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Order Batching Problem in O2O Supermarkets

In: AI and Analytics for Smart Cities and Service Systems

Author

Listed:
  • Kewei Zhou

    (Central University of Finance and Economics)

  • Kexin Gao

    (Central University of Finance and Economics)

  • Shandong Mou

    (Central University of Finance and Economics)

Abstract

With the development of omni-channel retailing, the O2O retailing combining traditional offline with online channels brings both opportunities and challenges to conventional supermarkets. As an important factor for supermarkets’ online service capabilities, order picking has become a critical point for the survival and development of O2O supermarkets. Thus, how to fulfil customer orders efficiently as well as accurately has become an urgent problem. In order to improve the order fulfilment performance of O2O supermarkets, we build an order batching mathematical model which aims at minimising the total Late Finish Time (LFT). A genetic algorithm is applied to solve this model. By analysing extensive numerical experiment outputs, we discuss the influences of SKU concentration of online customer orders, the mean and variance of Maximum Allowable Completion Time (MACT) on the performance of order batching and tardiness. Besides, the Degree of Congestion (DOC) which reflects the potential conflict between online order fulfilment and offline customer shopping is particularly discussed. We lastly provide managerial insights and some practical recommendations for O2O supermarkets.

Suggested Citation

  • Kewei Zhou & Kexin Gao & Shandong Mou, 2021. "Order Batching Problem in O2O Supermarkets," Lecture Notes in Operations Research, in: Robin Qiu & Kelly Lyons & Weiwei Chen (ed.), AI and Analytics for Smart Cities and Service Systems, pages 286-299, Springer.
    • Handle: RePEc:spr:lnopch:978-3-030-90275-9_24
    DOI: 10.1007/978-3-030-90275-9_24
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