Search Results (763)

This study presents a comprehensive multi-criteria decision-making (MCDM) framework for evaluating and selecting optimal non-destructive testing (NDT) techniques for oil and gas facility maintenance operations. This research used a Fuzzy Analytic Hierarchy Process (FAHP) integrated with multiple MCDM methods to assess eight NDT techniques including radiographic testing, ultrasonic testing, and thermographic testing. The evaluation framework incorporated seven technical criteria and seven economic criteria. The FAHP results revealed spatial resolution (0.175) as the most critical technical criterion, followed by depth penetration (0.155) and defect characterization (0.143). For economic criteria, downtime costs (0.210) and operational costs (0.190) emerged as the most significant factors. This study used TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment of Evaluations), and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) methods to rank NDT techniques, with results consolidated using the CRITIC (CRiteria Importance Through Intercriteria Correlation) method. The final techno-economic analysis identified radiographic testing as the most suitable NDT method with a score of 0.665, followed by acoustic emission testing at 0.537. Visual testing ranked lowest with a score of 0.214. This research demonstrates the effectiveness of combining fuzzy logic with multiple MCDM approaches for NDT method selection in offshore welding operations. Full article

The persistent issue of unemployment and the mismatch between graduate skills and labor market demands has drawn increasing attention from academics and educational institutions, especially in light of rapid advancements in technology. Emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML) offer valuable opportunities to analyze job market dynamics. In this work, we present a novel framework aimed at predicting graduate employability using current labor market data from Morocco. Our approach combines Multi-Criteria Decision-Making (MCDM) techniques with ML-based predictive models. AHP prioritizes employability factors and TOPSIS ranks skill demands—together forming input features for machine learning models. 2100 job listings obtained through web scraping, we trained and evaluated several ML models. Among them, the K-Nearest Neighbors (KNN) classifier demonstrated the highest accuracy, achieving 99.71% accuracy through 5-fold cross-validation. While the study is based on a limited dataset, it highlights the practical relevance of combining MCDM methods with ML for employability prediction. This study is among the first to integrate AHP–TOPSIS with KNN for employability prediction using real-time Moroccan labor market data. Full article

The rise of streaming services and online story-sharing has led to a vast amount of cinema and television content being viewed and reviewed daily by a worldwide audience. It is a unique challenge to grasp the nuanced insights of these reviews, particularly as context, emotion, and specific components like acting, direction, and storyline intertwine extensively. The aim of this study is to address said complexity with a new hybrid Multi Criteria Decision-Making MCDM model that combines the Deck of Cards Method (DoCM) with the Circular Pythagorean Fuzzy Set (CPFS) framework, retaining the symmetry of information. The study is conducted on a simulated dataset to demonstrate the framework and outline the plan for approaching real-world press reviews. We postulate a more informed mechanism of assessing and choosing the most appropriate deep learning assembler, such as the transformer version, the hybrid Convolutional Neural Network CNN-RNN, and the attention-based framework of aspect-based sentiment mapping in film and television reviews. The model leverages both the cognitive ease of the DoCM and the expressive ability of the Pythagorean fuzzy set (PFS) in a circular relationship setting possessing symmetry, and can be applied to various decision-making situations other than the interpretation of media sentiments. This enables decision-makers to intuitively and flexibly compare alternatives based on many sentiment-relevant aspects, including classification accuracy, interpretability, computational efficiency, and generalization. The experiments are based on a hypothetical representation of media review datasets and test whether the model can combine human insight with algorithmic precision. Ultimately, this study presents a sound, structurally clear, and expandable framework of decision support to academicians and industry professionals involved in converging deep learning and opinion mining in entertainment analytics. Full article

This study assessed ten alternatives, comprising nine power generation technologies and Battery Energy Storage Systems (BESS), using a combined hybrid approach based on group Multi-Criteria Decision-Making (MCDM) methods. Specifically, AHP was employed for determining criteria weights, while fuzzy VIKOR was utilised for ranking the alternatives. Six electricity sector experts evaluated each technology, organised within a hierarchical decision model that included four main criteria: economic, environmental, technical, and social, along with 13 subcriteria. To mitigate subjectivity in criteria weights stemming from diverse expert backgrounds, a consensus technique was implemented post-AHP. Fuzzy VIKOR was employed to address uncertainty in expert ratings. The findings revealed a significant preference towards renewable technologies, with Photovoltaic (PV) and Wind at the forefront, whereas Coal occupied the lowest position. A validation process was conducted using BWM for criteria weights and fuzzy TOPSIS for ranking alternatives. This hybrid soft computing method’s key contributions include its modular design, allowing for the sequential determination of criteria weights, followed by the calculation of alternative rankings, fostering interactive and collaborative evaluations of various energy mixes by expert groups. Additionally, the study evaluated three emerging energy technologies: BESS, Small Modular Nuclear Reactors (SMRs), and Hydrogen, highlighting their potential in the evolving energy landscape. Full article

