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.     

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.     

A particle swarm optimization algorithm for the multiple-level warehouse layout design problem

Warehouse operation and management is one of the essential parts of manufacturing and service operations. The warehouse layout problem is a key to warehouse operations. Generally, warehouse layout design models attempt to optimize different objectives such as the orientation of storage racks, the allocation of space among competing uses, the number of cranes, the overall configuration of the facility, etc. The warehousing strategies can be classified as distribution-type, production-type and contract-type warehouse strategies. In this study, a distribution-type warehouse considered that various type products are collected from different suppliers for storing in the warehouse for a determined period and for delivery to different customers. The aim of the study is to design a multiple-level warehouse shelf configuration which minimizes the annual carrying costs. The turnover rates of the products are classified and they are considered while putting/picking them to/from shelves regarding the distances between the shelves and docks. Since proposed mathematical model was shown to be NP-hard, a particle swarm optimization algorithm (PSO) as a novel heuristic was developed for determining the optimal layout.     

基于混合元启发式算法的订单分批问题

订单拣选是仓库运营管理中一项高劳动强度与高成本的操作,拣货员在仓库中从货位拣选出满足订单需求的货物.订单分批问题(order batching problem, OBP)是订单拣选中的重要规划问题,该问题以最小化拣选批次路径时长为目标,将用户订单分配至拣选批次中.首先,为了优化订单分配构造高质量批次,提出一种混合元启发式算法,在自适应大邻域搜索框架中融入基于不可行下降的局部搜索,同时引入自适应惩罚机制和一批基于订单与基于批次的移除启发式以及新的算法组件;其次,为了优化拣选路径进一步降低批次旅行时间,提出单向启发式,利用动态规划优化组合多个路径策略.实验表明,在合理计算时间内,所提出算法的求解质量优于多重启变邻域搜索(MS-VNS)、混合自适应大邻域搜索及禁忌搜索(ALNS/TS),而且所提出算法的最大路径长度减少率达到22.36%.     

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.     

Two-echelon, multi-commodity supply chain network design with mode selection, lead-times and inventory costs

Designing distribution networks - as one of the most important strategic issues in supply chain management - has become the focus of research attention in recent years. This paper deals with a two-echelon supply chain network design problem in deterministic, single-period, multi-commodity contexts. The problem involves both strategic and tactical levels of supply chain planning including locating and sizing manufacturing plants and distribution warehouses, assigning the retailers' demands to the warehouses, and the warehouses to the plants, as well as selecting transportation modes.We have formulated the problem as a mixed integer programming model, which integrates the above mentioned decisions and intends to minimize total costs of the network including transportation, lead-times, and inventory holding costs for products, as well as opening and operating costs for facilities. Moreover, we have developed an efficient Lagrangian based heuristic solution algorithm for solving the real-sized problems in reasonable computational time.     

Throughput analysis for order picking system with multiple pickers and aisle congestion considerations

Most of previous studies in picker-to-parts warehousing systems investigated only single-picker operations and are therefore adequate to evaluate order picking efficiency by travel distance as aisle congestion never takes place in such systems. In real world applications, the congestion inevitably occurs when a system has multiple pickers working together within the same region. This paper presents an approximation method based on a GI/G/1 closed queueing network by using self-correcting approximation technique algorithm to evaluate the throughput time of an order picking system with multiple pickers and aisle congestion considerations for different routing policies. The results generated by the proposed method are compared and validated via simulation model using eM-plant simulator for different sizes of warehouses. The results indicate that the approximation method appears to be sufficiently accurate for practical purposes. The sensitivity analysis of the throughput time with respect to order sizes, number of pickers and number of aisles are conducted and the performance of different item storage policies are also evaluated using the proposed approximation model.     

Container storage space assignment problem in two terminals with the consideration of yard sharing

Facing the shortage of storage space of container terminal yard, a yard sharing strategy that uses dry port's surplus storage space to ease container congestion is proposed. This novel strategy can address the container storage space assignment problem for inbound containers. The problem is studied based on the storage yard of the combined container terminal and dry port. First, a multiple-objective mixed integer programming model that considers yard sharing strategy with the objectives of minimizing total travel distance, minimizing imbalance in number of containers, maximizing shared storage space of the dry port is formulated to obtain optimal solutions. Second, a non-dominated sorting genetic algorithm II (NSGA-II) is proposed. Next, the performance of the algorithm is verified by a set of instances. Numerical experiments are conducted to elucidate the problem with yard sharing strategy intuitively. Furthermore, the performance of the model in four aspects proclaims the advantages of yard sharing strategy and certifies the comprehensiveness. Finally, sensitivity analysis is conducted by two aspects which are weight coefficient and feasible distance to verify the efficiency of the proposed method.     

