A consensus facilitation model based on experts’ weights for investment strategy selection

In response to recent work on the aggregation of individual judgments on logically connected propositions into collective judgments, it is often asked whether judgment aggregation is a special case of Arrowian preference aggregation. We argue for the converse claim. After proving two impossibility theorems on judgment aggregation (using “systematicity” and “independence” conditions, respectively), we construct an embedding of preference aggregation into judgment aggregation and prove Arrow’s theorem (stated for strict preferences) as a corollary of our second result. Although we thereby provide a new proof of Arrow’s theorem, our main aim is to identify the analogue of Arrow’s theorem in judgment aggregation, to clarify the relation between judgment and preference aggregation, and to illustrate the generality of the judgment aggregation model.

Banking sector performance is important at the stage of economic growth. During the past two decades, many of developed and developing countries in the world experienced bank failures on a large scale. The Analytic Hierarchy Process (AHP) is a multiple criteria decision-making methodology designed to help decision makers in prioritizing decisions that involves both tangible and intangible criteria and has been implemented extensively all over the world. The paper employs the AHP to evaluate the performance of commercial banks in Turkey during the period 1997–1999. The main starting point for this study is to evaluate the performance of the banks in Turkey. Turkish banking industry’s financial ratios are placed in a hierarchical decision structure to calculate financial performance score for the commercial banks in Turkey. We used AHP as a systematic methodology of acquiring knowledge from experts through group decision-making. Forty-eight banks are prioritized based on 26 financial criteria using AHP. We, however, observed that failed banks ranked in lower rows. In other words, based on this analysis the decision-makers can also predict the likelihood of a banking failure. Sensitivity analysis was performed using Expert Choice. Ranking was sensitive to capital, liquidity and shares-in-sector ratios. In other words, small change in the importance of these criteria caused rank reversal of certain banks. In this paper, the banking performance score was constructed by using publicly open financial data. This framework can be used as an Early Warning System (EWS) to detect the banks that would experience serious problems and can help the decision makers to select the banks to invest in. Capability to test solution’s robustness is another superiority of the methodology since real life problems are dynamic in nature.

Group decision-making and the analytic hierarchy process: Exploring the consensus-relevant information content

The analytic hierarchy process (AHP) is recognised as one of the most commonly applied methods in the multiple attribute decision-making (MADM) literature. In the AHP, encompassing uncertainty feature necessitates using suitable uncertainty theories, since dealing efficiently with uncertainty in subjective judgements is of great importance in real-world decision-making problems. The neutrosophic set (NS) theory and grey systems are two reliable uncertainty theories which can bring considerable benefits to uncertain decision-making. The aim of this study is to improve uncertain decision-making by incorporating advantages of the NS and grey systems theories with the AHP in investigating sustainability through agility readiness evaluation in large manufacturing plants. This study pioneers a combined neutrosophic-grey AHP (NG-AHP) method for uncertain decision-making modelling. The applicability of the hybrid NG-AHP method is shown in an illustrative real-case study for agility evaluations in the Iranian steel industry. The computational results indicate the effectiveness of the proposed method in adequately capturing uncertainty in the subjective judgements of decision makers. In addition, the results verify the significance of the research in group decision-making under uncertainty. The practical outcome reveals that, to become a more sustainable agile steel producer in the case country, they should first focus on the “organisation management agility” as the most significant criterion in the assessment followed by “manufacturing process agility,” “product design agility,” “integration of information system,” and “partnership formation capability,” respectively.

Different forecasting methods can lead to very different results in power system load forecast. In order to improve forecasting accuracy, combined forecasting methods have been introduced in solving the engineering forecasting problems. In this paper, the conception of soft method is first presented in power system load forecast. The computing and analyzing methods in soft science such as decision-making analyses have been introduced to solve load forecast that are unstructured problems of multi-factors. The combined forecasting problem is treated as multi-hierarchies and multi-factors evaluation by composing qualitative analyses and quantitative calculation. In addition, the experiences and judgments of experts will be collected to implement judgment matrices in group decisionmaking. The soft method based on improved analytic hierarchy process (AHP) is proposed to carry out long-middle term load combined forecast in this paper. A hierarchy structure has been established by analyzing various factors that affect the load forecast. It is the key to determining the combined weight coefficients in the optimal combined forecasting method. Fuzzy complementary judgment matrixes of pairwise comparison will be formed by the expert in each hierarchy and be converted to a fuzzy consistent matrix. The eigenvector can be calculated using its general formula and be regarded as weight coefficient in combined forecasting. The combined forecast methods based on the improved AHP is of clear hierarchy structure, sufficient judgment information and simple calculation formula. The forecasting examples show that this method is practical, convenient and accurate.

