INTUITIONISTIC FUZZY MACONT METHOD FOR LOGISTICS 4.0 BASED CIRCULAR ECONOMY INTERESTED REGIONS ASSESSMENT IN THE AGRI-FOOD SECTOR

This study aims to evaluate and prioritize the key interested regions of Circular Economy (CE) in terms of implementing the industry 4.0 technologies for the performance of logistics activities in the agri-food sector. For this purpose, we introduce a hybrid ranking framework based on Relative Closeness Coefficient (RCC)-based objective weighting model, the RANking COMparison (RANCOM) subjective weighting procedure and the Mixed Aggregation by Comprehensive Normalization Technique (MACONT) with Intuitionistic Fuzzy Information (IFI). In this framework, new IF-score function and an improved distance measure are proposed in the context of IFI to evade the limitations of existing ones. A hybrid IF-RCC-RANCOM-MACONT framework is introduced to prioritize the options over defined criteria. To prove the applicability of introduced approach, it is employed on a case study of circular economy interested regions assessment in the agri-food sector, consisting of five alternatives and nine criteria under the dimensions of sustainability. Sensitivity analysis is shown to highlight the impact of used parameters on the final outcomes. At last, a comparison with extant approaches is made to demonstrate the robustness of obtained results

Age differences in prosociality across the adult lifespan: A meta-analysis

Lifespan developmental theories and research suggest a positive effect of adult age on prosociality. However, this effect lacks consistency, with many studies excluding the period of midlife. This study summarized cross-sectional studies on adult age and prosociality, combining 120 (independent) samples (n = 103,829) in a lifespan meta-analysis approach. Linear and quadratic age effects on prosociality were analyzed, as well as comparisons between younger, middle-aged, and older adults. Prosociality was assessed via behavioral measures and self-reports. In both these domains, results indicated small linear age effects and higher prosociality in older compared to younger adults, supporting the hypothesis of increased prosociality in older age. Additionally, leveraging open data sets (64/120 independent samples), predominantly unpublished, we found some evidence for potential quadratic age effects on behavioral prosociality: Middle-aged adults exhibited higher behavioral and self-reported prosociality than younger adults, but no differences between middle-aged and older adults were observed. This meta-analysis offers new perspectives on age trajectories of prosociality, suggesting midlife as a potentially important phase of pronounced prosociality

Pension service institution selection by a personalized quantifier-based MACONT method

With the emergence of a variety of pension service institutions, how to choose a suitable institution has become a strategic decision-making problem faced by pension service demanders. To solve this problem, this study identifies key evaluation criteria of pension service institutions through the analysis of the relevant literature. Then, this study proposes a mixed aggregation by comprehensive normalization technique (MACONT) with a personalized quantifier to select pension service institutions, where the personalized qualifier with cubic spline interpolation is used to derive the position weights of criteria, and the MACONT is improved to determine the ranking of alternatives. A case study about the selection of pension service institutions is provided to verify the feasibility of the proposed model. It is found that the proposed method is effective in dealing with heterogeneous evaluation information, and the personalized quantifiers can be combined with MACONT methods to obtain an optimal solution associated with the attitude of pension service demanders. The identified key evaluation criteria are not only significant for pension service demanders, but also conducive to the further improvement of property management related to pension services

Multidimensional Performance Evaluation Using the Hybrid MCDM Method: A Case Study in the Turkish Non-Life Insurance Sector

The aim of this study is to assess and rank the financial and service network performance of seven Turkish non-life insurance companies from 2018 to 2022 using the ENTROPY- MEREC - MACONT decision model. The study evaluates multidimensional firm performance based on selected performance indicators. The weights of these indicators were determined using ENTROPY and MEREC (method based on the removal effects of criteria) procedures. The MACONT (mixed aggregation by comprehensive normalization technique) procedure is used to obtain the multidimensional performance ranking of non-life insurance companies over time. The results of the MEREC and ENTROPY procedures indicate that the number of agencies, asset size, technical profit, and return on assets are generally effective criteria for the multidimensional performance of non-life insurance companies. The MACONT ranking results show that company IC2 had the best multidimensional performance during the analysis period. The validity and consistency of the results of the proposed decision model were tested using various sensitivity analyses

Enabling organizations to strategically manage risks in circular supply chains

Circular supply chains (CSCs) prioritize resource efficiency by reusing, remanufacturing, and recycling materials, offering environmental benefits and competitive advantages. However, they are facing various risks and organizational challenges that hinder their efficiency. To ensure long-term sustainability, this study identifies the key risks and introduces a prioritization model for risk management in CSC strategies proposing a framework for the risk evaluation based on the set of criteria and prioritization model using a combination of three multicriteria decision-making (MCDM) methods. The model applies fuzzy Delphi-based analytical hierarchy process (AHP) method to determine the criteria weights and fuzzy axial distance-based aggregated measurement (ADAM) method for ranking the alternatives. The findings of this study allow for the identification of the most important risks associated with CSC enabling stakeholders to allocate resources strategically and focus efforts on crucial areas. Results highlight that the most important risks are supply chain complexity, resource availability, and quality and technological challenges. The most important contributions are the identification of the most relevant risks that threaten the resilience and sustainability of CSCs, the establishment of the framework for their evaluation, and the development of a novel hybrid MCDM model for their ranking. These contributions are also the main theoretical implications of the study. On a practical level, it enables organizations to strategically manage risks, standardize risk assessment, and improve their competitive advantage by enhancing resilience, reducing disruptions, and lowering operational cost

