Genetic algorithm based approaches to solve the order batching problem and a case study in a distribution center
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Genetic algorithm based approaches to solve the order batching problem and a case study in a distribution center Çagla ˘ Cergibozan1
· A. Serdar Tasan1
Received: 24 September 2019 / Accepted: 18 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The order batching problem is a combinatorial optimization problem that arises in the warehouse order picking process. In the order batching problem, the aim is to find groups of orders and picking routes of these groups to minimize distance travelled by the order picker. This problem is encountered especially in manual order picking systems where the capacity of picking vehicle is limited. Solving the order batching problem becomes more important when the size of the problem (e.g. number of storage locations, number of aisles, number of customer orders, etc.) is large. The content of the batch and picking route affect the retrieval-time of the orders. Therefore, an effective batching and routing approach is essential in reducing the time needed to collect ordered items. The main objective of this study is to develop fast and effective metaheuristic approaches to solve the order batching problem. For this purpose, two genetic algorithm based metaheuristic approaches are proposed. The numerical test of the proposed algorithms is performed with generated data sets. The proposed methods are thought to be useful to solve real-life problems in different warehouse configurations. Accordingly, a real case study is conducted in the distribution center of a well-known retailer in Turkey. The case study includes the storage assignment process of incoming products. The results demonstrate that developed algorithms are practical and useful in real-life problems. Keywords Genetic algorithms · Metaheuristics · Order batching problem · Warehousing · Logistics
Introduction Warehouses are important parts of logistics systems, and the success of warehouse operations affects the entire supply chain performance. In warehouse operations, order picking is a very costly process and should be carried out efficiently. While preparing several customer orders, all of the orders are expected to be collected at a minimum cost; therefore, batching operations are considered to minimize the collecting efforts. The related cost function may be travelled distance, elapsed time, etc. From a maximization problem perspective, the objective function may be customer satisfaction level, system performance, the number of orders completed in a predetermined time interval, etc. In the related literature, the order batching problem (OBP) is stud-
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Ça˘gla Cergibozan [email protected] A. Serdar Tasan [email protected]
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Department of Industrial Engineering, Dokuz Eylül University, 35390 Izmir, Turkey
ied with several exact, heuristic, and metaheuristic methods. As exact techniques, studies in the literature include column generation based methods (Tang et al. 2011; Muter and Öncan 2015), branch-and-bound (B&B) algorithms (Gademann et al. 2001), branch-and
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