阶段的启发式算法。最后通过在小规模情况下进行决策准确性和平稳性的数值实验验证了SAA算法和启发式算法的有效性。
(2)直送-循环取料的随机需求混合越库调度的建模和算法。首先描述问题并提出问题的假设,提出了两阶段补偿模型及求解问题的SAA算法,并提出了基于预留容量的两阶段启发式算法。小规模数值实验的结果表明了算法的有效性,而大规模的数值实验给出了启发式算法的最佳参数选择策略。
本文的研究旨在可以为企业越库物流的实际运作提供决策支持,同时希望可以将研究成果真正运用到实际中,为越库物流的调度管理提供理论基础和有用的算法。
This thesis studies the modeling and heuristics of cross docking scheduling problem with stochastic demand. Cross docking is an operation that moves goods directly from receiving point to shipping point without storage. There are two stages in cross docking scheduling: the first stage in which goods are collected from suppliers to cross docking center by vehicles, the second stage in which goods are sent from cross docking center to customers. Cross docking scheduling can be classed into two classes by the transportation model: direct delivery and milk run. Cross docking scheduling problem is a kind of vehicle routing problem.
The randomness of demand always occurs in practical scheduling and is difficult to avoid. Also it’s widely studied in stochastic scheduling. However, it’s seldom studied in cross docking scheduling. This thesis analyzes cross docking, transportation models and vehicle routing problems. The stochastic demands in cross docking systems with different transportation models are analyzed. Reasonable assumptions and data generating methods are introduced.
According to different transportation model, this thesis studied two kinds of cross docking scheduling problems with stochastic demand:
(1) The modeling and heuristics of two-stage direct directly cross docking scheduling with stochastic demand. First, the problem is described and assumptions are raised. A two-stage recourse model, a SAA based heuristic and a two-stage heuristic are presented. According to the numerical experiment about solution effectiveness and steady in small scale, the SAA heuristic and two-stage heuristic are proved to be effective.
(2) Modeling and heuristics of direct directly - milk run mixed cross docking with stochastic demand. First, the problem is described and assumptions are raised. A two-stage recourse model, a SAA based heuristic and a capacity reservation based two-stage heuristic are presented. According to the numerical experiment about solution effectiveness and steady in small scale, the SAA heuristic and two-stage heuristic are proved to be effective. And the best parameter settings are given by the large scale numerical experiment.
This thesis aims to provide decision support for the practical cross docking operation in logistics companies. Also, the research aims to be applied in the practical operation and provide the theory support and useful heuristic for the scheduling management in cross docking system.
引文
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[2] Napolitano M.. Making the move to crossdocking[M]. Warehousing Education and Research Council. 2000.
[3] Schaffer B.. Implementing a successful cross-docking operation[J]. Plant Engineering. 2000, 54(3): 128-133.
[4] Gue K. R.. Crossdocking: just-in-time for distribution[M]. Naval Postgraduate School Graduate School of Business. 2001.
[5] Stalk G., Evans P., Shulman L. E.. Competing on capabilities, the new rules of corporate strategies[J]. Harvard Business Review. 1992, 57-68.
[6]马东彦.越库——物流领域的准时制策略[J].物流科技. 2008, (1): 1-5.
[7]王武,钟双红.直送式配送运输路径优化算法浅析[J].物流工程与管理. 2009, 181(7): 60-62.
[8]张蕾.循环取货的应用问题分析[J].中外物流. 2006, 18: 23-24.
[9] Toth Paolo, Vigo Daniele. The Vehicle Routing Problem[M]. Society for Industrial and Applied Mathematics philadephia. 2002
[10] Guo Yaohuang, Xie Binglei, Guo Qiang. Overview of Stochastic Vehicle Routing Problems[J]. Journal of Southwest Jiaotong University. 2002, 10(2): 113-121.
[11] Waller A. Matthew, Cassady C. Richard, Ozment John. Impact of cross-docking on inventory in a decentralized retail supply chain[J]. Transportation Research Part E: Logistics and Transportation Review. 2006, 42(5): 359-382.
[12] Yu Wooyeon, Egbelu J. Pius. Scheduling of inbound and outbound trucks in cross docking systems with temporary storage[J]. Eurpean Journal of Operation Research. 2008, 184(1): 377-396.
