A hybrid vehicle engine selection problem using a decision support framework under uncertainty

Global warming, fueled by greenhouse gas emissions, presents a significant environmental challenge. The road transport sector contributes substantially to these emissions, with internal combustion engines in traditional vehicles constituting a primary source. As a response, this study investigates cleaner vehicle engine technology selection, such as electric and hybrid engines, in an uncertain decision-making environment. To effectively manage ambiguity, associated with human judgments and imprecise data, this paper introduces a novel multi-criteria decision-making (MCDM) method called Extended Measurement Alternatives and Ranking based on Compromise Solution (E-MARCOS). The study follows a two-stage approach: firstly, outlining the decision-making process of the E-MARCOS method customized for interval data, applying Shannon’s Entropy for interval data to ensure robust and reliable criteria weighting, and secondly, conducting the method validation. The method’s applicability is validated through three case studies, comparing the results with those obtained from other interval-based MCDM methods, including TOPSIS, VIKOR, and COPRAS. This comparison highlights E-MARCOS's impressive performance and versatility across various decision-making contexts. The method's robustness is further confirmed through uncertainty validation procedures, including sensitivity analysis, which assesses the impact of changes in weights and parameters. Additionally, E-MARCOS’s stability is verified via a rank reversal analysis, demonstrating its resilience to the addition or removal of alternatives in dynamic environments. Overall, this study presents E-MARCOS as a promising and effective approach for addressing complex decision-making problems in uncertain settings, with wide applicability in real-world scenarios.