Instilling eco-friendly practices within the maritime industry: An intuitionistic fuzzy decision-analytic model for terminal operating system selection in green ports

Abstract

Green port operations require efficient and sustainable terminal management, supported by robust decision-making tools for selecting a terminal operating system (TOS). This research examines the complex process of selecting TOS in the context of green ports, aiming to identify the key criteria that influence the preference for TOS in facilitating port services. The study develops an intuitionistic fuzzy (IF) set-based hybrid decision-analytic model for TOS selection to enhance the overall performance of green ports. The IF–logarithmic decomposition of criteria importance (LODECI)–Aczel–Alsina Weighted Assessment (ALWAS) model was introduced. The IF–LODECI method was formulated for criterion weighting. It incorporated the maximum decomposition approach for robust weight stabilization. The IF–ALWAS method, based on the ALWAS method, was proposed to evaluate alternatives. The new hybrid decision-analytic model integrated Aczel–Alsina t-norm and t-conorm operations, with the IF–Aczel–Alsina weighted averaging operator as the final step. The application of the model was exemplified through a case study conducted at green ports in Türkiye, focusing on environmentally conscious criteria for TOS selection, involving six experts, 10 criteria, and five alternatives. The results revealed that “berth management and scheduling” was identified as the most significant criterion, while “Navis TOS” demonstrated the highest overall performance among the alternatives. Rigorous sensitivity analyses were conducted to validate the robustness of the proposed hybrid model and algorithm. This study presents a comprehensive decision-making framework for selecting TOS in green ports, bridging the gap between theoretical advancements and practical applications.

Keywords

Terminal operating systems / Green ports / Intuitionistic fuzzy sets / Aczel–Alsina weighted assessment

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