Tutorial on prescriptive analytics for logistics: What to predict and how to predict

Xuecheng Tian; Ran Yan; Shuaian Wang; Yannick Liu; Lu Zhen; Xuecheng Tian; Ran Yan; Shuaian Wang; Yannick Liu; Lu Zhen

doi:10.3934/era.2023116

Electronic Research Archive

2023, Volume 31, Issue 4: 2265-2285. doi: 10.3934/era.2023116

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Research article

Tutorial on prescriptive analytics for logistics: What to predict and how to predict

1.
Department of Logistics & Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
2.
School of Civil & Environmental Engineering, Nanyang Technological University, Singapore
3.
Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
4.
School of Management, Shanghai University, Shanghai, China
† The authors contributed equally to this work

Received: 04 October 2022 Revised: 17 January 2023 Accepted: 18 January 2023 Published: 24 February 2023

The development of the Internet of things (IoT) and online platforms enables companies and governments to collect data from a much broader spatial and temporal area in the logistics industry. The huge amount of data provides new opportunities to handle uncertainty in optimization problems within the logistics system. Accordingly, various prescriptive analytics frameworks have been developed to predict different parts of uncertain optimization problems, including the uncertain parameter, the combined coefficient consisting of the uncertain parameter, the objective function, and the optimal solution. This tutorial serves as the pioneer to introduce existing literature on state-of-the-art prescriptive analytics methods, such as the predict-then-optimize framework, the smart predict-then-optimize framework, the weighted sample average approximation framework, the empirical risk minimization framework, and the kernel optimization framework. Based on these frameworks, this tutorial further proposes possible improvements and practical tips to be considered when we use these methods. We hope that this tutorial will serve as a reference for future prescriptive analytics research on the logistics system in the era of big data.
- machine learning,
- predictive analytics,
- prescriptive analytics,
- optimization,
- logistics
Citation: Xuecheng Tian, Ran Yan, Shuaian Wang, Yannick Liu, Lu Zhen. Tutorial on prescriptive analytics for logistics: What to predict and how to predict[J]. Electronic Research Archive, 2023, 31(4): 2265-2285. doi: 10.3934/era.2023116

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Abstract

The development of the Internet of things (IoT) and online platforms enables companies and governments to collect data from a much broader spatial and temporal area in the logistics industry. The huge amount of data provides new opportunities to handle uncertainty in optimization problems within the logistics system. Accordingly, various prescriptive analytics frameworks have been developed to predict different parts of uncertain optimization problems, including the uncertain parameter, the combined coefficient consisting of the uncertain parameter, the objective function, and the optimal solution. This tutorial serves as the pioneer to introduce existing literature on state-of-the-art prescriptive analytics methods, such as the predict-then-optimize framework, the smart predict-then-optimize framework, the weighted sample average approximation framework, the empirical risk minimization framework, and the kernel optimization framework. Based on these frameworks, this tutorial further proposes possible improvements and practical tips to be considered when we use these methods. We hope that this tutorial will serve as a reference for future prescriptive analytics research on the logistics system in the era of big data.

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