分区自动拣选系统拣选策略优化研究
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摘要
随着客户订单的逐渐多样化和客户服务要求的逐步提高,现代物流配送中心对订单处理时间提出了更高的要求。拣选作业作为订单处理过程中的重要环节,其作业效率是衡量配送中心整体效率的关键。据统计,拣选作业时间占物流配送中心作业总时间的35%左右。为有效减少拣选作业时间,越来越多行业采用自动拣选系统代替人工拣选系统。相对人工拣选系统,自动拣选系统作业效率高、差错率低,适用于小批量、多品种的货物拣选。
分拣机系统因其高时效、占地面积小等特点成为应用最广泛的一种自动拣选系统。多台分拣机通常被划分至多个拣选区内,各拣选区可以同时对同一订单并行拣选,有效减少订单处理时间。因此,分区划分后的自动拣选系统即分区自动拣选系统应用越来越普遍,而系统优化问题,成为提高配送中心作业效率的关键因素。
目前国内外对于拣选系统的研究大多集中在人工拣选领域,与自动拣选系统相关的文献较少。为数不多的自动拣选系统领域的文献,也多集中在分拣机的改造和设备选型方面,较少涉及拣选策略优化。基于此,本文总结对分区自动拣选系统作业效率影响较大的三大拣选策略：品项分配策略、分区合流顺序分配策略和订单拣选顺序分配策略。以订单处理总时间最小化为目标,运用禁忌搜索法、动态聚类法、贪婪算法、动态规划法、遗传算法等方法对以上三大拣选策略进行优化。本文的主要研究内容和成果如下：
(1)分析分区自动拣选系统工作流程,建立并行拣选、串行合流模式下拣选系统的数学模型。
在分区自动拣选系统数学模型中,将订单处理总时间分为两部分：合流时间和延迟时间。其中,合流时间由设备性能及客户订单决定,在设备稳定的情况下为常量；延迟时间由当前订单及前一订单的订单结构决定,为变量。在分析系统模型的基础上,总结订单处理总时间的影响因素。
(2)品项分配子问题中,将优化目标由订单处理总时间最小转化为延迟因子总和最小,设计两种算法对模型进行求解。
首先提出延迟因子表示当前订单某一拣选区在某一特殊情况下的延迟时间。特殊情况的表现为前一订单各拣选区和当前订单前面拣选区延迟时间均为零。通过理论证明,拣选区延迟因子与延迟时间具有相同的变化趋势,因此可利用延迟因子对模型进行求解,降低模型求解难度。
根据各拣选区品项数量是否固定,分别提出基于品项交换的禁忌搜索算法和基于品项转移的动态聚类算法。最后通过实例仿真证明两种算法的有效性。
(3)分区合流顺序优化子问题中,分析分区合流顺序变化必要条件,并以此提出贪婪启发式算法求解模型。
首先分析分区合流顺序对订单处理总时间的影响,以订单处理总时间最小为目标,建立以分区合流顺序为变量的系统模型。将此模型抽象为一般系统模型1(分组)|rη=c(i-1)j+tη|Cmax'并运用集合划分理论证明该问题为NP-hard司题。
提出分区合流顺序变化而使订单处理总时间减少的必要条件,并以此为基础,设计贪婪启发式算法与动态规划法相结合对模型进行求解。仿真显示算法可大幅减小系统订单处理总时间,提高作业效率。
(4)订单拣选顺序优化子问题中,运用自适应遗传算法进行模型求解,并对算法进行改进。
首先从理论、实例两方面分析订单拣选顺序对订单处理总时间的影响,以订单拣选顺序为变量、订单处理总时间最小为目标建立系统模型,并将模型简化为TSP问题。提出改进的自适应遗传算法对模型进行求解。其中,算法的改进部分体现在两个方面：一、初始种群的生成过程中：引进海明距离表示种群中两个个体的差异,并选取海明距离较大的个体生成初始种群,以防止陷入局部最优解。二、改进交叉概率和变异概率：当进化过程处于“停滞”状态时,增加较优个体的交叉和变异概率,避免陷入局部最优。最后通过实验证明算法的优越性。
The orders become more and more diversified and the demand of customer service becomes higher and higher in the modern logistics distribution center. All of above need a faster order fulfillment time. Picking is an important operation in the order handling process and the working efficiency of the picking operation is the key factor which affects the total working effieicncy of the distribution center. According to statistics, the picking time accounts for about35%of the total order fulfillment time. Therefore, more and more distribution centers become to apply automated order picking system. Compared to manual order picking system, the automated order picking system is faster and less prone to mistakes, so it is suitable for the order in which there are many kinds of SKU and the total number of SKUs is small.
