基于分区拣选策略的分拣机系统综合优化研究
|
摘要
随着我国经济的不断发展,配送中心的商品拣选日益呈现小批量、多品种、高时效的发展趋势,对订单处理时间提出了更高的要求。拣选系统是处理客户订单的关键环节,其工作效率往往成为制约配送中心吞吐能力的瓶颈。据统计,订单拣选成本可占到配送中心总作业成本的60%,订单处理总时间可占到总作业时间的40%左右。为有效降低订单处理总时间,越来越多的行业采用自动拣选系统,如卷烟、药品行业配送中心的分拣机系统。
在各种自动拣选系统中,分拣机系统最适合处理多品种、小批量的订单货物。在不显著增加作业成本前提下,分拣机系统多采用分区拣选策略降低订单处理总时间。该策略下分拣机系统划分为若干拣货区,各区分拣机共同拣选同一订单货物并送至缓冲区,然后在恰当的时机合流货物,完成订单处理。因此,研究分区拣选策略下的分拣机系统优化问题,对于降低订单处理总时间,节约物流成本具有重要意义。
然而,目前国内外学者关于此领域的研究存在若干问题。一是研究对象多集中在人工拣选系统,很少涉及到分拣机系统,而人工拣选系统中订单处理总时间构成与分拣机系统差别很大。二是关于分区拣选策略下的综合优化研究较少,多为单一影响因素优化,如品项分配优化。三是关于分拣机系统研究多集中在设备选型优化与改造方面,较少涉及对拣选策略和方法的优化。
基于此,本文提出分区拣选策略下的分拣机系统综合优化问题。该问题以订单处理总时间最小为优化目标,研究内容包括该策略下影响订单处理总时间的各主要因素：拣货区数量、货物合流模式、品项分配、品项拆分。相比对单一因素的优化分析,本文研究问题更为复杂。
在研究该问题过程中,本文主要内容与成果如下：
(1)通过设计排队系统描述分拣机系统在分区拣选策略下的工作流程,建立了综合优化问题模型。
设计了排队系统描述分区拣选策略下分拣机系统工作流程,得到了订单处理总时间的结构组成：货物拣选时问、分拣机暂停时间、货物合流时问；得到并分析了各主要影响因素：拣货区数量、货物合流模式、品项分配、品项拆分。以此建立了分拣机系统综合优化问题模型,优化目标为订单处理总时间最小,涉及变量包括各主要影响因素,该模型属于复杂整数规划问题。
(2)为降低模型求解难度,将优化目标转化为实现最佳订单拣选量分配结果,并将综合优化问题拆分为两大子问题,采取先独立讨论再综合求解的思路。
通过线性规划松弛与代理松弛模型约束条件,得到订单处理总时间下界函数和最佳订单拣选量分配结果：各订单拣选量均匀分配到各拣货区。将该结果作为新的优化目标,降低了模型求解难度,扩展了求解方法。根据变量特点,将综合优化问题分解为品项分配与品项拆分子问题分别讨论,以便最终确定综合求解方法。
此外,通过在不同合流模式下分析下界函数与拣货区数量单调性关系,得到了更小的拣货区数量范围,降低了解的遍历空间。
(3)品项分配子问题中,提出了基于拣选量均分的品项聚类目标,设计了复合聚类算法求解。
首先介绍了学者Jane的聚类目标,分析其缺陷后根据最佳拣选量分配结果,提出了基于拣选量均分的聚类目标,并在向量空间中以曼哈顿距离描述。然后介绍了常用的层次聚类算法,分析其缺陷后提出了复合聚类算法。该算法通过改进最长处理时间优先算法(Largest Processing Time, LPT)的静态聚类算法得到良好初始解,再采用改进的K-means动态聚类算法优化初始解。最后实例分析证明了基于拣选量均分的聚类目标与复合聚类算法优越性。
(4)品项拆分子问题中,通过EIQ分析方法初步拆分品项,并提出子品项拣选量再分配问题,根据问题特点设计了回溯法求解。
在初步拆分品项的EIQ分析中,将品项按拣选总量降序排序,并通过累加拣选量所占比例确定拆分品项。然后各选定品项都拆分为两个子品项,将其在各订单拣选量平均分配。
为优化初步品项拆分结果,提出子品项拣选量再分配问题。以最佳订单拣选量分配结果为优化目标,建立了数学模型并提出回溯法求解。根据问题特点,在组织和搜索解空间过程中两次降低了解的搜索范围。最后实例分析证明了回溯法的有效性。
(5)根据各子问题讨论结果,提出分拣机系统综合优化问题的综合求解方法。
综合求解方法结合了枚举法与多种启发式算法,首先初步拆分品项并确定拣货区数量范围,然后进行品项分配优化与子品项拣选量再分配优化,最后通过判断算法结束条件迭代优化,得到了两种合流模式和拣货区数量范围内的有限组解,通过比较各解对应的订单处理总时间确定最佳求解结果,既降低了综合优化问题的求解难度,又在较大空间中搜索了可行解。实例分析证明了该方法的有效性和优越性。
As the economy keeps growing, the commodity with small batch diversification are needed more frequently, which requires a faster order fulfillment time. The working efficiency of order picking system always becomes the bottleneck of throughput of distribution center. It's said that the order picking cost accounts for about 60% of the total cost, and the order fulfillment time accounts for about 40% of the total. In order to satisfy customer's requirement, recently more distribution center starts to apply automated order picking system to deal with orders, such as the cigarette and medicine dispenser systems of distribution center.
Among all kinds of automated order picking systems, the dispenser system are most suitable to pick goods with small batch diversification. The total order fulfillment time is an important index to evaluate dispenser system. Without obvious increase of system cost, the zone picking strategy effectively decrease the total order fulfillment time. Under this strategy, the dispenser system is partitioned into several zones, each of them is working simultaneously to pick the same order and transport the picked goods to buffer areas, then they are merged together at the right time. So, it's the study on zone picking strategy of great importance to decrease the total order fulfillment time and logistics cost.
However, the literature research in this field has several problems. First is most related research focuses in manual order picking system, little refers to automated picking system. And the construction of order fulfillment time is different in both systems. Second is most study is about single factor's optimization, such as the stock keeping unit (SKU) assignment problem, instead of integrated research. Third is in the field of dispenser system, most research focus in the equipment selection, little refers to picking strategies and methods optimization.
Based on this, this paper presents the integrated optimization problem of dispenser system under zone picking strategy. The objective is to minimize the total order fulfillment time. The content includes all main factors of affecting the total order fulfillment time:zone numbers, goods merging mode, SKU assignment, SKU split. Compared to the single factor research, the problem this paper study is more complicated.
