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Order batching operations: an overview of classification, solution techniques, and future research

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  • Çağla Cergibozan

    (Dokuz Eylül University)

  • A. Serdar Tasan

    (Dokuz Eylül University)

Abstract

Companies have been trying continuously to reduce their logistics costs in the current competitive markets. Warehouses which are important components of the logistics systems draw attention in accordance with cost reducing efforts. Warehouse management is becoming more critical, as storage and distribution of the ordered items to customers is a cost-intensive process for the companies, and this process affects the customer service level directly. In warehouse order picking systems, order batching problem is encountered when several orders are demanded, and this paper reveals recent developments and particular trends for the order batching problem. This study aims to give a literature review on order batching operations in the framework of order picking process. It also displays current position and importance of the problem among warehouse operations, and presents future research directions based on key findings. The paper examines several research papers and existing reviews about order batching and order picking. Papers are selected after a systematic approach with predetermined search strategy, classified and presented with important characteristics under main headings.

Suggested Citation

  • Çağla Cergibozan & A. Serdar Tasan, 2019. "Order batching operations: an overview of classification, solution techniques, and future research," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 335-349, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1248-4
    DOI: 10.1007/s10845-016-1248-4
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    References listed on IDEAS

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    1. Lam, H.Y. & Ho, G.T.S. & Mo, Daniel Y. & Tang, Valerie, 2023. "Responsive pick face replenishment strategy for stock allocation to fulfil e-commerce order," International Journal of Production Economics, Elsevier, vol. 264(C).
    2. Iman Dayarian & Jennifer Pazour, 2022. "Crowdsourced order‐fulfillment policies using in‐store customers," Production and Operations Management, Production and Operations Management Society, vol. 31(11), pages 4075-4094, November.
    3. Anderson Rogério Faia Pinto & Marcelo Seido Nagano, 2020. "Genetic algorithms applied to integration and optimization of billing and picking processes," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 641-659, March.
    4. Heiko Diefenbach & Simon Emde & Christoph H. Glock & Eric H. Grosse, 2022. "New solution procedures for the order picker routing problem in U-shaped pick areas with a movable depot," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(2), pages 535-573, June.
    5. Shandong Mou, 2022. "Integrated Order Picking and Multi-Skilled Picker Scheduling in Omni-Channel Retail Stores," Mathematics, MDPI, vol. 10(9), pages 1-19, April.
    6. Çağla Cergibozan & A. Serdar Tasan, 2022. "Genetic algorithm based approaches to solve the order batching problem and a case study in a distribution center," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 137-149, January.
    7. Srinivas, Sharan & Yu, Shitao, 2022. "Collaborative order picking with multiple pickers and robots: Integrated approach for order batching, sequencing and picker-robot routing," International Journal of Production Economics, Elsevier, vol. 254(C).

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