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A prescriptive analytics framework for efficient E-commerce order delivery
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DOI：10.1016/j.dss.2021.113584.png)
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摘要
En 中文
Achieving timely last-mile order delivery is often the most challenging part of an e-commerce order fulfillment. Effective management of last-mile operations can result in significant cost savings and lead to increased customer satisfaction. Currently, due to the lack of customer availability information, the schedules followed by delivery agents are optimized for the shortest tour distance. Therefore, orders are not delivered in customer-preferred time periods resulting in missed deliveries. Missed deliveries are undesirable since they incur additional costs. In this paper, we propose a decision support framework that is intended to improve delivery success rates while reducing delivery costs. Our framework generates delivery schedules by predicting the appropriate delivery time periods for order delivery. In particular, the proposed framework works in two stages. In the first stage, order delivery success for every order throughout the delivery shift is predicted using machine learning models. The predictions are used as an input for the optimization scheme, which generates delivery schedules in the second stage. The proposed framework is evaluated on two real-world datasets collected from a large e-commerce platform. The results indicate the effectiveness of the decision support framework in enabling savings of up to 10.2% in delivery costs when compared to the current industry practice.
Keyword:
Analytics
Data-driven delivery
Machine learning
Vehicle routing
E-commerce
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