The dispenser system is widely used in the distribution center among all kinds of automated order picking system since it is faster and needs less space. Usually, the dispensers are divided into several zones, so different zones can pick one order simutaniously and the order picking time will be reduced. Therefore, the research on the zone automated order picking system will have a great significance.
However, most researchers focus on the manual order picking system as so far, the literatures about the automated order picking system are much fewer. Worse than that, most literatures about the automated order picking system focus on the mechanical improvement and the equipment selection, little refers to the optimization of picking strategy. Because of all above reasons, this paper concludes three picking strategies which have most important impact on the working efficiency of the zone automated order picking system:item assignment strategy, merging sequence assignment strategy and the order picking sequence assignment strategy. Based on this, the tabu search algorithm, dynamic cluster algorithm, greedy heuristic algorithm, dynamic programming method and genetic algorithm are used to optimize the strategies to minimize the order fulfillment time. The main content and the achievement of this paper are as follows.
(1) Analyze the working process of the zone automated order picking system, builds the mathematical model of the system based on parallel picking and serial merging method.
In the model of the zone automated order picking system, the order picking time is divided into two parts:merging time and delay time. The merging time is decided by parameters of the equipment and the number of SKUs in the current order. If the system can run steady, the merging time is a constant; whereas the delay time is a variable since it is decided by the order structure of the current order and prior order.
The impact factors which will influence the order picking time are given based on the model of the system.
(2) Considering the sub problem of item assignment, converts the optimizing object from minimizing order picking time to minimizing the summation of the delay time, and then design two algorithms to solve the problem.
First, the delay factor is proposed to represent the delay time under a special situation. Under the special situation, the delay times of the following zones in the prior order and prior zones in the current order are zero. The delay factor is proven to have the same vary trend with the delay time for each zone, so it can be used to solve the model of the sub problem to simply the complexity.
The tabu search algorithm based on item exchange and the dynamic cluster algorithm based on item shift are proposed respectively aimed at whether the number of kind of SKUs is fixed in each zone. The simulation verified the validity of the two algorithms.
(3) Considering the sub problem of merging sequence optimization, the necessary condition of changing the merging sequence is proposed firstly, and then the greedy heuristic algorithm based on the necessary condition is proposed to solve the problem.
First, we analyze the impact of different merging sequences on the order picking time, and then build the system model regarding the merging sequences of zones as variables. The model is abstracted as a general system model and expressed as 1(group)|rη=c(r-1)j+tη|Cmax. This model is proven to be a NP-hard model with3-partition theory.
The necessary condition of changing the merging sequence of a zone is proposed, and then a greedy heuristics algorithm combined with the dynamic programming method is designed to solve the model. The simulation result shows the order picking time can be reduced and picking efficiency is improved by the algorithm.
(4) Regarding sub problem of the order picking sequence optimization, we improve the self-adaptive genetic algorithm to solve it.
First, we analyze the impact of the order picking sequence on the order picking time, and then build the mathematical model regarding the order picking sequence as variable. This model can be simpled to be TSP problem model. At last, the improved self-adative genetic algorithm is proposed to solve the model. There are two differences between the proposed algorithm and the traditional self-adaptive genetic algorithm:First, the proposed algorithm brings HD distance into it. The HD distance of two genes represents the degree of difference between them. We choose the genes which have longer HD distance with other genes to form the initial solutions group. This will help avoiding local best solution. Second, the proposed algorithm improves the crossing probability and the variation probality. When the evolution process is stopped, increasing the crossing probability and the variation probability of the better genes will help avoiding local best solution. The simulation result shows the validity of the proposed algorithm.
