Informatica

Sustainable practices are essential for long-term societal development, minimizing environmental impacts while promoting the efficient use of resources. Multi-criteria decision-making (MCDM) approaches can play a vital role in assessing and prioritizing sustainability solutions by considering diverse economic, social, and environmental factors. This study proposes a multi-criteria group decision-making approach based on the Objective Pairwise Adjusted Ratio Analysis (OPARA) method in a fuzzy environment and presents its application for the assessment of sustainable agriculture solutions. In the proposed approach, the evaluation criteria weights are determined by combining subjective weights from experts and objective weights obtained from the MEREC (Method Based on the Removal Effects of Criteria) method. The Relative Preference Relation (RPR) approach is employed for ranking fuzzy numbers and final evaluation. Sensitivity analysis and comparison with other methods are conducted to assess the robustness and validity of the proposed approach. The results demonstrate the effectiveness of the proposed approach in evaluating solutions. Based on the final evaluation from the case study, the most important criteria are “Availability and quality of water”, “Focus on immediate economic returns”, and “Financial incentives and access to credit”, while the most suitable solutions for advancing sustainable agriculture are “Financial and credit support”, “Education and enhancement of farmers’ knowledge”, and “Enhancement of research and development”.

Multi-criteria group decision-making has gained considerable attention due to its ability to aggregate diverse expert opinions and establish a preference order among alternatives. While probabilistic hesitant fuzzy (PHF) sets offer increased flexibility and generality for representing criteria values compared to traditional fuzzy and hesitant fuzzy set theories, existing aggregation techniques often fail to enhance consensus among biased expert judgments. Motivated by the need for more effective consensus-based decision-making, this paper proposes a new framework that integrates PHF set theory with Aczel-Alsina weighted averaging and geometric aggregation operators. These operators, known for their flexibility and the inclusion of an adjustable parameter, are particularly well-suited for addressing real-world decision-making challenges. The framework employs a cross-entropy based model to determine criteria weights and multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method to establish priority orders of alternatives. The proposed framework is demonstrated through a case study on manufacturing outsourcing vendor selection. The results show that Bertrandt is the most suitable vendor, with a score of 0.2390, and resources consumption is identified as the most critical criterion, with a weight of 0.20. To validate the robustness of the proposed framework, sensitivity and comparison analyses have also been conducted.

This paper develops a two-stage decision approach with probabilistic hesitant fuzzy data. Research challenges in earlier models are: (i) the calculation of occurrence probability; (ii) imputation of missing elements; (iii) consideration of attitude and hesitation of experts during weight calculation; (iv) capturing of interdependencies among experts during aggregation; and (v) ranking of alternatives with resemblance to human cognition. Driven by these challenges, a new group decision-making model is proposed with integrate methods for data curation and decision-making. The usefulness and superiority of the model is realized via an illustrative example of a logistic service provider selection.

The Best-Worst Method (BWM) is a recently introduced, innovative multi-criteria decision-making (MCDM) technique used to determine criterion weights for selection processes. However, another method is needed to complete the selection of the most preferred alternative. In this research, we propose a group decision-making methodology based on the multiplicative BWM to make this selection. Furthermore, we give new models that allow for groups with different best and worst criteria to exist. This capability is crucial in reconciling the differences among experts from various geographical locations with diverse evaluation perspectives influenced by social and cultural disparities. Our work contributes significantly in three ways: (1) we propose a BWM-based methodology for evaluating alternatives, (2) we present new linear models that facilitate decision-making for groups with different best and worst criteria, and (3) we develop a dissimilarity ratio to quantify the differences in expert opinions. The methodology is illustrated via numerical experiments for a global car company deciding which car model alternative to introduce in its markets.

