A Bibliometric Analysis of Inverse Optimization Research: Trends, Impact, and Key Contributions

Inverse optimization is defined as a process of reverse traditional mathematical optimization where parameters of an optimization model are determined such that given decisions will be either approximately or precisely optimal. The concept of inverse optimization was initially introduced and formulated in 2001. Research in this field has both theoretical and practical applications and has been applied in various areas. This study utilizes bibliometric analysis to determine the most impactful studies and researchers in the field of inverse optimization. Our research objective is to examine the current state of inverse optimization studies, based on data from the Web of Science database. Several classifications including leading journals, most frequently cited papers and influential authors are made. The results indicate that the majority of inverse optimization applications have been in the realm of cancer treatment and that the number of studies in this field has decreased during 2019-2020, that might be due to COVID-19 pandemic. Also, the USA and MIT (Massachusetts Institute of Technology) are among the most productive and influential entities in this field.