分拣机系统因其高时效、占地面积小等特点成为应用最广泛的一种自动拣选系统。多台分拣机通常被划分至多个拣选区内,各拣选区可以同时对同一订单并行拣选,有效减少订单处理时间。因此,分区划分后的自动拣选系统即分区自动拣选系统应用越来越普遍,而系统优化问题,成为提高配送中心作业效率的关键因素。
目前国内外对于拣选系统的研究大多集中在人工拣选领域,与自动拣选系统相关的文献较少。为数不多的自动拣选系统领域的文献,也多集中在分拣机的改造和设备选型方面,较少涉及拣选策略优化。基于此,本文总结对分区自动拣选系统作业效率影响较大的三大拣选策略：品项分配策略、分区合流顺序分配策略和订单拣选顺序分配策略。以订单处理总时间最小化为目标,运用禁忌搜索法、动态聚类法、贪婪算法、动态规划法、遗传算法等方法对以上三大拣选策略进行优化。本文的主要研究内容和成果如下：
(1)分析分区自动拣选系统工作流程,建立并行拣选、串行合流模式下拣选系统的数学模型。
在分区自动拣选系统数学模型中,将订单处理总时间分为两部分：合流时间和延迟时间。其中,合流时间由设备性能及客户订单决定,在设备稳定的情况下为常量；延迟时间由当前订单及前一订单的订单结构决定,为变量。在分析系统模型的基础上,总结订单处理总时间的影响因素。
(2)品项分配子问题中,将优化目标由订单处理总时间最小转化为延迟因子总和最小,设计两种算法对模型进行求解。
首先提出延迟因子表示当前订单某一拣选区在某一特殊情况下的延迟时间。特殊情况的表现为前一订单各拣选区和当前订单前面拣选区延迟时间均为零。通过理论证明,拣选区延迟因子与延迟时间具有相同的变化趋势,因此可利用延迟因子对模型进行求解,降低模型求解难度。
根据各拣选区品项数量是否固定,分别提出基于品项交换的禁忌搜索算法和基于品项转移的动态聚类算法。最后通过实例仿真证明两种算法的有效性。
(3)分区合流顺序优化子问题中,分析分区合流顺序变化必要条件,并以此提出贪婪启发式算法求解模型。
首先分析分区合流顺序对订单处理总时间的影响,以订单处理总时间最小为目标,建立以分区合流顺序为变量的系统模型。将此模型抽象为一般系统模型1(分组)|rη=c(i-1)j+tη|Cmax'并运用集合划分理论证明该问题为NP-hard司题。
提出分区合流顺序变化而使订单处理总时间减少的必要条件,并以此为基础,设计贪婪启发式算法与动态规划法相结合对模型进行求解。仿真显示算法可大幅减小系统订单处理总时间,提高作业效率。
(4)订单拣选顺序优化子问题中,运用自适应遗传算法进行模型求解,并对算法进行改进。
首先从理论、实例两方面分析订单拣选顺序对订单处理总时间的影响,以订单拣选顺序为变量、订单处理总时间最小为目标建立系统模型,并将模型简化为TSP问题。提出改进的自适应遗传算法对模型进行求解。其中,算法的改进部分体现在两个方面：一、初始种群的生成过程中：引进海明距离表示种群中两个个体的差异,并选取海明距离较大的个体生成初始种群,以防止陷入局部最优解。二、改进交叉概率和变异概率：当进化过程处于“停滞”状态时,增加较优个体的交叉和变异概率,避免陷入局部最优。最后通过实验证明算法的优越性。
The orders become more and more diversified and the demand of customer service becomes higher and higher in the modern logistics distribution center. All of above need a faster order fulfillment time. Picking is an important operation in the order handling process and the working efficiency of the picking operation is the key factor which affects the total working effieicncy of the distribution center. According to statistics, the picking time accounts for about35%of the total order fulfillment time. Therefore, more and more distribution centers become to apply automated order picking system. Compared to manual order picking system, the automated order picking system is faster and less prone to mistakes, so it is suitable for the order in which there are many kinds of SKU and the total number of SKUs is small.
