A TODIM based decision-making framework using intuitionistic double hierarchy linguistic terms for evaluating polymer absorbing algae in marine debris management

This study proposes a novel multiple-criteria group decision-making (MCGDM) approach for addressing marine debris caused by the excessive use of synthetic materials. The method integrates the intuitionistic double hierarchy linguistic term set (IDHLTS) with the TODIM framework to enhance decision-making under uncertainty. The IDHLTS combines the features of double hierarchy linguistic term sets with intuitionistic fuzzy sets, thereby capturing both membership and non-membership information to represent expert judgments more accurately. To aggregate decision information effectively, Hamacher operational rules and aggregation operators are employed due to their flexible, parametric nature. Furthermore, transformation functions, score functions, and distance measures are formally defined, and their mathematical properties are analyzed to ensure reliability. The TODIM method is incorporated to account for decision makers’ behavioral preferences, particularly loss aversion. To illustrate its practical relevance, a real-world case study is conducted to identify the most effective algal species for the remediation of synthetic debris in marine ecosystems. Microalgae are emphasized for their ability to decompose polymers through enzymatic and bioactive secretions, converting them into smaller fragments and assimilating the released carbon as a nutrient source. By applying the proposed MCGDM framework, environmentally sustainable algae with minimal ecological disruption are identified. Comparative analysis using classical approaches to MCDM, such as TOPSIS, weighted sum model (WSM), three-way decision (TWD), and grey relational analysis (GRA), it appears that despite slight variations in the intermediate ranking, all approaches find the same best and worst alternatives. It is notable that the proposed IDHLTS-TODIM approach encompasses more discrimination between alternatives by using the group decision-making structure, and the loss aversion behavior, which are not sufficiently considered by the benchmark approaches. The sensitivity analysis at various settings of the parameters also supports the stability and soundness of the ranking results. Sensitivity analysis further validates the feasibility, robustness, and stability of the proposed method. The findings demonstrate practical value of the approach, providing policymakers, environmental researchers, and marine conservationists with a systematic decision-support tool for implementing algae-based bioremediation strategies to mitigate synthetic marine debris.