普通立体仓库的货位优化模型与算法研究

在物流仓储的总成本中,库存成本占很大比重。企业为了减少库存成本,不断设法去改进和优化仓库"存储"的过程。针对普通立体仓库的入库过程,在考虑叉车载重、货位容量和存储策略等限制条件下,建立了以入库作业过程中工作人员行走总路程最小为目标的货位优化模型。根据问题和模型特点,将货位优化问题分为两层优化:货位选择优化层和货位顺序优化层;然后将传统遗传算法同启发式算法相结合对模型进行求解;最后通过仿真实验验证了模型和算法的合理性和可行性。该成果可用于解决多数普通仓库的货位优化问题。     

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 simulating annealing algorithm to solve the green vehicle routing & scheduling problem with hierarchical objectives and weighted tardiness

We present a green vehicle routing and scheduling problem (GVRSP) considering general time-dependent traffic conditions with the primary objective of minimizing CO₂ emissions and weighted tardiness. A new mathematical formulation is proposed to describe the GVRSP with hierarchical objectives and weighted tardiness. The proposed formulation is an alternative formulation of the GVRSP in the way that a vehicle is allowed to travel an arc in multiple time periods. The schedule of a vehicle is determined based on the actual distance that the vehicle travels each arc in each time period instead of the time point when the vehicle departs from each node. Thereby, more general time dependent traffic patterns can be considered in the model. The proposed formulation is studied using various objectives functions, such as minimizing the total CO₂ emissions, the total travel distance, and the total travel time. Computational results show that up to 50% reduction in CO₂ emissions can be achieved with average reductions of 12% and 28% compared to distance-oriented solutions and travel-time-oriented solutions, respectively. In addition, a simulated annealing (SA) algorithm is introduced to solve large-sized problem instances. To reduce the search space, the SA algorithm searches only for vehicle routes and rough schedules, and a straightforward heuristic procedure is used to determine near-optimal detailed schedules for a given set of routes. The performance of the SA algorithm is tested on large-sized problems with up to 100 nodes and 10 time periods.     

Towards Zero-Warehousing Smart Manufacturing from Zero-Inventory Just-In-Time production

Over the past half-century, manufacturers have strived to achieve “Zero Inventory (ZI)” manufacturing, which puts the burden on suppliers to build their warehouses nearby production lines to meet the strict time delivery requirements of ZI production. Considerable improvements in manufacturing processes have been made. However, this practice in the meanwhile brings excessive warehousing operations along the supply chain. Recently, massive up-front investment has been made by some larger manufacturers for industrial 4.0 and warehouse automation to facilitate warehousing operations, such as put away and picking. However, warehouse operation activities enhanced by automation are still non-value-adding and current practice of redundant handling process implies continuous cost increase with the demand expanding. Therefore, it is of significance to simplify the corresponding operations as much as possible. This paper makes a comprehensive introduction to a new manufacturing paradigm named Zero-Warehousing Smart Manufacturing (ZWSM) from the concept, practices, principles, and core technologies. ZWSM basically aims to avoid traditional non-value-adding warehousing operations such as put away and order picking, as well as to reduce the warehousing space for operations to the minimum. First, prototype layout and basic practices under ZWSM are proposed. Second, synchronization, unitization and uncertainty hedging, which are the key principles to achieve the ZWSM, are explicitly introduced. Third, A Zero-Warehousing Smart Manufacturing Platform (ZWSMP) is developed with the Internet of Things (IoT) enabled infrastructures. Corresponding logistics services are presented to provide information visibility and achieve operations improvement. A case study of the Hong Kong prefabrication construction project is used to demonstrate the materials delivery process among different supply chain partners to provide a basis for zero-warehousing achievement.     

SKU arrangement on a unidirectional picking line

An order picking system consisting of independent unidirectional picking lines has been investigated in this study. A picking line forms a type of unidirectional carousel as multiple pickers walk in a clockwise direction around a conveyor belt running down the centre of the picking line. There are three decision tiers in this order picking system, namely the assignment of stock keeping units (SKUs) to different waves, the arrangement of SKUs into locations on a picking line for a wave of picking and the sequencing of orders. Each tier has an objective to minimise the total travel distance of pickers. The focus of this paper is on the arrangement of SKUs on a picking line for a wave of picking. An exact mathematical formulation is introduced which is not solvable to optimality for large real‐life instances. Two heuristics known to be optimal for certain carousel systems as well as two further heuristics using SKU correlations are therefore tested. The heuristic approaches are compared against lower bounds generated by relaxing the exact formulation and a set of random solutions. Known algorithms for carousel systems are not optimal in this carousel system and all heuristic approaches perform approximately equally well. It is shown that the gains from addressing the order sequencing decision tier outweighs that of the SKU arrangement decision tier. Moreover, the complexity of addressing the entire system of decision can be reduced by arranging SKUs with a simple heuristic having a negligible impact on solution quality.     