A group decision-making model based on incomplete comparative expressions with hesitant linguistic terms

The analytic hierarchy process (AHP) is a method to help people make a complex decision by analyzing and synthesizing multiple criteria for the decision objective in a hierarchy. We adopt the AHP as a new strategy for the semantic concept detection (SCD) in video so that multiple factors involved in SCD, including multiple modalities and relating concepts, can be hierarchically analyzed and synthesized. In this paper, we first explain, by an example, why and how the SCD problem can be analyzed by the AHP. Then, following the idea of the AHP, we develop a new rank aggregation (RA) method, called AHP-RA. Experimental results of RA for SCD in video show the effectiveness of this method.

Aggregation of direct and indirect judgments in pairwise comparison matrices with a re-examination of the criticisms by Bana e Costa and Vansnick

This article aims to present a case study of a real decision problem, with the main objective of assisting an animal production company, on the installation of a new factory. When evaluating the various alternatives, organizations seek clear, objective and, if possible, mathematically represented criteria. However, decision-making involves a cognitive mental process resulting from the selection of the most appropriate course of action, based on various tangible and intangible criteria (Saaty, 2000) arbitrarily chosen by decision makers. In addition to the various criteria, we also have the participation of several decision makers with the common objective of choosing of the best place to guarantee the highest growth of the company. Several methods have been developed to aid decision-making in this scenario. In the described case, we used one of those methods, namely AHP (Analytic Hierarchy Process), to hierarchize the possible location for the installation of the new factory, being the alternatives the following: Carregado, Benavente and Covilha. In 1980, Professor L. Saaty of the University of Pittsburgh, created AHP, which allows using qualitative and/or quantitative criteria in the evaluation process. This method starts with the construction of a decision tree where the criteria, sub-criteria and the various alternatives are detailed. In a second step, those criteria are compared among the various elements at the same level of hierarchy, using a matrix created by the author - Fundamental Ratio Scale in pair-wise comparison of Saaty -, with values from 1 to 9, depending on their relevance, being 1 of equal importance and the 9 of absolute importance. The third step in the model is to calculate the relative priority of each criteria and to analyze the consistency of the judgments of the decision makers (i.e. the consistency of their responses/ comparisons). At final step, the composite priority matrix for the various alternatives is obtained, reflecting the alternatives hierarchy. According to Forman and Peniwati (1998), the behavior of a decision makers’ group is a factor that will determine how the information will be analyzed and aggregated. Should the group acts as a unit (when individuals in a group essentially desire the best for the organization they represent, regardless of their own preferences, values and goals, acting in tune and making their judgments so that the group behaves as a new individual), a variant of the AHP known as Individual Judgment Aggregation (IJA) is used. For groups that prefer to maintain individual analysis, there is Individual Priority Aggregation (IPA) alternative. In both cases it is possible to assign different weights to the decision makers in the process or to consider them with same degree of importance in terms of the decision. In both cases, the group decision is treated mathematically. In the application of the AHP to our case, two decision makers considered 3 criteria subdivided into several sub-criteria: implementation (sub-criteria: suppliers, cost of installation and distance to current clients), labor (sub-criteria: availability and qualification) and incentives (sub-criteria: social security, and taxes). In this case study, decision-makers' consensus was chosen, using IJA. The application of this method to this real case allowed us to create a model of analysis and support decision, contributing with another tool for this complex decision process.

Background/Objectives: Viral diseases remain a major threat to global public health, particularly during outbreaks when limited therapeutic resources must be rapidly and fairly distributed to large populations. Although Convalescent Plasma (CP) transfusion has shown clinical promise, existing allocation frameworks treat patient prioritization, donor selection, and validation as separate processes. This study proposes a credible, converged smart framework integrating multicriteria decision-making (MCDM) and regression-based validation within a telemedicine environment to enable transparent, data-driven CP allocation. Methods: The proposed framework consists of three stages: (i) Analytic Hierarchy Process (AHP) for weighting five clinically relevant biomarkers, (ii) dual prioritization of patients and donors using Order Preference by Similarity to Ideal Solution (TOPSIS) and Višekriterijumsko Kompromisno Rangiranje (VIKOR) with Group Decision-Making (GDM), and (iii) regression-based model selection to identify the most robust prioritization model. An external dataset of 80 patients and 80 donors was used for independent validation. Results: The external GDM AHP-VIKOR prediction model demonstrated strong predictive performance and internal consistency, with R2 = 0.971, MSE = 0.0010, RMSE = 0.032, and MAE = 0.025. Correlation analysis confirmed biomarker behavior consistency and stability in ranking, thereby reinforcing the reliability of the prioritization outcomes. Conclusions: The proposed patient–donor matching framework is accurate, interpretable, and timely. This work presents an initial step toward realizing safe AI-enabled transfusion systems within telemedicine, supporting transparent and equitable CP allocation in future outbreak settings.

