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Learning from the past to shape the future: A comprehensive text mining analysis of OR/MS reviews

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  • Romero-Silva, Rodrigo
  • de Leeuw, Sander

Abstract

This paper provides an overview of the evolution and state-of-the-art of the Operations Research and Management Science (OR/MS) subject area from 1956 to 2019. Using text mining techniques on the content of the title, abstract, and author keywords of papers classified by the Web of Science as literature review studies in OR/MS, we found that there are 76 topical consolidated clusters in the field covering a wide range of reviewed topics. Since 2015, reviews on supply chain risk management and big data analytics have had the highest impact in the field, whereas topics such as Industry 4.0, socio-technical systems, social networks, green supply, sustainable supply chain, and resilience engineering have all received significant attention from researchers. Reviews on analytic hierarchy process were found to be the most impactful overall, showing the high relevance of multi-criteria decision making in the current research and practice contexts. Furthermore, a text mining analysis of the papers citing OR/MS literature reviews showed that optimization continues to be one of the most highly influential methodological contributions of OR/MS to other research areas and that topics such as circular economy, carbon emissions, and social commerce have yet to find some traction in OR/MS research, suggesting future research and multidisciplinary opportunities for the field. Results also show that the research area of Public Administration has been greatly influenced by OR/MS reviews as 16% of all the papers published in that field have cited at least one of the 1744 review papers included in this study. Finally, a summary table of published structured literature reviews per topic (benchmarks, classifications, taxonomies) is presented as a short bibliography of OR/MS review papers.

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  • Romero-Silva, Rodrigo & de Leeuw, Sander, 2021. "Learning from the past to shape the future: A comprehensive text mining analysis of OR/MS reviews," Omega, Elsevier, vol. 100(C).
    • Handle: RePEc:eee:jomega:v:100:y:2021:i:c:s0305048320307428
    DOI: 10.1016/j.omega.2020.102388
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