Regression approximation for a partially centralized inventory system considering transportation costs

Inventory centralization for multiple stores with stochastic demands reduces costs by establishing and maintaining a central ordering/distribution point. However the inventory centralization may increase the transportation costs since either the customer must travel more to reach the product, or the central warehouse must ship the product over longer distance to reach the customer. In this paper, we study a partially centralized inventory system where multiple central warehouses exist and a central warehouse fulfills the aggregated demand of stores. We want to determine the number, the location of central warehouses and an assignment of central warehouses and a set of stores. The objective is the minimization of the sum of warehouse costs and transportation cost. With the help of the regression approximation of cost function, we transform the original problem to more manageable facility location problems. Regression analysis shows that the approximated cost function is close to the original one for normally distributed demands.     

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.     

Coordinated selection of procurement bids in finite capacity environments

Pressure to increase agility and reduce costs is pushing enterprises to dynamically select among offers from a broader range of suppliers. This process is facilitated by the adoption of web services standards. An important requirement in this context is the ability to move away from unidimensional price-based e-procurement models and develop richer solutions that are capable of capturing other important attributes in the selection of supplier bids. Research on the evaluation and selection of supplier bids (“winner determination”) has traditionally ignored the temporal and finite capacity constraints under which manufacturers and service providers often operate. We consider the problem faced by a firm that procures multiple key components or services from a number of possible suppliers. Bids submitted by suppliers include both a price and a delivery date. The firm has to select a combination of supplier bids that will maximize its overall profit. Profit is determined by the revenue generated by the products (or services) sold by the firm, the costs of the components (or services) it acquires as well as late delivery penalties it incurs if it fails to deliver its products/services in time to its own customers. We provide a formal model of this important class of problems, discuss its complexity and introduce rules that can be used to efficiently prune the resulting search space. We proceed to show that our model can be characterized as a pseudo-early/tardy scheduling problem and use this observation to build an efficient heuristic search procedure. Computational results show that our heuristic procedure typically yields solutions that are within a few percent from the optimum. They further indicate that taking into account the manufacturer/service provider’s capacity can significantly improve its bottom line.     

A decomposition-based heuristic for stochastic emergency routing problems

This paper proposes a decomposition-based heuristic for a network delivery problem in which relief workers acquire valuable emergency supplies from relief warehouses, and transport them to meet the urgent needs of distressed population centres. The problem context dictates that the relief items reach these population centres before critical deadlines. However, co-ordination challenges and random disruptions introduce uncertainty in both network travel times and the destination deadlines. Hence, relief workers have to negotiate the tension between ensuring a high probability of punctual delivery and maximising the combined value of the relief supplies delivered. For an arbitrary routing scheme which guarantees punctual delivery in an uncertainty-free state of nature, the heuristic yields an upper bound on the probability that, under uncertainty, the routing scheme described will lead to tardy delivery. We demonstrate our solution approach on a small numerical example and glean insights from experiments on a realistically sized problem. Overall, our central model and proposed solution approach are useful to managers who need to evaluate routing options and devise effective operational delivery plans in humanitarian crisis situations.     

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.     

响应动态约束条件的多目标货位优化算法研究

针对自动化立库货位决策与优化问题,考虑到优化目标多样、托盘使用状态及可分配货位动态变化等因素,提出了一种响应动态约束条件的多目标货位优化算法。以巷道作业均衡、货架重心稳定及作业路径最短建立多目标优化模型,基于变异系数自适应差分进化算法,使用货位随机数编码,根据实时货位可行域进行个体解码,以响应动态货位约束条件。提出了基于层次分析的Pareto解评价方法,从而获得多批作业货位持续优化的目标权重,为仓储货位决策提供合理方案。多批作业算法实验结果表明:所提算法效果显著优于多目标简单加权算法,能够有效应用于动态货位决策与优化。     

Effects of box features on spine loading during warehouse order selecting.

Low back disorders in distribution centres or warehouses have been identified as an area of elevated risk in many industries. The task of an order selector requires workers manually to lift boxes from storage bins to a mobile pallet. This study explored the effect of box features and box location when lifting from a pallet in a storage bin upon spine loading. Ten experienced warehouse workers were asked to lift boxes from a pallet while the size, weight, handle features and location of the box on a pallet were changed. An EMG-assisted model was employed to assess spine compression, lateral shear and anterior-posterior shear during the lifts. The position from which the worker lifted a box on a pallet had the most profound effect on spine loading while the lower level of the pallet represented the greatest loadings on the spine. Box weight did not appear to be a feasible means of controlling spine loading unless its position on the pallet could also be controlled. The inclusion of handles had an effect similar to reducing the box weight by 4.5 kg, whereas box size did not effectively affect spine loading. The mechanisms by which these factors affect spine loading are discussed.