A storage assignment heuristic method based on genetic algorithm for a pick-and-pass warehousing system

An order storage assignment problem (SAP) is to find an effective way to locate products in a warehouse in order to improve the operational efficiency of order picking. Since SAP is an NP-hard problem, many heuristic algorithms have been proposed. Most of previous researches focused on picker-to-parts warehousing systems or automated storage and retrieval systems. However, pick-and-pass systems play an important role for the faster delivery of small and frequent orders of inventory with the rise of e-commerce and e-business in the global supply chain. Two factors lead to idle time of pickers in a pick-and-pass system: picking line imbalance and shortage replenishment of products. This paper develops a genetic based heuristic method to solve SAP for a pick-and-pass system with multiple pickers to determine the appropriate storage space for each product and balance the workload of each picking zone so that the performance of the system can be improved. A simulation model based on FlexSim is used to implement the proposed heuristic algorithm and compare the throughput for different storage assignment methods as well. The results indicate that the proposed heuristic policy outperforms existing assignment methods in a pick-and-pass system.     

Data mining based storage assignment heuristics for travel distance reduction

Among the warehousing activities in distribution centres, order picking is the most time‐consuming and labour‐intensive. As a result, order picking may become a bottleneck preventing distribution centres from maximizing the effectiveness of their warehousing activities. Although storage location assignment (or product allocation) is a tactical decision, it is especially influential on the effectiveness of order picking. In previous studies, most storage assignment approaches considered the order frequency of individual products rather than that of product groups, which often are purchased together. This paper proposes a new association measure, weighted support count (WSC), based on association rule mining, to represent both the intensity and nature of the relationships between products in a distribution centre. This paper presents two storage assignment heuristics, the modified class‐based heuristic (MCBH) and the association seed based heuristic (ASBH), designed to facilitate efficient order picking by applying WSC. The real‐world data set of a grocery distribution centre is used to verify the effectiveness of the proposed approaches. From the computational results, MCBH cuts at most 4% from the travel distance for order picking per month, as compared with the traditional class‐based approach. Meanwhile, ASBH achieves at most a 13% reduction in travel distance.     

仓储物流机器人批量拣选路径规划仿真

针对仓储物流机器人在拣选作业过程中难以进行高效实时的路径规划问题,提出一种有效的解决方法。首先,根据拣选作业的需要建立一个灵活的仓储空间模型并对拣选作业任务流程进行描述。其次,根据批量拣选作业任务的特点,建立以路径总长度最小为优化目标的旅行商问题的数学模型。再次,提出改进的自适应遗传算法解决旅行商问题。最后,在考虑路径转折角代价的前提条件下,提出改进的A*算法,并与改进的自适应遗传算法相结合实现批量拣选的路径规划。仿真结果表明,该方法具有较快的收敛速度、较小的平均路径长度以及较少的算法运行时间,能很好地适应机器人批量拣选路径规划的要求。      

Product location,allocation and server home base location for an order picking line with multiple servers

In this paper, we are interested in several interrelated control issues for a ‘pick to order’ (or, ‘strict’ order picking) picking line which stores N=nk types of products in n bins, each with k shelves. To fill each order, a container is transported past the various locations containing products, and the appropriate quantity of each product is removed from its respective storage location and put into the order container using an ‘out and back’ picking strategy. Each of several pickers is assigned a set or ‘zone’ of products. We are interested in the concurrent problems of: (1) product location, (2) picker home base location, and (3) allocating products to each picker so that the expected order cycle time is minimized. We provide easily implemented algorithms to solve these problems and are able to show that the results apply for several alternate picking strategies. For fixed product locations, we develop an efficient dynamic programming algorithm which determines the optimal product allocation and server locations.     

