Determining the number of zones in a pick-and-sort order picking system

In this study we consider a pick-and-sort order picking system, in which batches of orders are picked simultaneously from different (work) zones by a group of order pickers. After picking, the orders are transported by a conveyor to a next station for consolidation and packing. Packing can only occur when an order has been picked completely. For a given number of workers, each assigned to a single zone, a larger number of zones reduces pick time (since travel time reduces), but increases waiting time for completion at the packing stations, because more partial batches needing assembly arrive at the packing stations. Our aim is to determine the optimal number of zones such that the total (picking and packing) time to complete a batch is minimised. We solve this problem by optimally assigning items to pick routes in each zone. We illustrate the method with data taken from a distribution centre of one of the largest online retailers in The Netherlands.     

A simulation-based comparison of two goods-to-person order picking systems in an online retail setting

Design and analysis of order picking systems continues to be an active topic of interest both in academia and practice, especially in light of the significant increase in online retail sales. In this paper, we examine two types of well-known, goods-to-person order picking systems, namely, a miniload system and a Kiva system. Using a simulation model, we compare the performance of the two systems on the basis of expected throughput and expected container retrieval times to process the same set of customer orders. We also discuss some of the advantages and limitations of the two systems.     

Maverick picking: the impact of modifications in work schedules on manual order picking processes

Order picking, the process of retrieving items from their storage locations to fulfil customer orders, ranks among the most labour- and time-intensive processes in warehousing. Prior research in this area had a strong focus on the development of operating policies that increase the efficiency of manual order picking, for example by calculating optimal routes for the order pickers or by assigning products to storage locations. One aspect that poses a major challenge to many warehouse managers in practice has, curiously enough, remained largely unexplored by academic research: modifications in workflows (i.e. workplace deviance in a positive or negative sense) in order picking, which we define as ‘maverick picking’. The purpose of this paper is to characterise maverick picking and to study its causes, its forms of appearance and its potential impact on order picking performance. To gain insights into maverick picking, we first survey the literature to illustrate the state-of-knowledge of maverick picking. Subsequently, we report the results of a multi-case study on maverick picking and deduct a related content framework. The results of our case study support the proposition that maverick picking is highly relevant in practice and that it is a major determinant of order picking performance.     

The use of bucket brigades in zone order picking systems

As the transactions through electronic commerce and TV home shopping increase, the warehouses often receive a large amount of small orders to be picked within tight time windows. One of the important warehousing activities is order picking, the process of retrieving a number of items from warehouse storage to meet a number of independent customer orders. This paper examines a new order picking method, bucket brigade order picking (BB picking). Bucket brigade is a way of coordinating workers who progressively perform a set of operations on a flow line. In the BB picking system, a worker performs operations on an order until the next worker downstream takes it over; then goes back to the previous worker upstream to take over a new order. We discuss distinct characteristics in order picking systems when bucket brigades are applied. We identify some efficiency losses under the BB picking and present a new BB picking protocol to improve the performance of order picking systems. The new BB picking is compared with the existing BB picking and zone picking through simulation experiments.     

Enhancing performance in order picking processes by dynamic storage systems

Increasing productivity and reducing labour cost in order picking processes are two major concerns for most warehouse managers. Particularly picker-to-parts order picking methods lead to low productivity as order pickers spend much of their time travelling along the aisles. To enhance order picking process performance, an increasing number of warehouses adopt the concept of dynamic storage where only those products needed for the current order batch are dynamically stored in the pick area, thereby reducing travel time. Other products are stored in a reserve area. We analyse the stability condition for a dynamic storage system with online order arrivals and develop a mathematical model to derive the maximum throughput a DSS can achieve and the minimum number of worker hours needed to obtain this throughput, for order picking systems with a single pick station. We discuss two applications of dynamic storage in order picking systems with multiple pick stations in series. In combination with simulation modelling, we are able to demonstrate that dynamic storage can increase throughput and reduce labour cost significantly.     

