Search Results (76)

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

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

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

Transitioning to sustainable energy systems demands the creation of innovative methods that deliver dependable and effective renewable energy technologies. CSP systems that integrate parabolic trough designs with thermal energy storage (TES) systems provide essential solutions to overcome energy intermittency challenges. Molten salts serve dual functions as heat transfer fluids (HTFs) and thermal energy storage (TES) media, making them critical to CSP system performance improvements. The study introduces a hybrid MCDM framework that combines the CRITIC method for objective weighting with the SWARA approach for expert-adjusted weighting and utilizes an enhanced Lexicographic Goal Programming to evaluate molten salt options for off-grid parabolic trough systems. The evaluation process considered melting point alongside thermal stability while also assessing cost-effectiveness, recyclability, and safety requirements. The use of Pareto front analysis helped identify non-dominated salts, which then underwent a tiered optimization process emphasizing safety, performance, and sustainability features. Results confirm that the ternary nitrate composition Ca(NO₃)₂:NaNO₃:KNO₃ offers the best overall performance across all tested policy scenarios, driven by its superior thermophysical properties. Solar Salt (NaNO₃-KNO₃) consistently ranks as a robust second choice, excelling in economic and sustainability metrics. The proposed approach provides a flexible, policy-sensitive framework for material selection tailored to enhance the efficiency and sustainability of off-grid CSP systems and support the renewable energy objectives. Full article

Urbanization and the rapid growth of e-commerce have significantly increased delivery volumes in cities, creating challenges in terms of cost, efficiency, and sustainability in last-mile delivery (LMD). To address these challenges, this paper proposes an innovative methodological framework for selecting optimal delivery strategies in urban environments, grounded in the principles of collaboration. The framework integrates an Extended FullEx method, developed to calculate criteria weights while accounting for expert reputation based on education and experience, with the MARCOS multi-criteria decision-making (MCDM) method used to rank delivery strategies. The Extended FullEx method proposed in this paper differs from the original FullEx by providing two improvements. The first concerns the introduction of the normalization procedure in the calculation of experts’ reputations, while the second addresses the different scoring of educational degrees, providing a more precise mathematical basis for the process. Four collaborative delivery strategies are evaluated against twelve sustainability-related criteria identified through an extensive literature review. The proposed framework is applied to a real-life case study in Novi Sad, Republic of Serbia. Results indicate that the most suitable delivery strategy is a hybrid model that combines the use of a consolidation center with smaller urban delivery hubs, providing practical insights for enhancing the sustainability and efficiency of urban delivery. This study contributes both methodologically, by advancing MCDM techniques, and practically, by offering decision-makers a comprehensive tool that integrates subjective expert knowledge and objective criteria assessment in the selection of sustainable LMD solutions. Full article

Sensor selection in IoT-based smart healthcare systems is a complex fuzzy decision-making problem due to the presence of numerous uncertain and interdependent evaluation criteria. Traditional fuzzy multi-criteria decision-making (MCDM) approaches often assume independence among criteria and rely on aggregation operators that impose sharp transitions between preference levels. These assumptions can lead to decision outcomes with insufficient differentiation, limited discriminatory capacity, and potential issues in consistency and sensitivity. To overcome these limitations, this study proposes a novel fuzzy decision-making framework by integrating Quasi-D-Overlap functions into the fuzzy MARCOS (Measurement of Alternatives and Ranking According to Compromise Solution) method. Quasi-D-Overlap functions represent a generalized extension of classical overlap operators, capable of capturing partial overlaps and interdependencies among criteria while preserving essential mathematical properties such as associativity and boundedness. This integration enables a more intuitive, flexible, and semantically rich modeling of real-world fuzzy decision problems. In the context of real-time health monitoring, a case study is conducted using a hybrid edge–cloud architecture, involving sensor tasks such as heartrate monitoring and glucose level estimation. The results demonstrate that the proposed method provides greater stability, enhanced discrimination, and improved responsiveness to weight variations compared to traditional fuzzy MCDM techniques. Furthermore, it effectively supports decision-makers in identifying optimal sensor alternatives by balancing critical factors such as accuracy, energy consumption, latency, and error tolerance. Overall, the study fills a significant methodological gap in fuzzy MCDM literature and introduces a robust fuzzy aggregation strategy that facilitates interpretable, consistent, and reliable decision making in dynamic and uncertain healthcare environments. Full article

The scientific investment decision of Park-level Integrated Energy System (PIES) projects is of great significance to energy enterprises for improving the efficient utilization of funds, promoting green and low-carbon transformation, and achieving the goal of carbon neutrality. This paper proposed a two-stage investment framework that integrates a multi-objective 0–1 programming model with a multi-criteria decision-making (MCDM) technique to determine the optimal PIES project investment portfolios under the constraint of quota investment. First, a multi-objective (MO) 0–1 programming model was constructed for typical PIES projects in Stage-I, which considers economic and environmental benefits to obtain Pareto frontier solutions, i.e., PIES project portfolios. Second, an evaluation index system from multiple dimensions was established, and a hybrid MCDM technique was adopted to comprehensively evaluate the Pareto frontier solutions in Stage-II. Finally, the proposed model was applied to an empirical case, and the simulation results show that the decision framework can achieve the best overall benefit of PIES project portfolios with maximal economic benefit and minimum carbon emissions. In addition, the robustness analysis was performed by changing the indicator weights to verify the stability of the proposed framework. This research work could provide a theoretical tool for investment decisions regarding PIES projects for energy enterprises. Full article