This paper proposes a new multi-criteria group decision-making (MCGDM) method utilizing q-rung orthopair fuzzy (qROF) sets, improved power weighted operators and improved power weighted Maclaurin symmetric mean (MSM) operators. The power weighted averaging operator and power weighted Maclaurin symmetric mean (MSM) operator used in the existing MCGDM methods have the drawback of being unable to distinguish the priority order of alternatives in some scenarios, especially when one of the qROF numbers being considered has a non-belongingness grade of 0 or a belongingness grade of 1. To address this limitation of existing MCGDM methods, four operators, namely qROF improved power weighted averaging (qROFIPWA), qROF improved power weighted geometric (qROFIPWG), qROF improved power weighted averaging MSM (qROFIPWAMSM) and qROF improved power weighted geometric MSM (qROFIPWGMSM), are proposed in this paper. These operators mitigate the effects of erroneous assessment of information from some biased decision-makers, making the decision-making process more reliable. Following that, a group decision-making methodology is developed that is capable of generating a reasonable ranking order of alternatives when one of the qROF numbers considered has a non-belongingness grade of 0 or a belongingness grade of 1. To investigate the applicability of the proposed approach, a case study is also presented and a comparison-based investigation is used to demonstrate the superiority of the approach.

Consensus creation is a complex challenge in decision making for conflicting or quasi-conflicting evaluator groups. The problem is even more difficult to solve, if one or more respondents are non-expert and provide uncertain or hesitant responses in a survey. This paper presents a methodological approach, the Interval-valued Spherical Fuzzy Analytic Hierarchy Process, with the objective to handle both types of problems simultaneously; considering hesitant scoring and synthesizing different stakeholder group opinions by a mathematical procedure. Interval-valued spherical fuzzy sets are superior to the other extensions with a more flexible characterization of membership function. Interval-valued spherical fuzzy sets are employed for incorporating decision makers’ judgements about the membership functions of a fuzzy set into the model with an interval instead of a single point. In the paper, Interval-valued spherical fuzzy AHP method has been applied to public transportation problem. Public transport development is an appropriate case study to introduce the new model and analyse the results due to the involvement of three classically conflicting stakeholder groups: passengers, non-passenger citizens and the representatives of the local municipality. Data from a real-world survey conducted recently in the Turkish big city, Mersin, help in understanding the new concept. As comparison, all likenesses and differences of the outputs have been pointed out in the reflection with the picture fuzzy AHP computation of the same data. The results are demonstrated and analysed in detail and the step-by-step description of the procedure might foment other applications of the model.

Green supplier selection has recently become one of the key strategic considerations in green supply chain management, due to regulatory requirements and market trends. It can be regarded as a multi-criteria group decision-making (MCGDM) problem, in which a set of alternatives are evaluated with respect to multiple criteria. MCGDM methods based on Analytic Hierarchy Process (AHP) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) are widely used in solving green supplier selection problems. However, the classic AHP must conduct large amounts of pairwise comparisons to derive a consistent result due to its complex structure. Meanwhile, the classic TOPSIS only considers one single negative idea solution in selecting suppliers, which is insufficiently cautious. In this study, an improved TOPSIS integrated with Best-Worst Method (BWM) is developed to solve MCGDM problems with intuitionistic fuzzy information in the context of green supplier selection. The BWM is investigated to derive criterion weights, and the improved TOPSIS method is proposed to obtain decision makers’ weights in terms of different criteria. Moreover, the developed TOPSIS-based coefficient is used to rank alternatives. Finally, a green supplier selection problem in the agri-food industry is presented to validate the proposed approach followed by sensitivity and comparative analyses.

Nowadays most required products and services of companies are provided through other organisations. Outsourcing as a new approach has a significant role in management literature. Supplier should be selected by executives, when the organization decides to acquire a product or service from other organizations. Concerning supplier selection, the managers should consider more than one factor or criterion, which may be inconsistent and contradictory. Therefore, supplier selection is a multi-criteria decision-making issue. Analytic network process (ANP) is a technique to solve multi-criteria decision-making problems in which the criteria affect each other and have nonlinear correlation. In this study, the goal is to use ANP to select the supplier in a group decision-making.