Emerging Trends and Research Insights in Fuzzy Multi-Criteria Decision-Making Applications for Logistics Location Selection: A Comprehensive Bibliometric Analysis (1982-2025)

Selecting an appropriate location is a pivotal decision in logistics. Multi-Criteria Decision-Making (MCDM) methods have gained considerable attention for their effectiveness in identifying optimal logistics sites. This paper explores the evolution, applications, and prospects of Fuzzy MCDM methods in logistics location selection. Through an extensive bibliometric analysis of 35,566 relevant papers sourced from the Web of Science (WoS) database spanning 1982 to 2025, this study uncovers key trends, influential authors, major institutions, and the geographical distribution of research contributions, employing tools such as VOSviewer 1.6.20. The findings indicate that China has established itself as the leading country in terms of published papers. At the same time, Edmundas Kazimieras Zavadskas emerges as the most prolific author in this bibliometric review. The United Kingdom is highlighted for its robust international co-authorship networks. Additionally, King Abdulaziz University is recognized as a significant institution fostering global collaborations, with Zavadskas noted as a central figure in the author collaboration network. Moreover, the journal IEEE Access is the most frequently cited publication outlet for related work. Commonly used key terms among authors include decision making, machine learning, and fuzzy logic. The findings also indicate that the most frequently discussed topics related to the United Nations Sustainable Development Goals (SDGs) are Industry, Innovation and Infrastructure, followed by Sustainable Cities and Communities and Well-being and Health. This study offers a comprehensive overview of research on Fuzzy MCDM methods for logistics location selection, providing valuable insights for future research directions in this rapidly evolving field.