Exact and inexact solution procedures for the order picking in an automated carousal conveyor

In many modern logistic systems, automated carousal conveyors are frequently used for storing/retrieving small to medium items to fulfil the customer's demand. The system is used to process a sequence of orders from customers in a dynamic fashion. Each order includes one or several items to be picked from the carousal storage or bin. The sequence between orders and the sequences among items within each order are to be determined, to minimise the total order processing or associated picking cost. This multi-order picking problem is formulated as 0–1 integer programming model, which resembles the well-known Multi-travelling salesman problem. Some important properties of this unique order processing system are described. New theoretical properties are established to characterise the optimal retrieval sequence in an order with multiple items to be picked. Based on these properties, efficient and effective heuristics are developed to solve this multi-order picking problem. The computational detail of these approaches is illustrated by an example. Numerical experiments with problem sizes up to 500 items for a batch of 50 orders are performed, where different order densities and order rates are considered.     

Dynamic order batching in bucket brigade order picking systems with consecutive batch windows and non-identical pickers

The blocking mitigation mechanism contained in a batching and sequencing model improves order picking performance in bucket brigade order picking systems by adjusting the batch formation in a given batch window. The batch formation of a given batch window affects the transition to the next batch window and limits the batch formation’s structure in the next window. In addition, imbalance in picker capability increases the variation of batch formation while mitigating blocking delay. Our paper proposes a dynamic indexed batching model to smooth the transition between consecutive batch windows and give a better approximation of non-identical pickers’ capability. We develop a mixed integer programming solution for a dynamic indexed batching model in a bucket brigade order picking system (DIBMB). Simulation experiments show that the DIBMB smooths the transition between batch windows as measured in the delay experienced by the first batch in each batch window. Comparisons to the available batching models show that under DIBMB, the blocking delay gradually increases throughout the sequence of batches.     

Additional effort estimation due to ergonomic conditions in order picking systems

Within a warehouse, the picking activity often relies on human operators. Therefore, when designing and evaluating a manual picking system, it is important to consider that, besides the high flexibility the pickers are able to warrant, they inevitably require an additional effort due to their ergonomic working conditions. In this paper, the authors propose a new model to consider such additional effort, starting from the concepts of human availability and rest allowance. The new method allows the evaluation of the current configuration of a certain warehouse, considering two different operative situations (directly employed operators and indirectly employed ones). Moreover, it makes it possible to estimate and to understand the benefits that can be achieved by introducing some ergonomic improvements. The proposed procedure has also been applied to a real industrial case study.     

Incorporating human factors in order picking planning models: framework and research opportunities

Order picking (OP) activities, essential to logistics operations, are laborious and time-intensive. Humans are central actors in the OP process and determine both OP effectiveness and efficiency. Many researchers have developed models for planning OP activities and increasing the efficiencies of such systems by suggesting different warehouse layouts, OP routes or storage assignments. These studies have, however, ignored workers’ characteristics, or human factors, suggesting that they cannot be substantiated, which led to only partially realistic results. This paper proposes a conceptual framework for integrating human factors into planning models of OP activities and hypothesises that doing so improves the performance of an OP system and workers’ welfare. The framework is based on a systematic literature review that synthesises findings documented in the OP and human factors literature. The results of the paper may assist researchers and practitioners in designing OP systems by developing planning models that help in enhancing performance and reducing long-term costs caused by work-related inefficiencies.     

基于NSGA-Ⅱ算法的分区拣选优化

电商背景下的客户订单呈现出多品种、小批量、高频次等特点,给仓库拣选工作带来很大的挑战。为提高拣选效率,在订单完全拆分的分批策略和组合优化的行走策略下,设计了以总服务时间最小、分区工作量平衡度最优和二次分拣效率最高的多目标分区拣选模型。由于3个目标函数之间存在矛盾,设计了NSGA-II算法对多目标优化模型进行求解。通过数值实验,与传统的不拆分订单的分区拣选系统对比,发现在订单批量环境为[1,4]时,分别使总服务时间减少了43.88%,平衡度改善了84.61%,并分析了区域个数、订单总数和订单批量环境对系统效率的影响。     

Scheduling of deadlock and failure-prone automated manufacturing systems via hybrid heuristic search