During the researching process, the main content and achievement are is as below:
(1) The integrated optimization problem model is built, by expressing the working procedure of dispenser system under zone picking strategy with the help of the queuing system.
This paper designed a queuing system to express the working procedure of dispenser system under zone picking strategy, get the structure of total order fulfillment time:goods picking time, dispenser suspending time, goods merging time; and get the main influencing factors:zone numbers, goods merging mode, SKU assignment, SKU split. Based on this, this paper built the integrated optimization problem model of dispenser system. The objective of this model is to minimize the total order fulfillment time, and the variables include all main influencing factors. The model belongs to the complicated integer programming problem.
(2) In order to reduce the difficulty of solving the model, this paper transformed the objective to the optimal picking quantity distribution result of orders, and split the integrated optimization problem to two sub-problems. The solving thinking of discussing sub-problems respectively and then proposing the integrated solving method is adopted.
By the linear relaxation and surrogate relaxation of constraint conditions of model, the lower bound function is got, as well as the optimal picking quantity distribution result:each order's picking quantity is averagely distributed among zones. Taking the optimal result as the new objective, the solving difficulty can be reduced, and the solving methods are expanded. In terms of the variable characters, the integrated optimization problem is disassembled into SKU assignment and SKU split sub-problems which are discussed respectively, so that the comprehensive solution can be got in the final.
Besides, by analyzing the monotonicity of the lower bound function and zone numbers, a smaller value range of zone numbers are got, which decreases the traverse space of results.
(3) In solving SKU assignment sub-problem, the SKU clustering objective is proposed based on the optimal picking quantity distribution results of orders. And a complex clustering algorithm is designed to solve this sub-problem.
This paper classifies the SKU assignment problem into clustering problem. It introduces the clustering objective based on scholar Jane, analyzes the faults and proposes clustering objective based on equivalent distribution of picking quantity, which is expressed by Manhattan distance of vector space. It also introduces the common hierarchical clustering algorithm, analyzes the faults and proposes complex clustering algorithm. This algorithm firstly gets a good initial solution by improved Largest Processing Time (LPT) static clustering algorithm, then optimizes the initial solution by improved K-means dynamic clustering algorithm. The real example analysis proves the superiority of clustering objective based on equivalent distribution of picking quantity, and the complex clustering algorithm.
(4) In solving SKU split sub-problem, firstly the initial SKU split is proceeded by EIQ analysis, then the sub-SKU picking quantity redistribution problem is presented, which is solved by the proposed backtracking method.
In the EIQ analysis of initial SKU split, all SKUs are sorted in descending sequence by picking their picking quantity of orders, and the split SKUs are selected by the proportion of their accumulated picking quantity. Then each one is split into two sub-SKUs and its picking quantity in each order is averagely distributed to them.
In order to optimize the result of initial SKU split, this paper proposes the sub-SKU picking quantity redistribution problem. The mathematical model of the problem is built with the objective of optimal picking quantity distribution result of orders. The backtracking method is proposed to solve the problem. In solving process, the search space is reduced two times. The first time happens in organizing solution space, when the search space is reduced by analyzing characters of variables. The second time happens in searching solution space, when only the specified sub-SKUs in zone are visited. The real example analysis proves the effectiveness and superiority of proposed backtracking method.
(5) Based on discussing results of two sub-problems, proposes the comprehensive solution to solve the integrated optimization problem of dispenser system.
The comprehensive solution combines enumeration method and multiple heuristic algorithms, firstly proceeds initial SKU split and gets the value range of zone numbers, then proceeds the SKU assignment and split optimization in two merging modes and value range of zone numbers, at last proceeds the iterative optimization by judging the end condition. In the final, the best solution is selected from finite solutions, by comparing the total order fulfillment time. The real example analysis proves the effectiveness and superiority of proposed backtracking method.
在各种自动拣选系统中,分拣机系统最适合处理多品种、小批量的订单货物。在不显著增加作业成本前提下,分拣机系统多采用分区拣选策略降低订单处理总时间。该策略下分拣机系统划分为若干拣货区,各区分拣机共同拣选同一订单货物并送至缓冲区,然后在恰当的时机合流货物,完成订单处理。因此,研究分区拣选策略下的分拣机系统优化问题,对于降低订单处理总时间,节约物流成本具有重要意义。
然而,目前国内外学者关于此领域的研究存在若干问题。一是研究对象多集中在人工拣选系统,很少涉及到分拣机系统,而人工拣选系统中订单处理总时间构成与分拣机系统差别很大。二是关于分区拣选策略下的综合优化研究较少,多为单一影响因素优化,如品项分配优化。三是关于分拣机系统研究多集中在设备选型优化与改造方面,较少涉及对拣选策略和方法的优化。
基于此,本文提出分区拣选策略下的分拣机系统综合优化问题。该问题以订单处理总时间最小为优化目标,研究内容包括该策略下影响订单处理总时间的各主要因素：拣货区数量、货物合流模式、品项分配、品项拆分。相比对单一因素的优化分析,本文研究问题更为复杂。
在研究该问题过程中,本文主要内容与成果如下：
(1)通过设计排队系统描述分拣机系统在分区拣选策略下的工作流程,建立了综合优化问题模型。
设计了排队系统描述分区拣选策略下分拣机系统工作流程,得到了订单处理总时间的结构组成：货物拣选时问、分拣机暂停时间、货物合流时问；得到并分析了各主要影响因素：拣货区数量、货物合流模式、品项分配、品项拆分。以此建立了分拣机系统综合优化问题模型,优化目标为订单处理总时间最小,涉及变量包括各主要影响因素,该模型属于复杂整数规划问题。
(2)为降低模型求解难度,将优化目标转化为实现最佳订单拣选量分配结果,并将综合优化问题拆分为两大子问题,采取先独立讨论再综合求解的思路。
通过线性规划松弛与代理松弛模型约束条件,得到订单处理总时间下界函数和最佳订单拣选量分配结果：各订单拣选量均匀分配到各拣货区。将该结果作为新的优化目标,降低了模型求解难度,扩展了求解方法。根据变量特点,将综合优化问题分解为品项分配与品项拆分子问题分别讨论,以便最终确定综合求解方法。
此外,通过在不同合流模式下分析下界函数与拣货区数量单调性关系,得到了更小的拣货区数量范围,降低了解的遍历空间。
(3)品项分配子问题中,提出了基于拣选量均分的品项聚类目标,设计了复合聚类算法求解。
首先介绍了学者Jane的聚类目标,分析其缺陷后根据最佳拣选量分配结果,提出了基于拣选量均分的聚类目标,并在向量空间中以曼哈顿距离描述。然后介绍了常用的层次聚类算法,分析其缺陷后提出了复合聚类算法。该算法通过改进最长处理时间优先算法(Largest Processing Time, LPT)的静态聚类算法得到良好初始解,再采用改进的K-means动态聚类算法优化初始解。最后实例分析证明了基于拣选量均分的聚类目标与复合聚类算法优越性。
(4)品项拆分子问题中,通过EIQ分析方法初步拆分品项,并提出子品项拣选量再分配问题,根据问题特点设计了回溯法求解。
在初步拆分品项的EIQ分析中,将品项按拣选总量降序排序,并通过累加拣选量所占比例确定拆分品项。然后各选定品项都拆分为两个子品项,将其在各订单拣选量平均分配。
为优化初步品项拆分结果,提出子品项拣选量再分配问题。以最佳订单拣选量分配结果为优化目标,建立了数学模型并提出回溯法求解。根据问题特点,在组织和搜索解空间过程中两次降低了解的搜索范围。最后实例分析证明了回溯法的有效性。
(5)根据各子问题讨论结果,提出分拣机系统综合优化问题的综合求解方法。
综合求解方法结合了枚举法与多种启发式算法,首先初步拆分品项并确定拣货区数量范围,然后进行品项分配优化与子品项拣选量再分配优化,最后通过判断算法结束条件迭代优化,得到了两种合流模式和拣货区数量范围内的有限组解,通过比较各解对应的订单处理总时间确定最佳求解结果,既降低了综合优化问题的求解难度,又在较大空间中搜索了可行解。实例分析证明了该方法的有效性和优越性。
