Informatica

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.

When it comes to building and sustaining a company’s financial base, financial officers (FOs) are indispensable. Consequently, hiring FOs should be fair and efficient to guarantee continuous economic growth. Evaluating their performance is crucial. The main objective of this research is to find the best financial officer. The research developed an innovative method based on the parametric representation of interval numbers to handle the uncertainty in real-life multi-criteria decision-making (MCDM) scenarios. This research considers all the essential characteristics of an FO to find the best candidate. We provide a new approach to determining the weight of each criterion and sub-criterion, the Parametric Interval Number-Analytic Hierarchy Process (PIVN-AHP). The next step in finding the best FO is to use a hybrid algorithm called PIVN-TOPSIS, which stands for Parametric Interval Number-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Several MCDM approaches, such as Simple Additive Weighting (SAW), Weighted Aggregated Sum Product Assessment (WASPAS), and the Weighted Sum Model (WSM), were used in a comparative study to confirm the ranks. We could also conduct a sensitivity study by shifting the weight of specific criteria. An FO’s evaluation focuses on key criteria and sub-factors, with PIVN-AHP used to calculate weights. “Accounts Knowledge” (C5) is the most significant criterion, while “Growth of Customer” (CW31) holds the highest sub-criterion weight.