Design and Control of Warehouse Order Picking: a literature review

Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for its warehouse, and consequently for the whole supply chain. In order to operate efficiently, the orderpicking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions.Order picking;Logistics;Warehouse Management

Determining Number of Zones in a Pick-and-pack Orderpicking System

In this study we consider a pick-to-pack orderpicking 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 the next station for packing. Our aim is to determine the optimal number of zones such that the overall (picking and packing) time to finish a batch is minimized. We solve this problem by optimally assigning items to pick routes in each zone. We illustrate the method with data taken from a distribution center of one of the largest online retailers in the Netherlands

Design and Control of Warehouse Order Picking: a literature review

Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for its warehouse, and consequently for the whole supply chain. In order to operate efficiently, the orderpicking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions

Performance Approximation and Design of Pick-and-Pass Order Picking Systems

In this paper, we discuss an approximation method based on G/G/m queuing network modeling using Whitt’s (1983) queuing network analyzer to analyze pick-and-pass order picking systems. The objective of this approximation method is to provide an instrument for obtaining rapid performance estimates (such as order lead time and station utilization) of the order picking system. The pick-and-pass system is decomposed into conveyor pieces and pick stations. Conveyor pieces have a constant processing time, whereas the service times at a pick station depend on the number of order lines in the order to be picked at the station, the storage policy at the station, and the working methods. Our approximation method appears to be sufficiently accurate for practical purposes. It can be used to rapidly evaluate the effects of the storage methods in pick stations, the number of order pickers at stations, the size of pick stations, the arrival process of customer orders, and the impact of batching and splitting orders on system performance.simulation;warehousing;order picking;queuing network;pick-and-pass

A Study on Storage allocation problem based on clustering algorithms for the improvement of warehouse efficiency

The operation of warehouses has long been a focus of industry research. Faced with rapidly growing business needs, improving storage efficiency, and reducing customer response times have become crucial issues for improving the operational efficiency of a warehouse. Given a fixed area of space, optimizing the storage strategy can reduce the cost of goods handling, improve the efficiency of storage and delivery, accelerate the overall operational efficiency of the warehouse, and reduce logistical costs. In this paper we study the improvement of a real-life company’s storage location strategy using cluster and association analysis. Two different clustering techniques namely pairwise comparison clustering and K-means clustering are used, and their performances are compared with the current random storage policy used by the company. Both clustering algorithms consider item association and classify items into groups based on how frequently they appear with each other in customer's orders. The next stage applies assignment techniques to locate the clustered group in each aisle so as to minimize the total number of aisle visits and ultimately picking distance. By emphasizing the item association, our model is suitable for orders with multiple items in the modern retailing sector. It also more effectively shortens the picking distance compared with random assignment storage method. In our case, Warehouse studied herein, both models prove more effective as it reduces over 35% and 25 % of the picking distances versus the current set-up. However, when compared with each other the K-means clustering method outperforms the pairwise comparison

Order batching in multi-server pick-and-sort warehouses.

In many warehouses, customer orders are batched to profit from a reduction in the order picking effort. This reduction has to be offset against an increase in sorting effort. This paper studies the impact of the order batching policy on average customer order throughput time, in warehouses where the picking and sorting functions are executed separately by either a single operator or multiple parallel operators. We present a throughput time estimation model based on Whitt's queuing network approach, assuming that the number of order lines per customer order follows a discrete probability distribution and that the warehouse uses a random storage strategy. We show that the model is adequate in approximating the optimal pick batch size, minimizing average customer order throughput time. Next, we use the model to explore the different factors influencing optimal batch size, the optimal allocation of workers to picking and sorting, and the impact of different order picking strategies such as sort-while-pick (SWP) versus pick-and-sort (PAS)Order batching; Order picking and sorting; Queueing; Warehousing;

Determining Warehouse Storage Location Assignments Using Clustering Analysis

The methodology used to assign products to a storage location in a warehouse can have a significant impact on the amount of time required to retrieve all of the items needed to fill an order. This paper describes a methodology that uses a clustering approach to determine storage assignments, where the metric of the strength of the relationship between two stock-keeping units (SKUs) is the number of times that the SKUs appear in the same order. Clustering is performed to maximize the frequency with which SKUs in the same cluster are ordered together. In testing, the clustering assignments were compared to a demand-based assignment strategy and showed a reduction of 20-30% in the number of aisles visited to retrieve orders

The impact of integrated cluster-based storage allocation on parts-to-picker warehouse performance

Order picking is one of the most demanding activities in many warehouses in terms of capital and labor. In parts-to-picker systems, automated vehicles or cranes bring the parts to a human picker. The storage assignment policy, the assignment of products to the storage locations, influences order picking efficiency. Commonly used storage assignment policies, such as full turnover-based and class-based storage, only consider the frequency at which each product has been requested but ignore information on the frequency at which products are ordered jointly, known as product affinity. Warehouses can use product affinity to make informed decisions and assign multiple correlated products to the same inventory “pod” to reduce retrieval time. Existing affinity-based assignments sequentially cluster products with high affinity and assign the clusters to storage locations. We propose an integrated cluster allocation (ICA) policy to minimize the retrieval time of parts-to-picker systems based on both product turnover and affinity obtained from historical customer orders. We formulate a mathematical model that can solve small instances and develop a greedy construction heuristic for solving large instances. The ICA storage policy can reduce total retrieval time by up to 40% c