As the economy keeps growing, the commodity with small batch diversification are needed more frequently, which requires a faster order fulfillment time. The working efficiency of order picking system always becomes the bottleneck of throughput of distribution center. It's said that the order picking cost accounts for about 60% of the total cost, and the order fulfillment time accounts for about 40% of the total. In order to satisfy customer's requirement, recently more distribution center starts to apply automated order picking system to deal with orders, such as the cigarette and medicine dispenser systems of distribution center.
Among all kinds of automated order picking systems, the dispenser system are most suitable to pick goods with small batch diversification. The total order fulfillment time is an important index to evaluate dispenser system. Without obvious increase of system cost, the zone picking strategy effectively decrease the total order fulfillment time. Under this strategy, the dispenser system is partitioned into several zones, each of them is working simultaneously to pick the same order and transport the picked goods to buffer areas, then they are merged together at the right time. So, it's the study on zone picking strategy of great importance to decrease the total order fulfillment time and logistics cost.
However, the literature research in this field has several problems. First is most related research focuses in manual order picking system, little refers to automated picking system. And the construction of order fulfillment time is different in both systems. Second is most study is about single factor's optimization, such as the stock keeping unit (SKU) assignment problem, instead of integrated research. Third is in the field of dispenser system, most research focus in the equipment selection, little refers to picking strategies and methods optimization.
Based on this, this paper presents the integrated optimization problem of dispenser system under zone picking strategy. The objective is to minimize the total order fulfillment time. The content includes all main factors of affecting the total order fulfillment time:zone numbers, goods merging mode, SKU assignment, SKU split. Compared to the single factor research, the problem this paper study is more complicated.
During the researching process, the main content and achievement are is as below:
(1) The integrated optimization problem model is built, by expressing the working procedure of dispenser system under zone picking strategy with the help of the queuing system.
This paper designed a queuing system to express the working procedure of dispenser system under zone picking strategy, get the structure of total order fulfillment time:goods picking time, dispenser suspending time, goods merging time; and get the main influencing factors:zone numbers, goods merging mode, SKU assignment, SKU split. Based on this, this paper built the integrated optimization problem model of dispenser system. The objective of this model is to minimize the total order fulfillment time, and the variables include all main influencing factors. The model belongs to the complicated integer programming problem.
(2) In order to reduce the difficulty of solving the model, this paper transformed the objective to the optimal picking quantity distribution result of orders, and split the integrated optimization problem to two sub-problems. The solving thinking of discussing sub-problems respectively and then proposing the integrated solving method is adopted.
By the linear relaxation and surrogate relaxation of constraint conditions of model, the lower bound function is got, as well as the optimal picking quantity distribution result:each order's picking quantity is averagely distributed among zones. Taking the optimal result as the new objective, the solving difficulty can be reduced, and the solving methods are expanded. In terms of the variable characters, the integrated optimization problem is disassembled into SKU assignment and SKU split sub-problems which are discussed respectively, so that the comprehensive solution can be got in the final.
Besides, by analyzing the monotonicity of the lower bound function and zone numbers, a smaller value range of zone numbers are got, which decreases the traverse space of results.
(3) In solving SKU assignment sub-problem, the SKU clustering objective is proposed based on the optimal picking quantity distribution results of orders. And a complex clustering algorithm is designed to solve this sub-problem.
This paper classifies the SKU assignment problem into clustering problem. It introduces the clustering objective based on scholar Jane, analyzes the faults and proposes clustering objective based on equivalent distribution of picking quantity, which is expressed by Manhattan distance of vector space. It also introduces the common hierarchical clustering algorithm, analyzes the faults and proposes complex clustering algorithm. This algorithm firstly gets a good initial solution by improved Largest Processing Time (LPT) static clustering algorithm, then optimizes the initial solution by improved K-means dynamic clustering algorithm. The real example analysis proves the superiority of clustering objective based on equivalent distribution of picking quantity, and the complex clustering algorithm.
(4) In solving SKU split sub-problem, firstly the initial SKU split is proceeded by EIQ analysis, then the sub-SKU picking quantity redistribution problem is presented, which is solved by the proposed backtracking method.
In the EIQ analysis of initial SKU split, all SKUs are sorted in descending sequence by picking their picking quantity of orders, and the split SKUs are selected by the proportion of their accumulated picking quantity. Then each one is split into two sub-SKUs and its picking quantity in each order is averagely distributed to them.
In order to optimize the result of initial SKU split, this paper proposes the sub-SKU picking quantity redistribution problem. The mathematical model of the problem is built with the objective of optimal picking quantity distribution result of orders. The backtracking method is proposed to solve the problem. In solving process, the search space is reduced two times. The first time happens in organizing solution space, when the search space is reduced by analyzing characters of variables. The second time happens in searching solution space, when only the specified sub-SKUs in zone are visited. The real example analysis proves the effectiveness and superiority of proposed backtracking method.
(5) Based on discussing results of two sub-problems, proposes the comprehensive solution to solve the integrated optimization problem of dispenser system.