This work focuses on the scheduling problem of deadlock and failure-prone automated manufacturing systems, and presents a new scheduling method by combining a robust supervisory control policy and hybrid heuristic search. It aims to minimise makespan, i.e. the completion time of the last part. Based on the extended reach ability graph of the system, it establishes a new heuristic function and two dispatching rules to guide the search process for a schedule. By embedding a robust supervisory control policy into the search process, it develops a polynomial robust dynamic window search algorithm. Failure and repair events of unreliable resources may occur during the execution of a schedule obtained by the proposed algorithm and may make the schedule infeasible. To reduce the influence caused by them and ensure all parts to be finished, this work proposes two event-driven strategies. The first one suspends the execution of the parts requiring failed resources and those to be started until all failed resources are repaired and permits only those parts that have already been processed on working machines to be completed. The second one invokes the proposed algorithm to obtain a new schedule at the vertex generated after a resource failure or repair event and executes the new schedule. Both strategies are effective while the latter performs better at the expense of more computation.     

Enhancing the order picking process through a new storage assignment strategy in forward-reserve area

This paper reexamines the order picking process in a warehouse facing the challenges that e-commerce brings about and which are characterised by a very large number of small sized orders and returns. Implementing effective storage assignment strategies combined with efficient batching, in this context, is very fundamental to keep the warehouse’s responsiveness and order completion times up to the standards. This paper investigates a new storage assignment strategy, initially implemented by a large shoes and footwear wholesaler, to enhance the performance of the order picking process in its warehouses. The impact of this new storage assignment strategy on the performance of the order picking process is analysed via simulation. The performance of the system, measured in terms of total picking time as well as order lead time while taking congestion into account, is compared to the cases where conventional storage assignment strategies are implemented. A full factorial design is set up and the simulation output is statistically analysed. The results of this analysis are reported and thoroughly discussed. Attending to the results, the proposed strategy presents a remarkable potential to shorten total picking travel distances and order completion time and consequently customer satisfaction.     

Human factors in order picking: a content analysis of the literature

Order picking (OP) is one of the most labour- and time-intensive processes in internal logistics. Over the last decades, researchers have developed various mathematical planning models that help to increase the efficiency of OP systems, for example, by optimising storage assignments or by specifying routes for the order pickers that minimise travel distance in the warehouse. Human characteristics that are often a major determinant of OP system performance have, however, widely been ignored in this stream of research. This paper systematically evaluates the literature on manual OP systems and conducts a content analysis to gain insights into how human factors (HF) have been considered and discussed in the scientific literature. The results of the analysis indicate that management-oriented efficiency criteria dominated prior research on OP, and that there is a clear lack of attention to HF in the design and management of OP systems. This poses an opportunity for research and design of manual OP systems.     

The value of integrating order picking and vehicle routing decisions in a B2C e-commerce environment

In B2C e-commerce sales, customers expect a fast and low-cost delivery. To be able to fulfil these customer expectations, both warehouse and distribution operations have to be performed in an efficient and effective way. Ideally, these two supply chain functions should be considered simultaneously in an integrated problem since they are interrelated. In this paper, a record-to-record travel algorithm is proposed to solve the integrated order picking-vehicle routing problem (I-OP-VRP). Experiments with both small-size and large-size instances are conducted. Furthermore, the integrated approach is compared with an approach in which both problems are solved sequentially. Results show that integration leads to increased service levels, i.e. it allows to shorten the time between placing an order and receiving the goods. On top, the integrated approach leads to costs savings of on average 1.8%. Thus, integration is indispensable for a fast and cost-efficient delivery of goods.     

Evaluating order picking performance trade-offs by configuring main operating strategies in a retail distributor: A Design of Experiments approach

Warehouse design has become important due to its impact on service to customers and total logistics costs. Order picking is the key activity of a warehouse and an appropriate design will directly affect its overall performance. The increasing complexity of warehouses means that the main operating strategies such as storage location assignment, batching and routing need to be considered simultaneously. A Design of Experiment approach aided by Discrete Event Simulation could help to meet these new picking design process requirements, especially when various performance measures need to be compared. Some of the trade-offs that managers must keep in mind when designing order picking systems are shown, describing a single-case study carried out at a retail distributor.     

