Prasenjit Chatterjee
59353654400
Publications - 5
Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) Method: A Comprehensive Bibliometric Analysis
Publication Name: Decision Making Applications in Management and Engineering
Publication Date: 2024-01-23
Volume: 7
Issue: 2
Page Range: 313-336
Description:
This paper explores the evolution, applications, and prospective developments of a very popular multi-criteria decision-making (MCDM) method called Measurement of Alternatives and Ranking according to COmpromise Solution Method (MARCOS). Employing an extensive bibliometric analysis, the study examines 115 pertinent papers sourced from the Scopus database spanning over the years from 2020 to 2024. This study also provides an evaluation of the methodological significance and outlines potential future directions of MARCOS method. The outcomes indicate "Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS)" by Stević et al. (2020) as the most cited paper. Journals such as "Sustainability (Switzerland)", "Mathematics" and "Expert Systems with Applications" stand out among the most cited journals. "University of East Sarajevo" is an institution distinguished for its prolific research in this field. "Stević Ž." Has been identified as the most cited and published author. The most frequently used keywords are "MARCOS", "MARCOS method", and "MCDM". CRiteria Importance Through Intercriteria. Correlation (CRITIC) method is a weighting model often integrated with MARCOS method. The results of the study provide researchers and practitioners in the field of MCDM with an important insight into the current state of the MARCOS methodology, highlighted studies and potential future developments. It also provides a comprehensive overview of the importance of this method in the multi-criteria decision-making literature, shedding light on future research directions.
Open Access: Yes
An interval-valued spherical fuzzy framework for strategic renewable energy selection
Publication Name: Decision Analytics Journal
Publication Date: 2025-09-01
Volume: 16
Issue: Unknown
Page Range: Unknown
Description:
Many countries are prioritizing renewable energy sources in response to fossil fuel depletion, environmental concerns, and the need for energy resilience. This study evaluates five renewable energy alternatives: Biomass, Wind, Solar, Geothermal, and Hydro, with the aim of reducing foreign energy dependency and enhancing flexibility under potential geopolitical disruptions. A three-stage hybrid decision-making framework is proposed, integrating Modified Preference Selection Index (MPSI) and Multi-Attributive Border Approximation Area Comparison (MABAC) methods within an Interval-Valued Spherical Fuzzy (IVSF) environment. In the first stage, expert input is collected. The second stage applies IVSF-MPSI to determine the criteria weights under uncertainty. The third stage employs IVSF-MABAC to rank the alternatives based on these weights. The results indicate that Solar Energy, with a distance value of 0.2783, is the most suitable renewable energy, followed by Wind, Hydro, Geothermal, and Biomass. The proposed IVSF-MPSI-MABAC model equips decision-makers with a mathematically rigorous, uncertainty-resilient evaluation framework that supports quantitative trade-off analysis, prioritization of capital-intensive projects, and alignment of renewable energy portfolios with long-term energy security and sustainability objectives, while the integrated sensitivity analysis ensures ranking stability and robustness against variations in decision parameters.
Open Access: Yes
Preference using Root Value based on Aggregated Normalizations (PROVAN): A data-driven method for socio-economic and innovation assessment
Publication Name: Socio Economic Planning Sciences
Publication Date: 2026-02-01
Volume: 103
Issue: Unknown
Page Range: Unknown
Description:
Socio-economic development (SED) remains a critical priority for policymakers aiming to foster inclusive growth and drive national progress. This study presents a comprehensive multi-criteria assessment of regional SED across 16 Indian states, focusing on the influence of innovation (INV) performance and foreign direct investment (FDI) on achieving sustainable development goals (SDGs). A new multi-criteria decision-making (MCDM) method, called Preference using Root Value based on Aggregated Normalisations (PROVAN), is introduced in this paper to enhance decision accuracy by integrating five different normalization techniques. Criteria weights are determined using an extended version of Weights by ENvelope and SLOpe (WENSLO) method, which incorporates multiple normalization strategies to improve robustness. The evaluation considers nine SED and seven INV criteria derived from secondary data sources. The causal relationships are statistically analyzed using Somer's δ test, and the model's reliability is confirmed through comparative and sensitivity analyses. Results reveal that Maharashtra emerges as the top-performing state in both SED (1.5572) and INV (1.5473), followed by Tamil Nadu and Karnataka, indicating strong performance across socio-economic and innovation indicators. The findings highlight significant inter-state disparities and confirm that states with stronger innovation capabilities tend to achieve better socio-economic outcomes. FDI is shown to positively influence sustainable economic development, reinforcing the strategic importance of attracting capital to advance SDGs. The proposed PROVAN-WENSLO framework offers a robust and adaptable tool for regional development planning and policy formulation.
Open Access: Yes
Evaluating autonomous urban freight solutions for smart and sustainable cities: A single valued neutrosophic multiple triangles scenarios model
Publication Name: Engineering Applications of Artificial Intelligence
Publication Date: 2025-12-24
Volume: 162
Issue: Unknown
Page Range: Unknown
Description:
The evaluation of autonomous urban freight logistics (UFL) solutions is crucial due to the increasing need for efficient and sustainable transportation systems in urban areas. Optimizing UFL possibilities becomes essential for developing smart city projects as cities expand and the need for reliable logistical services increases. This study introduces an artificial intelligence (AI)-driven framework for group decision-making in UFL evaluation. It develops single-valued neutrosophic (SVN) Copula-Dombi averaging and geometric operators that act as AI reasoning tools, capable of handling uncertainty, contradiction, and indeterminacy beyond classical models. To improve reliability and agreement among decision-makers, the study also presents a consensus-based SVN Copula-Dombi multiple triangles scenarios (MUTRISS) decision support model. The model is tested on a real case in India. It evaluates drones, electric light commercial vehicles (e-LCVs), autonomous e-LCVs, and droids as UFL solutions. Twelve criteria are used, and their weights are set with an optimization model. Results show drones rank first with a score of 0.6055, followed by droids at 0.6033. The study also checks robustness through comparison and sensitivity tests. The study supports smart city logistics and sustainable urban development by assisting logistics managers and city authorities in selecting appropriate autonomous UFL systems.
Open Access: Yes
A Multi-Criteria Decision Analysis Framework to Explore Determinants of Catastrophic Healthcare Expenses
Publication Name: Societies
Publication Date: 2025-12-01
Volume: 15
Issue: 12
Page Range: Unknown
Description:
Despite significant advances in the medical sciences, out-of-pocket (OOP) healthcare costs have remained a concern, especially for lower-middle-class and poor people. The current study aims to investigate the critical factors that notably contribute to catastrophic healthcare expenses (CHCEs). To this end, the ongoing research is conducted through two phases. The first phase aims to identify the key determinants of CHCEs through expert and household evaluations. A multi-criteria decision analysis (MCDA) framework using the FullEX method is developed to analyze expert and household opinions. In the second phase, the experts investigate the hierarchical relationships among key determinants. Interpretive structural modeling (ISM) and MICMAC analysis are carried out to examine the structural relationships among the determinants. The findings of the FullEX analysis reveal that experts and households are in consensus. It is found that low-income level, number of dependable members, frequent birth rate, high child mortality, and lack of job security and risk pooling mechanisms notably contribute to the higher CHCEs. The ISM analysis indicates the strong driving power of income, education, and job security, leading to disparities in rural economic conditions, reflecting the need for holistic development. The MICMAC analysis confirms the hierarchical relationships among the key determinants of CHCEs. The findings necessitate formulating an inclusive strategy to reduce financial distress and improve the healthcare outlook for rural households, leading to sustainable development.
Open Access: Yes
DOI: 10.3390/soc15120353
