Applied Machine Learning in Operations Management

  • Hamsa Bastani
  • , Dennis J. Zhang
  • , Heng Zhang

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    11 Scopus citations

    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
    Title of host publicationSpringer Series in Supply Chain Management
    PublisherSpringer Nature
    Pages189-222
    Number of pages34
    DOIs
    StatePublished - 2022

    Publication series

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

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