[13] Lee Young Hae, Jung Woo Jung, Lee Kyong Min. Vehicle routing scheduling for cross-docking in the supply chain[J]. Computer & Industrial Engineering. 2006, 51(2): 247-256.
[14] Boysen Nils, Fliedner Malte. Cross dock scheduling, Classification, literature review and research agenda[J]. Omega. 2010, 38(6): 413-422.
[15]洪亮.越库配送在连锁零售业中的应用[J].物流技术. 2006, (10): 12-14.
[16]马德良,张家钰,戴惠良. VMI系统下一个物流成本控制优化模型的建立[J].东华大学学报:自然科学版. 2003, 29(6): 35-38.
[17]施文,许茂增,刘志学.基于RSM的越库配送系统参数优化[J].计算机应用研究. 2009, 26 (8): 2956-2959.
[18]陈峰,宋凯雷.越库物流调度问题及其近似与精确算法[J].工业工程与管理. 2006, (6): 53-58.
[19]曹克官,陈峰.直运越库物流的精确算法研究[J].工业工程与管理. 2009, (5): 41-48.
[20]马珊静,陈峰,宋得朝等.越库配送物流系统车辆调度算法的研究[J].现代制造工程. 2009, (1): 12-15.
[21]俞亮,陈峰.最小化误工个数的越库调度模型与启发式算法[J].上海交通大学学报. 2009, 43(12): 1984-1988.
[22] Burns D. Lawrence, Hall W. Randolph, Blumenfeld E. Dennis et al. Distribution strategies that minimize transportation and inventory costs[J]. Operations Research. 1985, 33 (3): 469-490.
[23] Gallego G., Simchi-Levi D. On the effectiveness of direct shipping strategy for the one-warehouse multi-retailer R-systems[J]. Management Science. 1990, 36: 240-243.
[24] Barnes-Schuster D., Bassok Y., Direct shipping and the dynamic single-depot /multi-retailer inventory system[J]. European Journal of Operational Research. 1997, 101: 509-518.
[25] Reinman, M., Rubio R., Wein.L.. Heavy traffic analysis of the dynamic stochastic inventory-routing problem[J]. Transportation Science. 1999, 33 (4): 361-380.
[26] Kleywegt J. Anton, Nori S. Vijay, Savelsbergh P. W. Martin. The stochastic inventory routing problem with direct deliveries[J]. Transportation Science. 2002, 36 (1): 94-118.
[27]龚凤美,马士华.基于Supply Hub的第三方物流直送工位模式研究[J].工业技术经济. 2007, 126 (18): 124-127.
[28]马东彦.基于直送的两阶段混合调度模型的模拟退火算法研究[J].物流技术. 2009. 205(10): 48-51.
[29]朱荣.第三方物流直送工位运作模式研究[J].经济与社会发展. 2010. (8): 12-17.
[30]沈飞,陈杰,陈峰.循环取料下的多道口分配问题及算法研究[J].物流技术. 2009. (9): 46-48.
[31]张坤,江海荣.汽车零部件循环取货车辆路径优化研究[J].物流科技. 2009. (2): 69-73.
[32] Dimitris J., Bertsimas. A Vehicle Routing Problem with Stochastic Demand[J]. Operation Research. 1992, 40(3): 574-585
[33] Laporte Gilbert, Trudeau Pierre. Vehicle Routing with Stochastic Demands, Properties and Solution Frameworks[J]. Transportation Science. 1989, 23(3): 166-176.
[34] Gendreau Michel, Laporte Gilbert, Seguin Rene. A Tabu Search Heuristic for the Vehicle Routing Problem with Stochastic Demands and Customers[J]. Operation Research. 1996, 44(3): 469-477.
[35] Moghadam B. Farhang, Sadjadi S. J., Seyedhosseini S. M.. Comparing Mathematical and Heuristic Method for Robust Vehicle Routing Problem[J]. International Journal of Research and Reviews in Applied Sciences. 2010, 2(2): 108-116.
[36] Ismail Z., Irhamah. Solving the Vehicle Routing Problem with Stochastic Demands via Hyybrid Genetic Alorithm - Tabu Search[J]. Journal of Mathematics and Statistics. 2008, 4(3): 161-167.
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