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

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2265-2285
Number of pages21
JournalElectronic Research Archive
Volume31
Issue number4
DOIs
Publication statusPublished - Apr 2023

Keywords

  • logistics
  • machine learning
  • optimization
  • predictive analytics
  • prescriptive analytics

ASJC Scopus subject areas

  • General Mathematics

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