Multi-criteria decision making-based waste management: A bibliometric analysis

Sohail, Shahab Saquib; Javed, Ziya; Nadeem, Mohammad; Anwer, Faisal; Farhat, Faiza; Hussain, Amir; Himeur, Yassine; Madsen, Dag Øivind

doi:10.1016/j.heliyon.2023.e21261

Cited by 4 publications

(1 citation statement)

References 47 publications

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“…There are also bibliometric analyses specific to MCDM methods in the literature. Sohail et al [32] conducted a bibliometric study focusing on the study of MCDM-based waste management models. They analyzed articles published in Scopus between 1992 and 2022.…”

Section: Bibliometric Analysismentioning

confidence: 99%

Mapping the Evolution of Multi-Attributive Border Approximation Area Comparison Method: A Bibliometric Analysis

Demir,

Chatterjee,

Zakeri

et al. 2023

Decis. Mak. Appl. Manag. Eng.

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This paper presents a comprehensive bibliometric analysis of Multi-Attributive Border Approximation Area Comparison (MABAC) method using the Biblioshiny application of the bibliometrix package, R program and VOSviewer tools to provide a holistic view of the research landscape by identifying its evolution, major contributors and most influential research areas. The study, analyzing 264 articles from the Scopus database (January 2015 to September 2023), reveals China as the leading contributor, with India spearheading international collaboration. The most impactful publication, "The selection of transport and handling resources in logistics centres using MABAC," by Pamučar and Ćirović [2], boasts 537 citations. Notably, the "University of Defence in Belgrade" is a prominent institution in this domain. "Pamučar D" emerges as the most cited author. Key terms include "MABAC," "MABAC method," and "MCDM," commonly associated with MABAC method. The top three cited journals are "Expert Systems with Applications," "Decision Making: Applications in Management And Engineering," and "Symmetry." The study provides valuable insights for researchers, practitioners, and decision-makers interested in MABAC's applications and future developments in MCDM, contributing to ongoing discussions about its relevance.

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Section: Bibliometric Analysismentioning

confidence: 99%