The comprehensive solution combines enumeration method and multiple heuristic algorithms, firstly proceeds initial SKU split and gets the value range of zone numbers, then proceeds the SKU assignment and split optimization in two merging modes and value range of zone numbers, at last proceeds the iterative optimization by judging the end condition. In the final, the best solution is selected from finite solutions, by comparing the total order fulfillment time. The real example analysis proves the effectiveness and superiority of proposed backtracking method.
引文
[1]张健.公路快速货运轴辐式网络运载规划研究与应用[D].济南：山东大学,2009.
[2]肖际伟.配送中心拣货系统优化[D].济南：山东大学,2010.
[3]王转,程国全,配送中心系统规划[M].北京：中国物资出版社,2003.
[4]KHACHATRYAN M. Small parts high volume order picking systems[D]. Georgia:Georgia Institute of Technology,2006.
[5]GOETSCHALCKX M, ASHAYERI J. Classification and design of order picking[J]. Logistics Information Management,1989,2(2):99-106.
[6]TOMPKINS J A, WHITE J A, BOZER Y A, et al., Facilities planning[M]. New Jersey:John Wiley & Sons,2003.
[7]王国华,中国现代物流大全C卷：现代物流技术与装备[M].北京：中国铁道出版社,2004.
[8]DE KOSTER R, LE-DUC T, ROODBERGEN K J. Design and control of warehouse order picking:A literature review[J]. European Journal of Operational Research,2007,182(2):481-501.
[9]吴耀华.利用高科技构建节约型物流配送系统[J].物流技术,2007,26(8)：5-6.
[10]黄启明.自动分拣系统及其应用前景分析[J].物流技术,2002(5)：7-8.
[11]DALLARI F, MARCHET G, MELACINI M. Design of order picking system[J]. The International Journal of Advanced Manufacturing Technology, 2009,42(1):1-12.
[12]GU J X, GOETSCHALCKX M, MCGINNIS L F. Research on warehouse operation:A comprehensive review[J]. European Journal of Operational Research,2007,177(1):1-21.
[13]ROUWENHORST B, REUTER B, STOCKRAHM V, et al. Warehouse design and control:Framework and literature review[J]. European Journal of Operational Research,2000,122(3):515-533.
[14]VAN DEN BERG J P. A literature survey on planning and control of warehousing systems[J]. IIE Transactions,1999,31(8):751-762.
[15]谢伟东,王磊,佘翊妮等.条烟自动分拣的异步法及其装置[J].工程设计学报,2007,14(2)：112-116.
[16]鞠镁隆,沈敏德.卷烟自动分拣机的创新设计[J].轻工机械,2006,24(1)：123-125.
[17]张鹏,沈敏德,范维华.基于Profibus-DP的塔式卷烟自动分拣机的设计应用[J].烟草科技,2007,36(3)：23-24.
[18]TSAI C-Y, LIOU J J H, HUANG T-M. Using a multiple-ga method to solve the batch picking problem:Considering travel distance and order due time[J]. International Journal of Production Research,2008,46(22):6533-6555.
[19]HESKETT J L. Cube-per-order index-a key to warehouse stock location[J]. Transportation and Distribution Management,1963(3):27-31.
[20]HESKETT J L. Putting the cube-per-order index to work in warehouse layout[J]. Transport and Distribution Management,1964,4(8):23-30.
[21]KALLINA C, LYNN J. Application of the cube-per-order index rule for stock location in a distribution warehouse[J]. Indterfaces,1976,7(1):37-46.
[22]GARFINKEL M. Minimizing multi-zone orders in the correlated storage assignment problem[D]. Georgia:Georgia Institute of Technology,2005.
[23]FRAZELLE E H. Stock location assignment and order picking productivity[D]. Georgia:Georgia Institute of Technology,1989.
[24]XIAO J, ZHENG L. A correlated storage location assignment problem in a single-block-multi-aisles warehouse considering bom information[J]. International Journal of Production Research,2010,48(5):1321-1338.
[25]肖建,郑力.考虑需求相关性的多巷道仓库货位分配问题[J].计算机集成制造系统,2008,14(12)：2447-2451.
[26]GAREY M R, JOHNSON D S, Computers and intractability:A guide to the theory of np-completeness[M]. New York:W. H. Freeman and Company, 1979.
[27]ANDERBERG M R, Cluster analysis for applications[M]. New York: Academic Press,1973.
[28]SADIQ M. A hybrid clustering algorithm for reconfiguration of dynamic order picking systems[D]. Arkansas:University of Arkansas,1995.
[29]SADIQ M, LANDERS T L, TAYLOR G D. An assignment algorithm for dynamic picking systems[J]. IIE Transactions,1996,28(8):607.
[30]LEE M. A storage assignment policy in a man-on-board automated storage-retrieval system[J]. International Journal of Production Research,1992, 30(10):2281-2292.
[31]LIU C M. Clustering techniques for stock location and order-picking in a distribution center[J]. Computers & Operations Research,1999,26(10-11): 989-1002.
[32]HO Y C, WEE H, CHEN H. A geometric design of zone-picking in a distribution warehouse[C]. Proceedings of the 2007 international conference on Computational science and its applications, Heidelberg:Springer-Verlag, 2007:625-636.
[33]SARKER B R. The resemblance coefficients in group technology:A survey and comparative study of relational metrics[J]. Computers & Industrial Engineering,1996,30(1):103-116.
[34]臧照良.成组技术基础上的CAPP[J].机械制造与自动化,2006,35(5)：91-93.
[35]ROMESBURG H C, Cluster analysis for researchers[M]. North Carolina: Lulu Press,1989.
[36]HO Y C, CHIEN S P. A comparison of two zone-visitation sequencing strategies in a distribution centre[J]. Computers & Industrial Engineering, 2006,50(4):426-439.
[37]AMIRHOSSEINI M M, SHARP G P. Simultaneous analysis of products and orders in storage assignment[J]. Manufacturing Science and Engineering, 1996(4):803-811.
[38]WEI H, CHEN Z. Clusters and filling-curve-based storage assignment in a circuit board assembly kitting area[J]. IIE Transactions,2008,40(1):569-585.
[39]HUA W. Cluster based storage policies in kitting area[D]. Georgia:Georgia Institute of Technology,2001.
[40]JANE C C, LAIH Y W. A clustering algorithm for item assignment in a synchronized zone order picking system[J]. European Journal of Operational Research,2005,166(2):489-496.
[41]LAWLER E L, LENSTRA J K, RINNOOY KAN A H G, SHMOYS D B. The traveling salesman problem[M]. Chichester:Wiley,1995.
