Applied Machine Learning in Operations Management

Hamsa Bastani, Dennis J. Zhang, Heng Zhang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The field of operations management has witnessed a fast-growing trend of data analytics in recent years. In particular, spurred by the increasing availability of data and methodological advancement in machine learning, a large body of recent literature in this field takes advantage of machine learning techniques for analyzing how firms should operate. In this chapter, we review applications of different machine learning methods, including supervised learning, unsupervised learning, and reinforcement learning, in various areas of operations management. We highlight how both supervised and unsupervised learning shape operations management research in both descriptive and prescriptive analyses. We also emphasize how different variants of reinforcement learning are applied in diverse operational decision problems. We then identify several exciting future directions at the intersection of machine learning and operations management.

Original languageEnglish (US)
Title of host publicationSpringer Series in Supply Chain Management
PublisherSpringer Nature
Pages189-222
Number of pages34
DOIs
StatePublished - 2022
Externally publishedYes

Publication series

NameSpringer Series in Supply Chain Management
Volume11
ISSN (Print)2365-6395
ISSN (Electronic)2365-6409

ASJC Scopus subject areas

  • Management of Technology and Innovation
  • Management Science and Operations Research
  • Control and Optimization

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