The dispenser system is widely used in the distribution center among all kinds of automated order picking system since it is faster and needs less space. Usually, the dispensers are divided into several zones, so different zones can pick one order simutaniously and the order picking time will be reduced. Therefore, the research on the zone automated order picking system will have a great significance.
However, most researchers focus on the manual order picking system as so far, the literatures about the automated order picking system are much fewer. Worse than that, most literatures about the automated order picking system focus on the mechanical improvement and the equipment selection, little refers to the optimization of picking strategy. Because of all above reasons, this paper concludes three picking strategies which have most important impact on the working efficiency of the zone automated order picking system:item assignment strategy, merging sequence assignment strategy and the order picking sequence assignment strategy. Based on this, the tabu search algorithm, dynamic cluster algorithm, greedy heuristic algorithm, dynamic programming method and genetic algorithm are used to optimize the strategies to minimize the order fulfillment time. The main content and the achievement of this paper are as follows.
(1) Analyze the working process of the zone automated order picking system, builds the mathematical model of the system based on parallel picking and serial merging method.
In the model of the zone automated order picking system, the order picking time is divided into two parts:merging time and delay time. The merging time is decided by parameters of the equipment and the number of SKUs in the current order. If the system can run steady, the merging time is a constant; whereas the delay time is a variable since it is decided by the order structure of the current order and prior order.
The impact factors which will influence the order picking time are given based on the model of the system.
(2) Considering the sub problem of item assignment, converts the optimizing object from minimizing order picking time to minimizing the summation of the delay time, and then design two algorithms to solve the problem.
First, the delay factor is proposed to represent the delay time under a special situation. Under the special situation, the delay times of the following zones in the prior order and prior zones in the current order are zero. The delay factor is proven to have the same vary trend with the delay time for each zone, so it can be used to solve the model of the sub problem to simply the complexity.
The tabu search algorithm based on item exchange and the dynamic cluster algorithm based on item shift are proposed respectively aimed at whether the number of kind of SKUs is fixed in each zone. The simulation verified the validity of the two algorithms.
(3) Considering the sub problem of merging sequence optimization, the necessary condition of changing the merging sequence is proposed firstly, and then the greedy heuristic algorithm based on the necessary condition is proposed to solve the problem.
First, we analyze the impact of different merging sequences on the order picking time, and then build the system model regarding the merging sequences of zones as variables. The model is abstracted as a general system model and expressed as 1(group)|rη=c(r-1)j+tη|Cmax. This model is proven to be a NP-hard model with3-partition theory.
The necessary condition of changing the merging sequence of a zone is proposed, and then a greedy heuristics algorithm combined with the dynamic programming method is designed to solve the model. The simulation result shows the order picking time can be reduced and picking efficiency is improved by the algorithm.
(4) Regarding sub problem of the order picking sequence optimization, we improve the self-adaptive genetic algorithm to solve it.
First, we analyze the impact of the order picking sequence on the order picking time, and then build the mathematical model regarding the order picking sequence as variable. This model can be simpled to be TSP problem model. At last, the improved self-adative genetic algorithm is proposed to solve the model. There are two differences between the proposed algorithm and the traditional self-adaptive genetic algorithm:First, the proposed algorithm brings HD distance into it. The HD distance of two genes represents the degree of difference between them. We choose the genes which have longer HD distance with other genes to form the initial solutions group. This will help avoiding local best solution. Second, the proposed algorithm improves the crossing probability and the variation probality. When the evolution process is stopped, increasing the crossing probability and the variation probability of the better genes will help avoiding local best solution. The simulation result shows the validity of the proposed algorithm.
引文
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