Order batching in a pick-and-pass warehousing system with group genetic algorithmOmega


Jason Chao-Hsien Pan, Po-Hsun Shih, Ming-Hung Wu
Management Science and Operations Research / Strategy and Management / Information Systems and Management


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Order batching in a pick-and-pass warehousing system with group genetic algorithm$

Jason Chao-Hsien Pan a, Po-Hsun Shih b,n, Ming-Hung Wu a a Department of Logistics Management, Takming University of Science and Technology, 56 Huanshan Road, Section 1, Taipei 11451, Taiwan, ROC b Department of Information Management, Vanung University, 1 Van-Nung Road, Chung-Li, Tao-Yuan 32061, Taiwan, ROC a r t i c l e i n f o

Article history:

Received 31 July 2013

Accepted 9 May 2015


Order batching policy

Order picking

Warehouse management

Pick-and-pass system a b s t r a c t

An order batching policy determines how orders are combined to form batches. Previous studies on order batching policy focused primarily on classic manual warehouses, and its effect on pick-and-pass systems has rarely been discussed. Pick-and-pass systems, a commonly used warehousing installation for small to medium-sized items, play a key role in managing a supply chain efficiently because the fast delivery of small and frequent inventory orders has become a crucial trading practice because of the rise of e-commerce and e-business. This paper proposes an order batching approach based on a group genetic algorithm to balance the workload of each picking zone and minimize the number of batches in a pick-and-pass system in an effort to improve system performance. A simulation model based on FlexSim is used to implement the proposed heuristic algorithm, and compare the throughput for different order batching policies. The results reveal that the proposed heuristic policy outperforms existing order batching policies in a pick-and-pass system. & 2015 Elsevier Ltd. All rights reserved. 1. Introduction

Warehousing management is a vital and logistical activity that can affect supply chain costs [29]. A pick-and-pass system, also called a progressive zoning system, divides a picking line into picking zones. Each zone is then typically assigned to a picker, and they are often connected by a conveyor in a warehouse [28]. This approach is commonly used for small to medium-sized items, such as household, health and beauty, office, and food products, which can be stored in relatively small and accessible pick locations along the picking line [27]. Since the emergence of e-commerce and e-business, global supply chain management has focused on the fast delivery of small and frequent inventory orders at a low total cost [3,33]. Thus, the operations of the pick-and-pass system play a key role in managing a supply chain efficiently.

The literature survey begins with a review on the warehouse management for pick-and-pass systems, followed by the developments of order batching policy. De Koster [6] approximated picking operations by applying the Jackson network model, and assumed that the service time at each pick station was exponentially distributed, and that customer orders arrived according to a

Poisson process. Yu and De Koster [38] proposed an approximation method based on a GI/G/m queuing network modeling technique by using Whitt?s queuing network analyzer ([36]) to investigate the effects of order batching and picking area zoning on the mean order throughput time in a pick-and-pass system. Jewkes et al. [24] developed an efficient dynamic programming algorithm for determining the optimal item allocation and picker locations for an order picking line comprising multiple pickers. Jane and Laih [23] proposed several heuristic algorithms for balancing the workload among pickers in a picking line. Gagliardi et al. [11] proposed and analyzed different product location and replenishment strategies for a distribution center that uses a pick-and-pass system for fulfilling orders. Pan and Wu [31] developed an analytical model for a pick-and-pass system by describing the operation of a picker as a Markov chain to determine the expected travel distance of pickers in a picking line, and proposed three algorithms that optimally allocate items to storages. Parikh and Meller [32] proposed a cost model for estimating the cost of each type of picking strategy to mitigate the problem of selecting between batch picking and zone picking strategies. Melacini et al. [28] defined a framework for the pick-and-pass system design to minimize the overall picking costs and meet the required service level.

Order batching problem (OBP), a major decision problem in the design and control of warehousing systems [5] and a key factor for the success of an order picking system [19], determines how

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Omega http://dx.doi.org/10.1016/j.omega.2015.05.004 0305-0483/& 2015 Elsevier Ltd. All rights reserved. ?This manuscript was processed by Associate Editor Sterna. n Corresponding author. Tel.: ?886 3 4515811 ext 280; fax:?886 3 4621348.

E-mail address: shih@vnu.edu.tw (P.-H. Shih).

Please cite this article as: Pan JC-H, et al. Order batching in a pick-and-pass warehousing system with group genetic algorithm. Omega (2015), http://dx.doi.org/10.1016/j.omega.2015.05.004i

Omega ? (????) ??????? orders are combined into batches to be processed in a picking trip to reduce the travel distance of orders to be fulfilled [30]. Because obtaining precise solutions to the large-scale OBP by exerting reasonable computational efforts is impractical [9], researchers have developed heuristic methods to determine near-optimal solutions. De Koster et al. [4] combined well-known heuristics with new heuristics in an attempt to generate effective, fast, and robust order batching algorithms that are sufficiently simple to use in real-world situations. Gademann et al. [10] addressed batching in a wave picking operation, and presented an exact algorithm that assigns orders to batches to minimize the maximal lead time for each batch. Hwang and Chang [22] investigated order batch processing in which either a part of, or an entire single order or specific pair of orders may be grouped into a batch with a fixed capacity. Hsu et al. [20] developed a genetic-based algorithm for managing OBP with various types of batch structures and warehouse layouts to minimize the total travel distance. Won and