Mesenchymal stem cells (MSCs) have shown promising ability to treat critical cases of coronavirus disease 2019 (COVID-19) by regenerating lung cells and reducing immune system overreaction. However, two main challenges need to be addressed first before MSCs can be efficiently transfused to the most critical cases of COVID-19. First is the selection of suitable MSC sources that can meet the standards of stem cell criteria. Second is differentiating COVID-19 patients into different emergency levels automatically and prioritising them in each emergency level. This study presents an efficient real-time MSC transfusion framework based on multicriteria decision-making(MCDM) methods. In the methodology, the testing phase represents the ability to adhere to plastic surfaces, the upregulation and downregulation of specific surface protein markers and finally the ability to differentiate into different kinds of cells. In the development phase, firstly, two scenarios of an augmented dataset based on the medical perspective are generated to produce 80 patients with different emergency levels. Secondly, an automated triage algorithm based on a formal medical guideline is proposed for real-time monitoring of COVID-19 patients with different emergency levels (i.e. mild, moderate, severe and critical) considering the improvement and deterioration procedures from one level to another. Thirdly, a unique decision matrix for each triage level (except mild) is constructed on the basis of the intersection between the evaluation criteria of each emergency level and list of COVID-19 patients. Thereafter, MCDM methods (i.e. analytic hierarchy process [AHP] and vlsekriterijumska optimizcija i kaompromisno resenje [VIKOR]) are integrated to assign subjective weights for the evaluation criteria within each triage level and then prioritise the COVID-19 patients on the basis of individual and group decision-making(GDM) contexts. Results show that: (1) in both scenarios, the proposed algorithm effectively classified the patients into four emergency levels, including mild, moderate, severe and critical, taking into consideration the improvement and deterioration cases. (2) On the basis of experts’ perspectives, clear differences in most individual prioritisations for patients with different emergency levels in both scenarios were found. (3) In both scenarios, COVID-19 patients were prioritised identically between the internal and external group VIKOR. During the evaluation, the statistical objective method indicated that the patient prioritisations underwent systematic ranking. Moreover, comparison analysis with previous work proved the efficiency of the proposed framework. Thus, the real-time MSC transfusion for COVID-19 patients can follow the order achieved in the group VIKOR results.

In the design of high-speed railway's traction power supply system, the choice for exterior power supply concerns many factors, and is a multi-criterion decision problem. In this paper, the principle of analytic hierarchy process (AHP) based on rough set theory and group decision-making is presented. An index system of exterior power supply evaluation is then constructed. Through independent decision making of each expert and the definition of the attribute significance based on the weighted sum, the weight values of alternatives is obtained and ranked, the quantitative description of decision process is realized and the optimum scheme for exterior power supply is determined. Taking an under construction high-speed railway for example, the availability and reasonableness of the proposed method is demonstrated.

On-site development of wave energy resources is a promising way to overcome power-shortage problems on islands. It is necessary to select suitable islands to deploy wave power plants, which are influenced by multiple factors related to resources, technology, economy, society, and environment. This study develops a two-stage decision framework to identify feasible islands and determine priority order based on geographic information systems (GIS) and multicriteria decision-making (MCDM). In the exclusion stage, unfeasible marine areas are excluded based on exclusion criteria and feasible island alternatives are identified. In the evaluation stage, alternatives are evaluated by evaluation criteria using the combined weighting method and the technique for order of preference by similarity to ideal solution (TOPSIS)-grey relation analysis (GRA) method. As the combined weighting method is based on the fuzzy group decision-making (GDM)-analytic hierarchy process (AHP) and the entropy method, it can effectively reduce subjective deviation. The proposed framework is applied in Shandong Province. It identifies 13 inhabited islands feasible for constructing wave power plants, among which Daguan, South Changshan, and Xiaoguan are the optimal ones. Sensitivity analysis is performed to verify the feasibility of the proposed framework. The results show that it is effective and could provide reference for practical engineering.

Mineshaft development mode design and decision-making is a complex system theory problem with significant implications for the mine’s investment cost, operational safety, and production efficiency. Because of the many factors that influence mine shaft development mode decision-making, various decision-makers have different worries and inclinations, resulting in greater subjectivity and uncertainty in the decision-making process. The concept of a specialty chain was born out of a belief in group decision-making. By merging and assessing the decision-making information of different groups in the same specialty chain, a systematic decision-making index database of the mine shaft development model was created. To elucidate the correlation model and hierarchical link between the decision-making indexes, the Interpretative Structure Model (ISM) was applied. The multilevel decision-making index system of mine shaft development mode was established. The decision-making group structure was optimized. The relative importance of the Analytical Hierarchy Process (AHP) was modified to determine the scale. A collaborative weight determination method of multiple decision-making groups was established to reduce the influence of individual subjective consciousness on decision-making results. The ISM-GAHP-FCA decision-making model of mine shaft development mode was built in conjunction with Fuzzy Comprehensive Analysis (FCA) to increase fuzzy decision-making information’s integration and analysis ability. The decision-making outcomes from the analysis of 10 typical mine shaft types in China are adaptable to the actual situation. The model can effectively express the hierarchy, significance, and fuzziness of mine shaft development mode decision-making indexes and limit the interference of decision-maker subjectivity on decision-making results.

Evaluating lecturer performance in Vietnam: An application of fuzzy AHP and fuzzy TOPSIS methods