[42]ASCHEUER N, GROTSCHEL M, ABDEL-HALIM E S. Order picking in an automatic warehouse:Solving online asymmetric TSPs[J]. Mathematical Methods of Operations Research,1999,49(4):501-515.
[43]DANIELS R L, RUMMEL J L, SCHANTZ R. A model for warehouse order picking[J]. European Journal of Operational Research,1998,105(1):1-17.
[44]RATLIFF H D, ROSENTHAL A S. Orderpicking in a rectan-gular warehouse: A solvable case of the traveling salesman problem[J]. Operations Research, 1983,31(3):507-521.
[45]CORNUE'JOLS G, FONLUPT J, NADDEF D. The traveling salesman problem on a graph and some related integer polyhedra[J]. Mathematical Programming,1985,33(2):1-27.
[46]DE KOSTER R, VAN DE POORT E S. Routing orderpickers in a warehouse: Acomparison between optimal and heuristic solutions[J]. HE Transactions, 1998,30(1):469-480.
[47]ROODBERGEN K J. Layout and routing methods for warehouses[D]. Netherland:RSM Erasmus University,2001.
[48]HALL R W. Distance approximation for routing manual pickers in a warehouse[J]. HE Transactions,1993,25(2):77-87.
[49]PETERSEN C G. An evaluation of order picking routing policies [J]. International Journal of Operations & Production Management,1997,17(11): 1098-1111.
[50]DE KOSTER R B M, LE-DUC T, YUGANG Y. Optimal storage rack design for a 3-dimensional compact AS/RS[J]. International Journal of Production Research,2008,46(6):1495-1514.
[51]BOZER A Y. Throughput performance of automated storage/retrieval systems under stochastic demand[J]. IIE Transactions,2005,37(4):367-378.
[52]ROODBERGEN K J, VIS I F A. A survey of literature on automated storage and retrieval systems [J]. European Journal of Operational Research,2009, 194(2):343-362.
[53]HAN M H, MCGINNIS L F, SHIEH J S, et al. On sequenceing retrievals in an automated storage/retrieval system[J]. IIE Transactions,1987,19(1):56-66.
[54]LEE H F, SCHAEFER S K. Retrieval sequencing for unit-load automated storage and retrieval systems with multiple openings [J]. International Journal of Production Research,1996,34(10):2943-2962.
[55]WANG J Y, YIH Y. Using neural networks to select a control strategy for automated storage and retrieval systems (AS/RS)[J]. International Journal of Computer Integrated Manufacturing,1997,10(6):487-495.
[56]LINN R J, XIE X D. A simulation analysis of sequencing rules for AS/RS in a pull-based assembly facility [J]. International Journal of Production Research, 1993,31(10):2355-2367.
[57]LINN R J, WYSK R A. An expert system based controller for an automated storage/retrieval system [J]. International Journal of Production Research,1990, 28(4):735-756.
[58]SEIDMANN A. Intelligent control schemes for automated storage and retrieval systems[J]. International Journal of Production Research,1988,26(5): 931-952.
[59]KRISHNAIAH CHETTY O V, SARVESWAR REDDY M. Genetic algorithms for studies on AS/RS integrated with machines [J]. International Journal of Advanced Manufacturing Technology,2003,22(3):932-940.
[60]LIM J M, KIM K S, YUM B J, Hwang H. Determination of an optimal configuration of operating policies for direct-input-output manufacturing systems using the taguchi method [J]. Computers & Industrial Engineering, 1996,31(3):555-560.
[61]LITVAK N. Optimal picking of large orders in carousel systems[J]. Operations Research Letters,2006,34(2):219-227.
[62]VAN DEN BERG J P. Multiple order pick sequencing in a carousel system:A solvable case of the rural postman problem.[J]. Journal of the Operational Research Society,1996,47(1):1504-1515.
[63]LITAK N, ADAN I. The travel time in carousel systems under the nearest item heuristic[J]. Journal of Applied Probability,2001,38(1):45-54.
[64]屈援,王雪莲.自动化仓库旋转货架拣选优化[J].起重运输机械,2007(9)：20-22.
[65]张攀,田国会,贾磊等.旋转货架拣选作业优化问题的新型混合遗传算法求解[J].机械工程学报,2004,40(6)：34-38.
[66]GRAY A, KARMARKAR U, SEIDMANN A. Design and operation of an order-consolidation warehouse:Models and applications [J]. European Journal of Operational Research 1992,58(1):14-36.
[67]DE KOSTER R. Performance approximation of pick-to-belt order picking systems[J]. European Journal of Operational Research,1994,72(3):558-573.
[68]LE-DUC T, DE KOSTER R, Determining number of zones in a pick-and-pack orderpicking system [M]. Rotterdam:Erasmus Research Institute of Management (ERIM),2005.
[69]PARK B C, FRAZELLE E H, WHITE J A. Buffer sizing models for end-of-aisle order picking systems [J]. IIE Transactions,1999,31(3):31-38.
[70]GUENOV M, RAESIDE R. Zone shapes in class based storage and multicommand order picking when storage/retrieval machines are used[J]. European Journal of Operational Research,1992,58(1):37-47.
[71]BARTHOLDI Ⅲ J J, HACKMAN S T, Warehouse and distribution science[M]. Atlanta:Georgia Institute of Technology,2008.
[72]BARTHOLDI Ⅲ J J, BUNIMOVICH L A, EISENSTEIN D D. Dynamics of two- and three-worker "bucket brigade" production lines[J]. Operations Research,1999,47(3):488-491.
[73]BARTHOLDI Ⅲ J J, EISENSTEIN D D, FOLEY R D. Performance of bucket brigades when work is stochastic[J]. Operations Research,2001,49(3): 710-719.
[74]BARTHOLDI Ⅲ J J, EISENSTEIN D D, LIM Y F. Bucket brigades on in-tree assembly networks[C]. Balancing Assembly and Transfer Lines, Amsterdam: Elsevier,2006:870-879.
[75]ZHANG Y G, WU Y H. Research of efficiency optimization in tobacco automated sorting system[C]. Proceedings of the 2007 International Conference on Automation and Logistics, New Jersey:IEEE,2007: 1209-1213.
[76]WU Y H, ZHANG Y G. Order-picking optimization for automated picking system with parallel dispensers [J]. Chinese Journal of Mechanical Engineering (English Edition),2008,21(6):25-29.
[77]张贻弓,林茂.卷烟分拣设备的选型与优化[J].物流技术与应用,2008(9)：86-87.