Transportation Management Systems (TMSs) play a pivotal role in streamlining logistics operations, yet selecting the most suitable TMS software remains a complex, multi-criteria decision-making problem. This study introduces a hybrid evaluation framework combining the Bayesian Best–Worst Method (BBWM) and TOPSIS to identify, weigh, and rank software selection criteria tailored to the logistics business. Drawing on insights from 13 logistics experts, five main criteria—technological competence, service, functionality, cost, and software developer (vendor)—and 16 detailed sub-criteria are defined to reflect business-specific needs. The core novelty of this research lies in its systematic weighting of TMS software criteria using the BBWM, offering robust and expert-driven priority insights for decision makers. Results show that functionality (26.6%), particularly load tracking (35.8%) and cost (22.7%), mainly software license cost (39.8%), are the dominant decision factors. Beyond operational optimization, this study positions TMS software selection as a strategic entry point for sustainable digital transformation in logistics. The proposed framework empowers business to align digital infrastructure choices with sustainability goals such as emissions reduction, energy efficiency, and intelligent resource planning. Applying TOPSIS to a real-world case in Türkiye, this study ranks software alternatives, with “ABC” emerging as the most favorable solution (57.2%). This paper contributes a replicable and adaptable model for TMS software evaluation, grounded in business practice and advanced decision science. Full article

The paper’s priority aim is to review the scientific literature on multi-criteria analysis in the transport sector. The work is a continuation of research published in the previous works: Broniewicz, Ogrodnik, Multi-criteria analysis of transport infrastructure projects; and Broniewicz, Ogrodnik, A comparative evaluation of multi-criteria analysis methods for sustainable transport. This paper updates the literature review of the subject matter, considering scientific papers published between 2021 and 2024. Based on a literature review, the topic’s popularity under study was assessed, the most popular methods/groups of MCDM/MCDA methods applied to transportation decision-making problems were identified, and new research topics that emerged in recent years were also identified. The article also includes the case study—a multi-criteria analysis of a selected road investment in Poland. The project variant was selected using four different criteria weighting methods, and the obtained results were compared. The comparative analysis performed allowed for the assessment of the application potential of the selected MCDM/MCDA methods. Special attention was paid to the weighting methods. Based on the multi-criteria analysis, a comparable set of weights was obtained using the AHP and Fuzzy AHP methods, while different results were obtained using the CRITIC method characterized by an objective approach to weighting. The TOPSIS method was used for the final ranking of the variants of the selected real road investment. The results confirmed the ranking obtained from the official design documentation of the selected investment. Full article

The rapid growth of e-commerce has created substantial environmental impacts, driving the need for advanced optimization models to enhance supply chain sustainability. As consumer preferences shift toward environmental responsibility, organizations must adopt robust quantitative methods to reduce ecological footprints while ensuring operational efficiency. This study develops a novel hybrid multi-criteria decision-making (MCDM) model to evaluate and prioritize green suppliers under uncertainty, integrating the rough-Dombi best–worst method (BWM) and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The proposed model addresses two key challenges: (1) inconsistency in expert judgments through rough set theory and Dombi aggregation operators and (2) ranking instability via an enhanced TOPSIS formulation that mitigates rank reversal. Mathematically, the rough-Dombi BWM leverages interval-valued rough numbers to model subjective expert preferences, while the Dombi operator ensures flexible and precise weight aggregation. The modified TOPSIS incorporates a dynamic distance metric to strengthen ranking robustness. A case study of five e-commerce suppliers validates the model’s effectiveness, with results identifying cost, green competitiveness, and external environmental management as the dominant evaluation dimensions. Key indicators—such as product price, pollution control, and green design—are rigorously prioritized using the proposed framework. Theoretical contributions include (1) a new rough-Dombi fusion for criteria weighting under uncertainty and (2) a stabilized TOPSIS variant with reduced sensitivity to data perturbations. Practically, the model provides e-commerce enterprises with a computationally efficient tool for sustainable supplier selection, enhancing resource allocation and green innovation. This study advances the intersection of uncertainty modeling, operational research, and sustainability analytics, offering scalable methodologies for mathematical decision-making in supply chain contexts. Full article