Order picking systems: Batching and storage assignment strategies

We consider batching and storage allocation strategies in a manual order picking system of small parts which processes high volume of orders. The arrivals of orders are assumed to follow a Poisson process, and the quantity of items in an order is assumed to be independently and identically distributed negative binomial variates. The order picking system is modeled by a two-stage queueing system with batching and picking activities. Bounds and approximate estimates for the mean and variance of the turnover time (total service time) of a batch of orders are derived. The quality of the results is evaluated via simulation. A comparison between uniform and skewed item storage assignment is also presented. The effects of batching and the batch size on the service time are discussed.     

单巷道固定货架路径规划问题的研究

固定货架系统是自动化立体仓库的重要组成部分,仓库运行效率主要取决于对货物进行拣选的控制策略。针对单巷道固定货架系统拣选作业过程,以存取时间最小为目标,构建了含多个约束条件的拣选作业路径规划问题的数学模型。在基本蚁群算法基础上,采取自适应调整算法参数、候选节点集合等改进措施,设计了一种改进的蚁群算法对问题进行求解。仿真实验表明该算法能够很好地解决中大规模的拣选作业问题,全局寻优能力强,收敛速度快。     

Item-associated cluster assignment model on storage allocation problems

Warehouse management is currently facing fierce competition. By integrating information systems, retailers order more frequently with multiple items, but each order has smaller quantities. The situation becomes more stressful in a disintermediation supply–demand system. A good example is in the Business-to-Customer (B2C) online retailing business in which warehouses have to fulfill divergence orders directly. This study proposes a two-stage Clustering-Assignment Problem Model (CAPM) for the customized-orders picking problem. For multi-item-small-quantity orders, the CAPM targets a between-item association rather than the traditional group clustering to reduce the picking distance.The first stage of CAPM draws item association indices, based on between-item support, from customers’ orders. It then develops a mathematical programming model to search for the maximum total item support. The second stage applies assignment techniques to locate the clustered group in the storage place so as to minimize picking distance. We use Lingo commercial software to help the solution-finding procedures. By emphasizing the item association, CAPM is suitable for orders with multiple items and smaller quantities in the modern retailing sector. It also more effectively shortens the picking distance compared with popular frequency-based and random assignment storage methods. In the example of the drug distribution center studied herein, CAPM proves more effective as it reduces over 45% of the picking distances versus the current set-up.     

A RFID-based storage assignment system for enhancing the efficiency of order picking

In today’s time-sensitive markets, effective storage policies are widely accepted as a means for improving the efficiency of order picking. As a result of customization, the variety of products handled by a warehouse has increased, making storage location assignment problems more complicated. Different approaches have been proposed by researchers for improving storage assignment and order picking. However, many industrial practitioners find it difficult to adopt such approaches due to complexity and high associated costs. In particular, small and medium enterprises (SMEs), that generally, lack resources and who have staff members with weak artificial intelligence backgrounds, still rely on experience when assigning storage locations for diverse products. In these circumstances, the quality of decision making cannot be guaranteed. In view of this, an intelligent system which can be easily adopted by SMEs is designed to improve storage location assignment problems. The proposed system, an RFID-based storage assignment system (RFID-SAS), is a rule-based system incorporating radio frequency identification (RFID) provides decision support for storage assignment in a warehouse. Unlike many existing situations, RFID tags are attached to products at the item level instead of at the pallet level. As the knowledge embedded in the system is represented in the form of rules, evaluation is important and is outlined in this paper. The effectiveness of the system is verified by means of a case study in which the system is implemented in a typical SME specializing in machinery manufacturing. The results illustrate that RFID-SAS can enhance the efficiency of order picking in a warehouse.     

A warehouse management system with sequential picking for multi-container deliveries

The separation of picking and packing processes of a warehouse management system usually brings extra storage buffers and relatively longer operating time. This paper develops a hybrid algorithm to generate a picking sequence for combining picking and packing operations. The algorithm includes three elements: container selection, loading configuration, and loading/picking sequence. A generic warehouse management system with the proposed sequential order picking function for a tea factory in central Taiwan has been implemented to demonstrate the elimination of storage buffers and the reduction of operation time.     