[78]LIU P, ZHOU C, WU Y H, et al. Slotting the complex automated picking system in tobacco distribution center[C]. Proceedings of the 2008 International Conference on Automation and Logistics, New Jersey:IEEE,2008: 2126-2130.
[79]刘德宝.复合式卷烟分拣系统研究与设计[D].济南：山东大学,2006.
[80]李静.卷烟配送中心自动分拣系统配置与优化研究[D].济南：山东大学,2009.
[81]张小勇.卷烟物流配送中心的规划与设计[J].物流技术与应用,2006,11(5)：82-85.
[82]孙磊,吴耀华,张冠女.动态EIQ_ABC分析在配送中心规划中的应用[J].山东大学学报(工学版),2007,37(3)：81-85.
[83]冯华威,李云军.AHP在配送中心分拣策略中的应用[J].商品储运与养护,2007,29(6)：88-89.
[84]LIU P, WU Y, XU N. Assigning SKUs to multiple automated-picking areas over multiple periods[C]. Proceedings of the 2009 International Conference on Automation and Logistics, New Jersey:IEEE,2009:50-55.
[85]肖际伟.复合式卷烟分拣系统分拣机组合优化[J].系统工程理论与实践,2010,30(2)：251-256.
[86]秦峰华.卷烟自动分拣机的性能优化与实现[D].济南：山东大学控制科学与工程学院,2007.
[87]张鹏,鞠镁隆,沈敏德,范纬华.基于PLC控制的卧式卷烟自动分拣机设计[J].机电一体化,2008(5)：77-79.
[88]范启印,马俊骑,冉文学等.虚拟视窗算法在成品烟自动分拣控制中的应用[J].昆明理工大学学报：理工版,2004,29(1)：65-72.
[89]赵凯,熊红云,戴建民等.基于虚拟容器队列的条烟自动分拣控制算法研究[J].计算机工程与科学,2008,30(2)：67-69.
[90]WU Y H, ZHANG Y G, WU Y Y. Compressible virtual window algorithm in picking process control of automated sorting system[J]. Chinese Journal of Mechanical Engineering (English Edition),2008,21(3):41-45.
[91]张贻弓,吴耀华.可合流的自动拣选系统订单排序优化[J].山东大学学报(工学版),2008,38(5)：67-71.
[92]张贻弓,吴耀华.双拣货区自动分拣系统品项分配优化[J].机械工程学报,2009,45(11)：152-157.
[93]卢少平,张贻弓,吴耀华等.自动分拣系统并行分区拣选优化策略[J].深圳大学学报理工版,2010,27(1)：120-126.
[94]姚恩瑜,何勇,陈仕平,数学规划与组合优化[M].杭州：浙江大学出版社,2001.
[95]鲍江宏,李炯城.基于遗传算法的集合划分问题求解[J].计算机工程与设计,2008,29(11)：2879-2882.
[96]HAN J, KAMBER M, Data mining:Concepts and techniques (second edition)[M]. San Francisco:Morgan Kaufmann,2006.
[97]MACQUEEN J, Some methods for classification and analysis of multivariate observations[M]. Berkley:University of California,1967.
[98]孙磊,吴耀华,张冠女.动态EIQ-ABC分析在配送中心规划中的应用[J].山东大学学报(工学版)2007,37(3)：81-85,113.
[99]刘昌祺,物流配送中心拣货系统选择及设计[M].北京：机械工业出版社,2005.
[100]杨玮,刘昌祺,曹巨江,彭国勋.物流配送中心拣货系统分析[J].包装工程,2005,26(1)：107-109.
[101]SARTAJ S,数据结构算法与应用C++语言描述[M].北京：机械工业出版社,2000.
[2]肖际伟.配送中心拣货系统优化[D].济南：山东大学,2010.
[3]王转,程国全,配送中心系统规划[M].北京：中国物资出版社,2003.
[4]KHACHATRYAN M. Small parts high volume order picking systems[D]. Georgia:Georgia Institute of Technology,2006.
[5]GOETSCHALCKX M, ASHAYERI J. Classification and design of order picking[J]. Logistics Information Management,1989,2(2):99-106.
[6]TOMPKINS J A, WHITE J A, BOZER Y A, et al., Facilities planning[M]. New Jersey:John Wiley & Sons,2003.
[7]王国华,中国现代物流大全C卷：现代物流技术与装备[M].北京：中国铁道出版社,2004.
[8]DE KOSTER R, LE-DUC T, ROODBERGEN K J. Design and control of warehouse order picking:A literature review[J]. European Journal of Operational Research,2007,182(2):481-501.
[9]吴耀华.利用高科技构建节约型物流配送系统[J].物流技术,2007,26(8)：5-6.
[10]黄启明.自动分拣系统及其应用前景分析[J].物流技术,2002(5)：7-8.
[11]DALLARI F, MARCHET G, MELACINI M. Design of order picking system[J]. The International Journal of Advanced Manufacturing Technology, 2009,42(1):1-12.
[12]GU J X, GOETSCHALCKX M, MCGINNIS L F. Research on warehouse operation:A comprehensive review[J]. European Journal of Operational Research,2007,177(1):1-21.
[13]ROUWENHORST B, REUTER B, STOCKRAHM V, et al. Warehouse design and control:Framework and literature review[J]. European Journal of Operational Research,2000,122(3):515-533.
[14]VAN DEN BERG J P. A literature survey on planning and control of warehousing systems[J]. IIE Transactions,1999,31(8):751-762.
[15]谢伟东,王磊,佘翊妮等.条烟自动分拣的异步法及其装置[J].工程设计学报,2007,14(2)：112-116.
[16]鞠镁隆,沈敏德.卷烟自动分拣机的创新设计[J].轻工机械,2006,24(1)：123-125.
[17]张鹏,沈敏德,范维华.基于Profibus-DP的塔式卷烟自动分拣机的设计应用[J].烟草科技,2007,36(3)：23-24.
[18]TSAI C-Y, LIOU J J H, HUANG T-M. Using a multiple-ga method to solve the batch picking problem:Considering travel distance and order due time[J]. International Journal of Production Research,2008,46(22):6533-6555.
[19]HESKETT J L. Cube-per-order index-a key to warehouse stock location[J]. Transportation and Distribution Management,1963(3):27-31.
[20]HESKETT J L. Putting the cube-per-order index to work in warehouse layout[J]. Transport and Distribution Management,1964,4(8):23-30.
[21]KALLINA C, LYNN J. Application of the cube-per-order index rule for stock location in a distribution warehouse[J]. Indterfaces,1976,7(1):37-46.
