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A GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0

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  • C.K.H. Lee

Abstract

In Retail 4.0, omni-channels require a seamless and complete integration of all available channels for purchasing. The diversification of channels not only diversifies data sources, but also rapidly generates an enormous amount of data. This highlights a need of big data analytics to extract meaningful knowledge for decision-making. In addition, anticipatory shipping is getting more popular to ensure fast product delivery. The goal is to predict when a customer will make a purchase and then begin shipping the product to the nearest distribution centres before the customer places the orders online. This paper proposes a genetic algorithm (GA)-based optimisation model to support anticipatory shipping. Cloud computing is deployed to store the big data generated from all channels. Cluster-based association rule mining is applied to discover the purchase pattern and predict future purchase in terms of If-Then prediction rules. A modified GA is then used to generate optimal anticipatory shipping plans. Apart from transportation cost and travelling distance, the confidence of prediction rules is also considered in the GA. A number of numerical experiments have been carried out to demonstrate the trade-off of different factors in anticipatory shipping, and the optimisation reliability of the model is verified.

Suggested Citation

  • C.K.H. Lee, 2017. "A GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 55(2), pages 593-605, January.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:2:p:593-605
    DOI: 10.1080/00207543.2016.1221162
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