The effect of worker learning and forgetting on storage reassignment decisions in order picking systems

Order picking is a time-intensive and costly logistics activity as it involves a high amount of manual work. Prior research has mostly neglected the influence of human factors on the efficiency of order picking systems. This paper develops a mathematical model that investigates the impact of learning and forgetting of a heterogeneous workforce on order picking time and, consequently, on storage assignment decisions. In particular, the paper investigates when to change a storage assignment and when to keep it if learning and forgetting occur among the members of an order picking workforce. The results show that learning and forgetting should be considered in order to achieve a proper planning of storage assignment strategies.     

Aggregation of orders in distribution centers using data mining

This paper considers the problem of constructing order batches for distribution centers using a data mining technique. With the advent of supply chain management, distribution centers fulfill a strategic role of achieving the logistics objectives of shorter cycle times, lower inventories, lower costs and better customer service. Many companies consider both their cost effectiveness and market proficiency to depend primarily on efficient logistics management. Warehouse management system (WMS) presently is considered a key to strengthening company logistics. Order picking is routine in distribution centers. Before picking a large set of orders, effectively grouping orders into batches can accelerate product movement within the storage zone. The order batching procedure has to be implemented in WMS and may be run online many times daily. The literature has proposed numerous batching heuristics for minimizing travel distance or travel time. This paper presents a clustering procedure for an order batching problem in a distribution center with a parallel-aisle layout. A data mining technique of association rule mining is adopted to develop the order clustering approach. Performance comparisons between the developed approach and existing heuristics are given for various problems.     

A systematic approach for product families formation in Reconfigurable Manufacturing Systems

The aim of this work is to establish a methodology for an effective working of Reconfigurable Manufacturing Systems (RMSs). These systems are the next step in manufacturing, allowing the production of any quantity of highly customised and complex products together with the benefits of mass production. In RMSs, products are grouped into families, each of which requires a system configuration. The system is configured to produce the first family of products. Once it is finished, the system is reconfigured in order to produce the second family, and so forth. Therefore, the effectiveness of a RMS depends on the formation of the best set of product families. Therefore, a methodology for grouping products into families, which takes into account the requirements of products in RMSs, is an issue of core importance. These requirements are modularity, commonality, compatibility, reusability, and product demand. The methodology starts by calculating, for each product requirement, a matrix that summarises the similarity between pairs of products. Then, through the use of the AHP methodology, a unique matrix that comprises the similarity values between products is obtained. The Average Linkage Clustering algorithm is applied to this matrix in order to obtain a dendogram that shows the diverse sets of product families that may be formed.     

Implementation of a parallel unstructured Euler solver on shared- and distributed-memory architectures

An efficient three-dimensional unstructured Euler solver is parallelized on a CRAY Y-MP C90 shared-memory computer and on an Intel Touchstone Delta distributed-memory computer. This paper relates the experiences gained and describes the software tools and hardware used in this study. Performance comparisons between the two differing architectures are made.This work was sponsored in part by ARPA (NAG-1-1485) and by NASA Contract No. NAS1-19480 while authors Mavriplis, Saltz and Das were in residence at ICASE, NASA Langley Research Center, Hampton, Virginia. This research was performed in part using the Intel Touchstone Delta System operated by Caltech on behalf of the Concurrent Supercomputing Consortium. Access to this fecility was provided by NASA Langley Research Center and the Center for Research in Parallel Processing. The content of the information does not necessarily reflect the position or the policy of the Government and no official endorsement should be inferred.     

Evaluation of an Augmented Reality Supported Picking System Under Practical Conditions

Order picking is one of the most important process steps in logistics. Because of their flexibility, human beings cannot be replaced by machines. But if workers, in order, picking systems are equipped with a head-mounted display, Augmented Reality can improve the information visualization. In this paper, the development of such a system—called Pick-by-Vision—is presented. The system is evaluated in a user study performed in a real storage environment. Important logistics figures as well as the subjective strain were measured. The results show that a Pick-by-Vision system can considerably improve industrial order picking processes.     