[22]GARFINKEL M. Minimizing multi-zone orders in the correlated storage assignment problem[D]. Georgia:Georgia Institute of Technology,2005.
[23]FRAZELLE E H. Stock location assignment and order picking productivity[D]. Georgia:Georgia Institute of Technology,1989.
[24]XIAO J, ZHENG L. A correlated storage location assignment problem in a single-block-multi-aisles warehouse considering bom information[J]. International Journal of Production Research,2010,48(5):1321-1338.
[25]肖建,郑力.考虑需求相关性的多巷道仓库货位分配问题[J].计算机集成制造系统,2008,14(12)：2447-2451.
[26]GAREY M R, JOHNSON D S, Computers and intractability:A guide to the theory of np-completeness[M]. New York:W. H. Freeman and Company, 1979.
[27]ANDERBERG M R, Cluster analysis for applications[M]. New York: Academic Press,1973.
[28]SADIQ M. A hybrid clustering algorithm for reconfiguration of dynamic order picking systems[D]. Arkansas:University of Arkansas,1995.
[29]SADIQ M, LANDERS T L, TAYLOR G D. An assignment algorithm for dynamic picking systems[J]. IIE Transactions,1996,28(8):607.
[30]LEE M. A storage assignment policy in a man-on-board automated storage-retrieval system[J]. International Journal of Production Research,1992, 30(10):2281-2292.
[31]LIU C M. Clustering techniques for stock location and order-picking in a distribution center[J]. Computers & Operations Research,1999,26(10-11): 989-1002.
[32]HO Y C, WEE H, CHEN H. A geometric design of zone-picking in a distribution warehouse[C]. Proceedings of the 2007 international conference on Computational science and its applications, Heidelberg:Springer-Verlag, 2007:625-636.
[33]SARKER B R. The resemblance coefficients in group technology:A survey and comparative study of relational metrics[J]. Computers & Industrial Engineering,1996,30(1):103-116.
[34]臧照良.成组技术基础上的CAPP[J].机械制造与自动化,2006,35(5)：91-93.
[35]ROMESBURG H C, Cluster analysis for researchers[M]. North Carolina: Lulu Press,1989.
[36]HO Y C, CHIEN S P. A comparison of two zone-visitation sequencing strategies in a distribution centre[J]. Computers & Industrial Engineering, 2006,50(4):426-439.
[37]AMIRHOSSEINI M M, SHARP G P. Simultaneous analysis of products and orders in storage assignment[J]. Manufacturing Science and Engineering, 1996(4):803-811.
[38]WEI H, CHEN Z. Clusters and filling-curve-based storage assignment in a circuit board assembly kitting area[J]. IIE Transactions,2008,40(1):569-585.
[39]HUA W. Cluster based storage policies in kitting area[D]. Georgia:Georgia Institute of Technology,2001.
[40]JANE C C, LAIH Y W. A clustering algorithm for item assignment in a synchronized zone order picking system[J]. European Journal of Operational Research,2005,166(2):489-496.
[41]LAWLER E L, LENSTRA J K, RINNOOY KAN A H G, SHMOYS D B. The traveling salesman problem[M]. Chichester:Wiley,1995.
[42]ASCHEUER N, GROTSCHEL M, ABDEL-HALIM E S. Order picking in an automatic warehouse:Solving online asymmetric TSPs[J]. Mathematical Methods of Operations Research,1999,49(4):501-515.
[43]DANIELS R L, RUMMEL J L, SCHANTZ R. A model for warehouse order picking[J]. European Journal of Operational Research,1998,105(1):1-17.
[44]RATLIFF H D, ROSENTHAL A S. Orderpicking in a rectan-gular warehouse: A solvable case of the traveling salesman problem[J]. Operations Research, 1983,31(3):507-521.
[45]CORNUE'JOLS G, FONLUPT J, NADDEF D. The traveling salesman problem on a graph and some related integer polyhedra[J]. Mathematical Programming,1985,33(2):1-27.
[46]DE KOSTER R, VAN DE POORT E S. Routing orderpickers in a warehouse: Acomparison between optimal and heuristic solutions[J]. HE Transactions, 1998,30(1):469-480.
[47]ROODBERGEN K J. Layout and routing methods for warehouses[D]. Netherland:RSM Erasmus University,2001.
[48]HALL R W. Distance approximation for routing manual pickers in a warehouse[J]. HE Transactions,1993,25(2):77-87.
[49]PETERSEN C G. An evaluation of order picking routing policies [J]. International Journal of Operations & Production Management,1997,17(11): 1098-1111.
[50]DE KOSTER R B M, LE-DUC T, YUGANG Y. Optimal storage rack design for a 3-dimensional compact AS/RS[J]. International Journal of Production Research,2008,46(6):1495-1514.
[51]BOZER A Y. Throughput performance of automated storage/retrieval systems under stochastic demand[J]. IIE Transactions,2005,37(4):367-378.
[52]ROODBERGEN K J, VIS I F A. A survey of literature on automated storage and retrieval systems [J]. European Journal of Operational Research,2009, 194(2):343-362.
[53]HAN M H, MCGINNIS L F, SHIEH J S, et al. On sequenceing retrievals in an automated storage/retrieval system[J]. IIE Transactions,1987,19(1):56-66.
[54]LEE H F, SCHAEFER S K. Retrieval sequencing for unit-load automated storage and retrieval systems with multiple openings [J]. International Journal of Production Research,1996,34(10):2943-2962.
[55]WANG J Y, YIH Y. Using neural networks to select a control strategy for automated storage and retrieval systems (AS/RS)[J]. International Journal of Computer Integrated Manufacturing,1997,10(6):487-495.
[56]LINN R J, XIE X D. A simulation analysis of sequencing rules for AS/RS in a pull-based assembly facility [J]. International Journal of Production Research, 1993,31(10):2355-2367.
[57]LINN R J, WYSK R A. An expert system based controller for an automated storage/retrieval system [J]. International Journal of Production Research,1990, 28(4):735-756.
[58]SEIDMANN A. Intelligent control schemes for automated storage and retrieval systems[J]. International Journal of Production Research,1988,26(5): 931-952.
[59]KRISHNAIAH CHETTY O V, SARVESWAR REDDY M. Genetic algorithms for studies on AS/RS integrated with machines [J]. International Journal of Advanced Manufacturing Technology,2003,22(3):932-940.
[60]LIM J M, KIM K S, YUM B J, Hwang H. Determination of an optimal configuration of operating policies for direct-input-output manufacturing systems using the taguchi method [J]. Computers & Industrial Engineering, 1996,31(3):555-560.
[61]LITVAK N. Optimal picking of large orders in carousel systems[J]. Operations Research Letters,2006,34(2):219-227.