The material selection process can be viewed as a multi-criteria decision-making (MCDM) problem with multiple objectives, which are often conflicting and of different importance. The selection of the most suitable biomaterial is considered as a very complex, important, and responsible task that is influenced by many factors. In this paper, a procedure for biomaterial selection based on MCDM is proposed by using a developed decision support system (DSS) named MCDM Solver. Within the framework of the developed DSS, the complete procedure for selecting the criteria weights was developed. Also, in addition to the adapted standard MCDM methods such as TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and VIKOR (VIšekriterijumsko KOmpromisno Rangiranje), an extended WASPAS (Weighted Aggregated Sum Product Assessment) method was developed, enabling its application for considering target-based criteria in solving biomaterial selection problems. The proposed MCDM Solver enables a structured decision-making process helping decision-makers rank biomaterials with respect to multiple conflicting criteria and make rational and justifiable decisions. For the validation of the developed DSS, two case studies, i.e., the selection of a plate for internal bone fixation and a hip prosthesis, were presented. Finally, lists of potential biomaterials (alternatives) in the considered case studies were ranked based on the selected criteria, where the best-ranked one presents the most suitable choice for the specific biomedical application. Full article

Trapezoidal-valued intuitionistic fuzzy numbers (TrVIFNs) are the real generalizations of intuitionistic fuzzy numbers, interval-valued intuitionistic fuzzy numbers, and triangular intuitionistic fuzzy numbers, which effectively model real-life problems that consist of imprecise and incomplete data. This study incorporates the Aczel-Alsina aggregation operators (which consist of parameter-based flexibility) for solving any group of decision-making problems modeled in a trapezoidal-valued intuitionistic fuzzy (TrVIF) environment. In this study, we first define new operations on TrVIFNs based on the Aczel-Alsina operations. Secondly, we introduce new trapezoidal-valued intuitionistic fuzzy aggregation operators, such as the TrVIF Aczel-Alsina weighted averaging operator, the TrVIF Aczel-Alsina ordered weighted averaging operator, and the TrVIF Aczel-Alsina hybrid averaging operator, and we discuss their fundamental mathematical properties by examining various theorems. This study also includes a new algorithm named ‘three-stage multi-criteria group decision-making’, where we obtain the criteria weights using the newly proposed TrVIF-MEREC method. Additionally, we introduce a new modified algorithm called TrVIF-WASPAS to solve the multi-criteria decision-making (MCDM) problem in the trapezoidal-valued intuitionistic fuzzy environment. Then, we apply this proposed method to solve a model case study problem involving location selection for a wind power plant project. Then, we discuss the proposed algorithm’s sensitivity analysis by changing the criteria weights concerning different parameter values. Finally, we compare our proposed methods with various existing methods, like some subclasses of TrVIFNs such as IVIFWA, IVIFWG, IVIFEWA, and IVIFEWG, and also with some MCGDM methods of TrVIFNs, such as the Dombi aggregation operator-based method in TrVIFNs and the TrVIF-Topsis method-based MCGDM, to show the efficacy of our proposed algorithm. This study has many advantages, as it consists of a total ordering principle in ranking alternatives in the newly proposed TrVIF-MCGDM techniques and TrVIF-WASPAS MCDM techniques for the first time in the literature. Full article

Hydrologically vulnerable areas should be identified for sustainable urban watershed management, flood mitigation, and climate-resilient infrastructure planning. However, assessing hydrological vulnerability in complex urban environments requires a comprehensive framework that integrates hydrological components and considers spatial heterogeneity. Thus, this study proposes an objective, data-driven method for identifying hydrologically vulnerable areas in urban regions using multicriteria decision-making (MCDM). The MCDM technique is used to rank the hydrological health of subwatersheds in an urbanizing watershed. Entropy-based weights are assigned to key hydrological indicators, which are computed using the soil and water assessment tool. Entropy-based weighting reveals that groundwater-related components contribute more to overall vulnerability than surface runoff. According to initial MCDM analysis, the most vulnerable areas are those in the upper reaches of the watershed, where steep slopes accelerate runoff and limit infiltration. This confounding influence of elevation is addressed by implementing topographic normalization and reevaluating subwatershed vulnerability while controlling for elevation bias. The findings underscore the importance of incorporating both hydrological and topographical factors into urban watershed vulnerability assessment and demonstrate the applicability of entropy-weighted MCDM to complex, data-scarce urban environments. The proposed framework is a replicable decision support tool for prioritizing hydrologically sensitive areas in intervention planning. Full article