Measuring system and software reliability using an automated data collection process

The factors which affect the behaviour of the customer's computing environment, which is undergoing a revolution away from a server or timeshare centric model to a client/server or distributed model, can no longer be identified solely through using traditional methods of data collection. Digital Equipment Corporation has developed an automated data collection process, collecting on-system data logging information from customer sites that has yielded consistent, quantitative, high integrity information. This information has been used to proactively focus on direct product and process improvements. This paper describes the on-system data logging process and analysis methodology used by Digital to measure system, product and operating system reliability with examples of the application of the techniques that provide insight into the causes of failures.     

Designing an optimal turnover-based storage rack for a 3D compact automated storage and retrieval system

Compact, multi-deep (3D) automated storage and retrieval systems (AS/RS) are becoming increasingly popular for storing products. We study such a system where a storage and retrieval (S/R) machine takes care of movements in the horizontal and vertical directions of the rack, and an orthogonal conveying mechanism takes care of the depth movement. An important question is how to layout such systems under different storage policies to minimize the expected cycle time. We derive the expected single-command cycle time under the full-turnover-based storage policy and propose a model to determine the optimal rack dimensions by minimizing this cycle time. We simplify the model, and analytically determine optimal rack dimensions for any given rack capacity and ABC curve skewness. A significant cycle time reduction can be obtained compared with the random storage policy. We illustrate the findings of the study by applying them in a practical example.     

A systematic approach to determine the optimal maintenance policy for an automated manufacturing system

We propose a systematic approach to determine the optimal maintenance policy for an automated manufacturing system which includes a flexible manufacturing cell (FMC) and several automated machine shops. The systematic approach combines simulation, fractional factorial design, noise or outer array of Taguchi design, regression metamodelling, and classical queueing analysis. A useful expression of the fractional utilization of the manufacturing system is derived and incorporated into formulating and solving the corresponding decision problem. The systematic approach provides an effective implementation procedure to handle practical maintenance problems found in a complex manufacturing environment. © 1997 John Wiley & Sons, Ltd.     

Optimal order picker routing in a conventional warehouse with two blocks and arbitrary starting and ending points of a tour

This paper investigates manual order picking, where workers travel through the warehouse to retrieve requested items from shelves. To minimise the completion time of orders, researchers have developed various routing procedures that guide order pickers through the warehouse. The paper at hand contributes to this stream of research and proposes an optimal order picker routing policy for a conventional warehouse with two blocks and arbitrary starting and ending points of a tour. The procedure proposed in this paper extends an earlier work of Löffler et al. (2018. Picker routing in AGV-assisted order picking systems, Working Paper, DPO-01/2018, Deutsche Post Chair-Optimization of Distribution Networks, RWTH Aachen University, 2018) by applying the concepts of Ratliff and Rosenthal (1983. “Order-picking in a Rectangular Warehouse: a Solvable Case of the Traveling Salesman Problem.” Operations Research 31 (3): 507–521) and Roodbergen and de Koster (2001a. “Routing Order Pickers in a Warehouse with a Middle Aisle.” European Journal of Operational Research 133 (1): 32–43) that used graph theory and dynamic programming for finding an optimal picker route. We also propose a routing heuristic, denoted S*-shape, for conventional two-block warehouses with arbitrary starting and ending points of a tour. In computational experiments, we compare the average order picking tour length in a conventional warehouse with a single block to the case of a conventional warehouse with two blocks to assess the impact of the middle cross aisle on the performance of the warehouse. Furthermore, we evaluate the performance of the S*-shape heuristic by comparing it to the exact algorithm proposed in this study.     

微粒群算法在自动控制系统设计中的应用

提出了将微粒群优化(Particle Swarm Optimization,PSO)算法与控制系统设计相结合的系统设计思路和方法。系统设计过程包括两个部分：首先基于历史输入输出数据,用微粒群算法建立系统的模型,然后基于得到的模型进行控制器的设计,并用微粒群算法进行控制器的参数优化整定。仿真试验结果表明,微粒群算法在控制系统设计的模型建立、控制器参数优化等方面发挥了重要的作用,简化了控制系统设计任务,提高了设计效率。