[62]VAN DEN BERG J P. Multiple order pick sequencing in a carousel system:A solvable case of the rural postman problem.[J]. Journal of the Operational Research Society,1996,47(1):1504-1515.
[63]LITAK N, ADAN I. The travel time in carousel systems under the nearest item heuristic[J]. Journal of Applied Probability,2001,38(1):45-54.
[64]屈援,王雪莲.自动化仓库旋转货架拣选优化[J].起重运输机械,2007(9)：20-22.
[65]张攀,田国会,贾磊等.旋转货架拣选作业优化问题的新型混合遗传算法求解[J].机械工程学报,2004,40(6)：34-38.
[66]GRAY A, KARMARKAR U, SEIDMANN A. Design and operation of an order-consolidation warehouse:Models and applications [J]. European Journal of Operational Research 1992,58(1):14-36.
[67]DE KOSTER R. Performance approximation of pick-to-belt order picking systems[J]. European Journal of Operational Research,1994,72(3):558-573.
[68]LE-DUC T, DE KOSTER R, Determining number of zones in a pick-and-pack orderpicking system [M]. Rotterdam:Erasmus Research Institute of Management (ERIM),2005.
[69]PARK B C, FRAZELLE E H, WHITE J A. Buffer sizing models for end-of-aisle order picking systems [J]. IIE Transactions,1999,31(3):31-38.
[70]GUENOV M, RAESIDE R. Zone shapes in class based storage and multicommand order picking when storage/retrieval machines are used[J]. European Journal of Operational Research,1992,58(1):37-47.
[71]BARTHOLDI Ⅲ J J, HACKMAN S T, Warehouse and distribution science[M]. Atlanta:Georgia Institute of Technology,2008.
[72]BARTHOLDI Ⅲ J J, BUNIMOVICH L A, EISENSTEIN D D. Dynamics of two- and three-worker "bucket brigade" production lines[J]. Operations Research,1999,47(3):488-491.
[73]BARTHOLDI Ⅲ J J, EISENSTEIN D D, FOLEY R D. Performance of bucket brigades when work is stochastic[J]. Operations Research,2001,49(3): 710-719.
[74]BARTHOLDI Ⅲ J J, EISENSTEIN D D, LIM Y F. Bucket brigades on in-tree assembly networks[C]. Balancing Assembly and Transfer Lines, Amsterdam: Elsevier,2006:870-879.
[75]ZHANG Y G, WU Y H. Research of efficiency optimization in tobacco automated sorting system[C]. Proceedings of the 2007 International Conference on Automation and Logistics, New Jersey:IEEE,2007: 1209-1213.
[76]WU Y H, ZHANG Y G. Order-picking optimization for automated picking system with parallel dispensers [J]. Chinese Journal of Mechanical Engineering (English Edition),2008,21(6):25-29.
[77]张贻弓,林茂.卷烟分拣设备的选型与优化[J].物流技术与应用,2008(9)：86-87.
[78]LIU P, ZHOU C, WU Y H, et al. Slotting the complex automated picking system in tobacco distribution center[C]. Proceedings of the 2008 International Conference on Automation and Logistics, New Jersey:IEEE,2008: 2126-2130.
[79]刘德宝.复合式卷烟分拣系统研究与设计[D].济南：山东大学,2006.
[80]李静.卷烟配送中心自动分拣系统配置与优化研究[D].济南：山东大学,2009.
[81]张小勇.卷烟物流配送中心的规划与设计[J].物流技术与应用,2006,11(5)：82-85.
[82]孙磊,吴耀华,张冠女.动态EIQ_ABC分析在配送中心规划中的应用[J].山东大学学报(工学版),2007,37(3)：81-85.
[83]冯华威,李云军.AHP在配送中心分拣策略中的应用[J].商品储运与养护,2007,29(6)：88-89.
[84]LIU P, WU Y, XU N. Assigning SKUs to multiple automated-picking areas over multiple periods[C]. Proceedings of the 2009 International Conference on Automation and Logistics, New Jersey:IEEE,2009:50-55.
[85]肖际伟.复合式卷烟分拣系统分拣机组合优化[J].系统工程理论与实践,2010,30(2)：251-256.
[86]秦峰华.卷烟自动分拣机的性能优化与实现[D].济南：山东大学控制科学与工程学院,2007.
[87]张鹏,鞠镁隆,沈敏德,范纬华.基于PLC控制的卧式卷烟自动分拣机设计[J].机电一体化,2008(5)：77-79.
[88]范启印,马俊骑,冉文学等.虚拟视窗算法在成品烟自动分拣控制中的应用[J].昆明理工大学学报：理工版,2004,29(1)：65-72.
[89]赵凯,熊红云,戴建民等.基于虚拟容器队列的条烟自动分拣控制算法研究[J].计算机工程与科学,2008,30(2)：67-69.
[90]WU Y H, ZHANG Y G, WU Y Y. Compressible virtual window algorithm in picking process control of automated sorting system[J]. Chinese Journal of Mechanical Engineering (English Edition),2008,21(3):41-45.
[91]张贻弓,吴耀华.可合流的自动拣选系统订单排序优化[J].山东大学学报(工学版),2008,38(5)：67-71.
[92]张贻弓,吴耀华.双拣货区自动分拣系统品项分配优化[J].机械工程学报,2009,45(11)：152-157.
[93]卢少平,张贻弓,吴耀华等.自动分拣系统并行分区拣选优化策略[J].深圳大学学报理工版,2010,27(1)：120-126.
[94]姚恩瑜,何勇,陈仕平,数学规划与组合优化[M].杭州：浙江大学出版社,2001.
[95]鲍江宏,李炯城.基于遗传算法的集合划分问题求解[J].计算机工程与设计,2008,29(11)：2879-2882.
[96]HAN J, KAMBER M, Data mining:Concepts and techniques (second edition)[M]. San Francisco:Morgan Kaufmann,2006.
[97]MACQUEEN J, Some methods for classification and analysis of multivariate observations[M]. Berkley:University of California,1967.
[98]孙磊,吴耀华,张冠女.动态EIQ-ABC分析在配送中心规划中的应用[J].山东大学学报(工学版)2007,37(3)：81-85,113.
[99]刘昌祺,物流配送中心拣货系统选择及设计[M].北京：机械工业出版社,2005.
[100]杨玮,刘昌祺,曹巨江,彭国勋.物流配送中心拣货系统分析[J].包装工程,2005,26(1)：107-109.
[101]SARTAJ S,数据结构算法与应用C++语言描述[M].北京：机械工业出版社,2000.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |