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Genetic algorithm based approaches to solve the order batching problem and a case study in a distribution center

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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.

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Authors and Affiliations

  1. Department of Industrial Engineering, Dokuz Eylül University, 35390, Izmir, Turkey

    Çağla Cergibozan & A. Serdar Tasan

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  1. Çağla Cergibozan
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  2. A. Serdar Tasan
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Correspondence to Çağla Cergibozan.

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Cergibozan, Ç., Tasan, A.S. Genetic algorithm based approaches to solve the order batching problem and a case study in a distribution center . J Intell Manuf 33, 137–149 (2022). https://doi.org/10.1007/s10845-020-01653-3

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  • DOI: https://doi.org/10.1007/s10845-020-01653-3

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