Development of alternative data normalization methods for the MCRAT method in multi-criteria decision-making

This study addresses the challenge of extending the applicability of the MCRAT (Multiple Criteria Ranking by Alternative Trace) method when the standard data normalization method fails due to zero elements in the decision matrix. To achieve this, the study explores alternative normalization methods. The objectives were to identify suitable normalization methods and verify their effectiveness when combined with the MCRAT method. Three cases were analyzed: ranking nine metal cutting alternatives with one "the larger the better" and three "the smaller the better" criteria, ranking nine metal milling alternatives with one "the larger the better" and one "the smaller the better" criterion, and ranking fourteen blast hole design alternatives in the mining industry with four "the larger the better" and two "the smaller the better" criteria. Despite differences in the cases, the study discovered two additional normalization methods that, when used with MCRAT, consistently identified the best alternative. This discovery confirms that MCRAT can be applied effectively even with zero elements in the decision matrix, thus significantly extending its applicability and providing enhanced decision-making benefits. By addressing this critical limitation, the study offers a significant contribution to the field of multi-criteria decision-making by expanding the range of tools available to practitioners and researchers. The enhanced MCRAT method, equipped with new normalization capabilities, is poised to become a more versatile and powerful tool in multi-criteria decision-making, ensuring that decision-makers can make more informed and accurate choices even in challenging situations. This extension marks a notable advancement, broadening the scope and utility of the MCRAT method across different sectors and decision-making scenario

Single Valued Complex Neutrosophic Set for Innovative Teaching Begins with Training: Evaluating the Success of Faculty EdTech Programs

As digital transformation continues to reshape the educational landscape, training programs aimed at equipping university faculty with technological competencies have become essential. Evaluating the effectiveness of these educational technology (EdTech) training initiatives is critical for ensuring quality instruction and meaningful technology integration. This study applies a Multi-Criteria Decision-Making (MCDM) approach to assess the success of faculty EdTech programs, identifying relevant criteria and evaluating a diverse range of training alternatives. The evaluation model not only prioritizes effectiveness but also aligns with institutional goals for innovation and sustainable professional development. The average method is used to compute the criteria weights. The MACONT method is used to rank the alternatives. The Single Valued Complex Neutrosophic set (SVCNS) is used with the MCDM approach to deal with uncertainty information. A case study is provided in this study

OPTIMIZING NON-INVASIVE REMOTE SENSING FOR GEOTHERMAL EXPLORATION WITH T-SPHERICAL DUAL HESITANT FUZZY DECISION MODEL

Traditional geothermal detection methods, such as extensive ground-based surveys and drillings, are often costly, time-consuming, and environmentally intrusive. To address these challenges, this study presents a novel hybrid fuzzy multi-criteria decision-making model to evaluate and prioritize non-invasive, cost-effective remote sensing (RS) techniques. This model uses T-spherical dual-hesitant fuzzy set to manage the inherent ambiguities in the evaluation of multiple criteria. The logarithmic percentage change-driven objective weighting technique assigns the relative importance of criteria, and the multiple triangle scenarios-II methodology helps in comprehensive evaluation and ranking. By incorporating expert judgment and addressing inherent uncertainties, this model provides a systematic framework for optimizing RS technique selection. Findings indicate that thermal infrared imaging, with a significance score of 0.7187, holds transformative potential for geothermal energy development. Sensitivity and comparative analyses further confirm the robustness of this approach. This research offers a valuable resource for energy developers and policymakers aiming to leverage RS technologies for efficient geothermal resource management and development

Evaluation of third-party logistics service providers for car manufacturing firms using a novel integrated grey LOPCOW-PSI-MACONT model

Automotive businesses often delegate logistical tasks to third-party logistics (3PLs) service providers to acquire a competitive edge in the dynamic market. Nevertheless, selecting the most suitable third-party logistics (3PL) partner is a multifaceted undertaking that needs careful evaluation of several criteria and alternatives. This research aims to introduce an integrated grey Multiple Criteria Decision Making (MCDM) framework for automotive businesses to deal with the multidimensional 3PL selection decision problem. This framework incorporates an enhanced Preference Selection Index (PSI), Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), and Mixed Aggregation by Comprehensive Normalization Technique (MACONT). The LOPCOW-G and grey PSI (PSI-G) methods extract the criterion weights, whereas the MACONT-G method ranks the alternatives. The suggested framework's practicality is shown by conducting a case study about evaluating and selecting a third-party logistics (3PLs) provider. The findings indicate that the parameters of significant importance are skilled workforce (0.0977), financial strength (0.0901), and IT-IS competence (0.0839). Furthermore, TPL4 has been recognized as the most optimum option with a value of 0.4797. The MACONT-G model is as well compared against other grey MCDM techniques to assess the validity of the proposed model. The Pearson correlation coefficient between MACONT-G and the other models based on grey sets is 0.958, suggesting a significant and positive link. Furthermore, it is worth noting that a sensitivity analysis has been conducted to validate the accuracy and reliability of the created framework. In conclusion, this study has identified managerial and policy implications that might assist policymakers and executives in effectively evaluating 3PL providers