Seismic retrofitting of reinforced concrete (RC) structures is essential for improving resilience and extending service life, particularly in regions with outdated building codes. However, selecting the optimal retrofitting strategy requires balancing multiple interdependent sustainability criteria—economic, environmental, and social—under expert-based uncertainty. This study presents a fuzzy hybrid multi-criteria decision-making (MCDM) approach that combines DEMATEL, DANP, and TOPSIS to represent causal interdependencies, derive interlinked priority weights, and rank retrofit alternatives. The assessment applies three complementary life cycle-based tools—cost-based, environmental, and social sustainability analyses following LCCA, LCA, and S-LCA frameworks, respectively—to evaluate three commonly used retrofitting strategies: RC jacketing, steel jacketing, and carbon fiber-reinforced polymer (CFRP) wrapping. The fuzzy-DANP methodology enables accurate modeling of feedback among sustainability dimensions and improves expert consensus through causal mapping. The findings identify CFRP as the top-ranked alternative, primarily attributed to its enhanced performance in both environmental and social aspects. The model’s robustness is confirmed via sensitivity analysis and cross-method validation. This mathematically grounded framework offers a reproducible and interpretable tool for decision-makers in civil infrastructure, enabling sustainability-oriented retrofitting under uncertainty. Full article

In multi-criteria decision-making (MCDM) environments characterized by uncertainty and data irregularities, the reliability of weighting methods becomes critical for ensuring robust and accurate decisions. This study introduces a novel hybrid objective weighting method—IQRBOW-E (Interquartile Range-Based Objective Weighting with Entropy)—which dynamically combines the statistical robustness of the IQRBOW method with the information sensitivity of Entropy through a tunable parameter $\beta$. The method allows decision-makers to flexibly control the trade-off between robustness and information contribution, enhancing the adaptability of decision support systems. A comprehensive experimental design involving ten simulation scenarios was implemented, in which the number of criteria, alternatives, and outlier ratios were varied. The IQRBOW-E method was integrated into the VIKOR framework and evaluated through average $Q$ values, stability ratios, SRD scores, and the Friedman test. The results indicate that the proposed hybrid approach achieves superior decision stability and performance, particularly in data environments with increasing outlier contamination. Optimal $\beta$ values were shown to shift systematically depending on data conditions, highlighting the model’s sensitivity and adaptability. This study not only advances the methodological landscape of MCDM by introducing a parameterized hybrid weighting model but also contributes a robust and generalizable weighting infrastructure for modern decision-making under uncertainty. Full article

Interest in surface integrity has grown in the manufacturing industry; indeed, it has become an integral part of the industry. It can be studied by examining surface roughness parameters, hardness variations, and microstructure. However, evaluating all these parameters together can be a challenging task. To address this multi-criteria decision-making model (MCDM), techniques such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Grey Relational Analysis (GRA) provide a suitable solution for optimizing the machining parameters that lead to improved product quality. This work investigated surface roughness parameters, including arithmetic average surface roughness (2D) (Ra), mean surface roughness depth (2D) (Rz), area arithmetic mean height (3D) (Sa), and maximum surface height (3D) (Sz), in conjunction with Vickers macrohardness (HV) and optical micrographs, to analyze machined surfaces during the turning of X5CrNi18-10 steel. The results suggest that machining with a spindle speed (N) of 2000 rpm or v_c of 282.7 m/min, a feed rate (f) of 0.1 mm/rev, and a depth of cut of 0.5 mm yields the best surface, achieving an “A” class surface finish. These parameters can be applied in manufacturing industries that utilize chromium–nickel alloys. Additionally, the method used can be applied to rank the quality of the product. Full article

A significant component of making intelligent decisions is optimizing algorithms. In this context, it is imperative to develop algorithms that are more efficient in order to efficiently and accurately process large quantities of intricate data. In addition, the main contribution of this study lies in the integration of optimization theory with swarm intelligence through multicriteria decision-making methods (MCDMs). This study indicates that combining dimensional analysis (DA) with particle swarm optimization (PSO) can smartly and efficiently improve analysis and decision making, resolving PSO’s shortcomings. A convergence investigation between the bat algorithm (BA), MOORA-PSO, TOPSIS-PSO, DA-PSO, and PSO is carried out to substantiate this assertion. Additionally, the ANOVA method is used to validate data dependability in order to evaluate the algorithms’ correctness. Full article

To assist stakeholders in selecting appropriate social media influencers (SMIs), this study proposes a multi-attribute decision-making framework for influencer evaluation based on their key performance metrics and engagement characteristics. This study introduces a new modification of the Evaluation Based on Distance from Average Solution (EDAS) under an interval-valued Fermatean fuzzy (IVFF) environment, addressing the limitations of the conventional EDAS method. In addition, a conceptual framework for the static and dynamic evaluation of SMIs is developed, integrating various crisp and fuzzy multi-criteria decision-making (MCDM) approaches. Empirical validation through two practical case studies demonstrates the effectiveness and applicability of the proposed framework, resulting in recommendations for marketers seeking to optimize their influencer-based marketing strategies. Full article