Storage assignment problem with travel distance and blocking considerations for a picker-to-part order picking system

Order picking is a key operation in managing a warehouse efficiently. Most previous studies on picking only considered single-picker operation; however, many pickers frequently work concurrently in the same region. Since congestion may occur in such a multi-picker system, waiting time must be taken into account together with travel time and distance when evaluating the efficiency of picking operations. The picking model under investigation can be formulated as a queueing network, and a heuristic storage assignment policy that considers both the travel time and the waiting time simultaneously by minimizing the average order fulfillment time is developed in the paper. An approximation method and a simulation model using eM-plant software are presented to implement the proposed heuristic algorithm and to compare the mean travel time for different storage assignment polices as well. The results indicate that the proposed heuristic policy outperforms existing storage assignment policies in a multi-picker warehouse environment.     

Pick-by-vision: augmented reality supported order picking

Order picking is one of the most important process steps in logistics. Due to their flexibility, human beings cannot be replaced by machines. But if workers in order picking systems are equipped with a head-mounted display, Augmented Reality can improve the information visualization. In this paper the development of such a Pick-by-Vision system is presented. It is evaluated in a user study performed in a real storage environment. Important logistic figures as well as subjective figures were measured. The results show that Pick-by-Vision can improve order picking processes on a big scale.     

A performance evaluation model for order picking warehouse design

This paper is concerned with a performance evaluation model for the order picking facility for warehouse design in a supply center (SC) by reducing the travel distance of transporters. This study includes important detail aspects of warehouse design and operational parameters such as warehouse size, rack size, number of transporters, and the system performance. In this study, we develop both mathematical and simulation modes considering probabilistic demand and picking frequency, and using simulation S/W, AutoMod. The results are compared and validated via simulation methods using AutoMod simulator. Finally, we developed a systematic and practical computer program and it was known that the proposed method is potentially efficient and useful in performance analysis for order picking warehouse problems.     

自动化立体仓库货位分配与作业调度集成优化

针对动态提高单载具堆垛机式自动化立体仓库拣选效率的问题,文中提出了一种基于共享货位存储与动态订单拣选策略下的货位分配与作业调度集成优化方法。将动态移库优化扩展到仓库的整个拣选生命周期,建立以双指令循环下堆垛机拣选任务所需的总作业时间最短为评价目标的数学模型,提出了一种基于K-Medoids聚类的粒子群优化(Particle Swarm Optimization,PSO)算法,用K-Medoids算法通过产品与订单的相关性进行初始货位的聚类分析,筛除劣质解的货位范围,并在K-Medoids聚类算法生成的解类簇基础上获得精确解。实验结果表明,考虑动态移库可以使仓库拣选效率提高20%,且该算法与传统PSO算法相比求解时间下降66%左右。     

Item-associated cluster assignment model on storage allocation problems

Warehouse management is currently facing fierce competition. By integrating information systems, retailers order more frequently with multiple items, but each order has smaller quantities. The situation becomes more stressful in a disintermediation supply–demand system. A good example is in the Business-to-Customer (B2C) online retailing business in which warehouses have to fulfill divergence orders directly. This study proposes a two-stage Clustering-Assignment Problem Model (CAPM) for the customized-orders picking problem. For multi-item-small-quantity orders, the CAPM targets a between-item association rather than the traditional group clustering to reduce the picking distance.     

基于蚁群优化算法的旋转货架拣选路径规划

给出自动化立体仓库单拣选台分层水平旋转货架系统的数学模型,提出一种改进的蚁群优化算法,用于解决货物拣选路径规划问题。该算法能快速找到最优货物拣选路径,得到的解质量较高且计算时间短。仿真结果表明,该方法适用于求解中小规模货物拣选路径的规划问题,可以提高自动存储作业效率。