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

An improved largest gap routing heuristic for order picking

The largest-gap policy is a routing heuristic for order picking systems. In this paper we develop an improved largest gap routing method. A simulation approach is used to demonstrate the superior performance of the improved largest gap routing over traditional largest gap. Moreover, this paper tests the performance impact of storage assignment rules on largest gap routings. Several scenarios with various order sizes and different item popularity proportions are tested. Monte-Carlo simulation is used to carry out the experiments. The numerical results from the computational analysis show that our improved largest gap routing always outperforms the traditional largest gap routing, i.e. for all order sizes. The effect is the most distinct when the order size is smaller. Finally the study demonstrates that the optimal storage assignment rule to be combined with largest gap routing is within-aisle storage

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;

Optimal Storage Rack Design for a 3D Compact AS/RS with Full Turnover-Based Storage

Compact, multi-deep (3D) automated storage and retrieval systems (AS/RS) are becoming increasingly popular for storing products with relatively low turnover on a compact area. An automated storage/retrieval crane takes care of movements in the horizontal and vertical direction in the rack, and a gravity conveying mechanism takes care of the depth movement. An important question is how to layout such systems to minimize the product storage and retrieval times. Although much attention has been paid to 2D AS/RS, multi-deep systems have hardly been studied. This paper studies the impact of system layout on crane travel time. We calculate the rack dimensions that minimize single-command cycle time under the full-turnover-based storage policy. We prove the expected travel time is minimized when the rack is square-in-time in horizontal and vertical directions and the conveyor’s dimension is the longest. We compare the model’s results with the performance of the random storage policy and show a significant crane travel time reduction can be obtained. We illustrate the findings of the study by applying them in a practical example.AS/RS;Warehousing;Order Picking;Storage Rack Design;Travel Time Model;Turnover-Based Storage

Algorithms for On-line Order Batching in an Order-Picking Warehouse

In manual order picking systems, order pickers walk or ride through a distribution warehouse in order to collect items required by (internal or external) customers. Order batching consists of combining these – indivisible – customer orders into picking orders. With respect to order batching, two problem types can be distinguished: In off-line (static) batching all customer orders are known in advance. In on-line (dynamic) batching customer orders become available dynamically over time. This report considers an on-line order batching problem in which the total completion time of all customer orders arriving within a certain time period has to be minimized. The author shows how heuristic approaches for the off-line order batching can be modified in order to deal with the on-line situation. A competitive analysis shows that every on-line algorithm for this problem is at least 2-competitive. Moreover, this bound is tight if an optimal batching algorithm is used. The proposed algorithms are evaluated in a series of extensive numerical experiments. It is demonstrated that the choice of an appropriate batching method can lead to a substantial reduction of the completion time of a set of customer orders.Warehouse Management, Order Picking, Order Batching, On-line Optimization

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 simulation approach to warehousing policies: the grandvision case

Esta tese de mestrado é um projecto desenvolvido na empresa GrandVision na área da Gestão da Cadeia de Abastecimento, mais concrectamente em Armazenagem, que apesar de muitas vezes desprezada, representa em média entre um quarto a um quinto dos custos logísticos. Apesar dos grandes avanços na tecnologia os armazéns tradicionais, de picking manual, continuam a representar 80% do universo. Aproveitando a vontade da Gestão da empresa em desenvolver projectos de melhoria para o Armazém, foi proposto o estudo ,através de simulação, de novas políticas de Armazenamento e de Picking para a operação de aprovisionamento das lojas MultiOpticas e GrandOptical. Os modelos testados em simulação partiram dos estudos previamente desenvolvidos nesta área e os resultados obtidos estão alinhados com os que foram anteriormente reportados. Com a conclusão desta tese, a Gestão da GrandVision fica no seu dispor de um procedimento de Arrumação baseado em Classes que quando combiando com uma política de Agrupamento de orderns podem trazer poupanças de tempo de ciclo a rondar os 32%, segundo o modelo de simulação.This master thesis is a project which took place in the company GrandVision. It is under the Supply Chain field of study, more precisely Warehousing; which despite having its importance underrated for many times, represents on average from one quarter to one fifth of the overall logistic costs. Regardless of the great technology break-troughs, traditional manual picker-to-part warehousing systems still represent 80% of the universe. Taking advantage of GrandVision’s management will in develop improvement projects to its warehouse; it was proposed the study, through simulation, of new Storage and Picking policies for the weekly Replenishment operation of MultiOpticas and GrandOptical Shops. The simulation models were created based on previous findings in this area of study, and results obtained are according with the ones previously reported in literature. With the conclusion of this master thesis, GrandVision’s management has in its possess a procedure of Class-Based Storage, which combined with a Batching Policy can bring, according with the simulation model, improvements around 32% of the Total Fulfillment Time