Journals / Sigma Mühendislik ve Fen Bilimleri Dergisi / 2026 / Volume: 40 Issue: 2

The order batching problem: A state-of-the-art review

Esra Boz(KTO Karatay University, Türkiye)0000-0002-1522-1768
Nil Aras(Eskişehir Technical University, Türkiye)0000-0001-6831-9155
Pages
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Publication Date
2022-06-06
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The most widely used metaheuristic algorithms are Neighborhood Search and Genetic Algorithms.

📌 3 Key Takeaways

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Objective & Hypothesis:The Order Batching Problem (OBP) has received a lot of attention from researchers in recent years.
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Key Findings & Data:Accordingly, the most studied problem with the OBP is the Picker Routing Problem (PRP) with 17% and 51% of the studies were solved by metaheuristic algorithms.
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Conclusion & Impact:The most widely used metaheuristic algorithms are Neighborhood Search and Genetic Algorithms.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Order Batching Problem · Operations Management · Supply Chain Management

Abstract

The Order Batching Problem (OBP) has received a lot of attention from researchers in recent years. Until now, this problem has been considered as a whole with different problems or it has been studied on its own, with different solution methods, in different warehouse layouts, with different data structures. The aim of this study is to provide the researchers with a general analysis for studies using the OBP. In accordance with this purpose; the OBP is analyzed by an extensive literature review that includes all relevant studies to date. This literature review is based on a specific method and the studies are grouped into specific classes. Accordingly, the most studied problem with the OBP is the Picker Routing Problem (PRP) with 17% and 51% of the studies were solved by metaheuristic algorithms. The most widely used metaheuristic algorithms are Neighborhood Search and Genetic Algorithms.

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Cite This Article

Boz, E., Aras, N. (2022). The order batching problem: A state-of-the-art review. Sigma Mühendislik ve Fen Bilimleri Dergisi, Volume: 40 Issue: 2. https://ajindex.com/articles/1344681

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