Heightened environmental policies along with the necessity for a resilient supply chain (SC) network have driven companies to adopt circular economy (CE) strategies. Although CE initiatives have shown significant effects on SC operations, the advent of digital technologies is encouraging businesses to digitize their SCs. However, the relationship connecting SC digitalization with CE practices remains underexplored. This study presents a novel framework that bridges the gap between CE principles and SC digitalization by identifying and prioritizing critical success factors (CSFs) for implementing SC4.0 in a circular economy context. We conducted a comprehensive literature review to determine CSFs and approaches relevant to Supply Chain 4.0 (SC4.0), and expert insights were gathered using the Delphi method for final validation. To capture the complex interrelationships among these factors, the study employed a combined approach using Intuitionistic Fuzzy Set (IFS), Analytic Network Process (ANP), decision-making trial and evaluation laboratory, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) techniques to assess the CSFs and strategies. The findings highlight that an intelligent work environment, performance tracking, and data accuracy and pertinence are the top three critical CSFs for SC digitalization. Furthermore, enhancing analytical capabilities, optimizing processes through data-driven methods, and developing a unified digital platform were identified as key strategies for transitioning to SC4.0. By embedding CE principles into the evaluation of digital SC transformation, this research contributes a novel interdisciplinary perspective and offers practical guidance for industries aiming to achieve both digital resilience and environmental sustainability. The study delivers a comprehensive evaluation of CSFs for SC4.0, applicable to a variety of sectors aiming for digital and sustainable transformation. Full article

Industrial enterprises and their supply chain partners are increasingly seeking methods to optimise production and logistics processes while pursuing sustainable development goals. The complexity and high risk associated with implementing Industry 4.0 technologies calls for structured decision-making support. This study presents a novel multi-criteria evaluation framework that integrates technological, organisational, and sustainability dimensions to support strategic transformation efforts. The proposed model comprises four subspheres of manufacturing, four subspheres of supply chain and logistics, twenty-three emerging technologies, and four sustainability perspectives adapted to industrial contexts. A hybrid MCDM approach combining DEMATEL and PROMETHEE II is applied to identify causal relationships, prioritise technologies, and rank sustainability priorities across different dimensions. The methodology enables companies to determine which technologies should be implemented first and how these relate to broader sustainability objectives. The results provide a structured roadmap for decision-makers, highlighting five key strategic areas for the sustainable implementation of Industry 4.0. In addition to its managerial relevance, the proposed model offers scientific novelty by bridging previously siloed research areas and demonstrating a data-driven approach to transformation planning. Full article

This study evaluates four hybrid infrastructure scenarios for supporting battery electric buses (BEBs) and fuel cell electric buses (FCEBs), analyzing different combinations of grid power, solar energy, battery storage, and fuel cell systems. A multi-stage framework—comprising energy demand forecasting, infrastructure capacity planning, and multi-criteria decision-making (MCDM) evaluation incorporating total cost of ownership (TCO), carbon emissions, and energy resilience—was developed and applied to a real-world transit depot. The results highlight critical trade-offs between financial, environmental, and operational objectives. The limited rooftop solar configuration, integrating solar energy through a Solar Power Purchase Agreement (SPPA), emerges as the most cost-effective near-term solution. Offsite solar with onsite large-scale battery storage and offsite solar with fuel cell integration achieve greater sustainability and resilience, but they face substantial cost barriers. The analysis underscores the importance of balancing investment, emissions reduction, and resilience in planning zero-emission bus fleets. Full article

Selecting an appropriate business development model is central to strategic decision-making in economic and business management. These models shape sustainable growth, long-term scalability, and strategic flexibility. Existing evaluation methods rely on heuristic or qualitative judgments that lack transparency, reproducibility, and sensitivity to evaluation criteria. To address these limitations, this study introduces a hybrid multi-criteria decision-making (MCDM) framework that integrates VIKOR, entropy weighting, and simulation to evaluate 35 business development models derived from 245 real-world cases. The evaluation covers six strategic criteria: scalability, adaptability, risk exposure, financial sustainability, implementation complexity, and market relevance. Entropy weighting assigns criterion importance based on data variability, and simulation generates input sets for sensitivity and stability analysis. Results highlight Cross-Border Investment, Tiered Access, and Crowd-Backed models as top-performing strategies across multiple dimensions. By combining multiple tools in a unified framework, the research advances MCDM methodology and supports strategic business development planning under uncertainty. This contribution strengthens both academic insight and managerial practice in economics and business management. Full article

The Tibet Autonomous Region presents immense potential for concentrated solar power (CSP) development, driven by its exceptional solar irradiance levels (e.g., a peak DNI exceeding 2100 kWh/m²/day). This positions it as a strategic contributor to China’s 2060 carbon neutrality target and aligns with global energy transition imperatives. However, CSP deployment in this region faces challenges stemming from unique high-altitude geographic characteristics, a complex terrain, and extreme climatic conditions—including pronounced diurnal temperature fluctuations, high wind speeds, and heavy winter snowfall. Additionally, traditional site-selection models inadequately address these region-specific environmental constraints. To address these limitations, an integrated framework combining geographic information systems (GIS) and multi-criteria decision-making (MCDM) is proposed in this study. A localized evaluation system is developed, incorporating four novel high-altitude-specific indicators: the average and maximum wind speed and the average and maximum snow depth. Criteria weights are determined through a hybrid approach integrating the analytic hierarchy process (AHP) and the entropy weight method (EWM), while candidate sites are prioritized using the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) ranking method. The case study results demonstrate that region-specific environmental factors exert a significantly stronger influence on site suitability than traditional solar resource indicators (e.g., direct normal irradiance) under Tibet’s extreme climatic conditions, emphasizing the necessity of localized evaluation frameworks. The proposed methodology not only provides a robust scientific foundation for CSP site selection in high-altitude regions with environmental complexities but also establishes a replicable framework for optimizing multiple trade-offs in renewable energy systems under geographically complex conditions. Full article

The utilization of hydrogen energy presents new opportunities for renewable energy integration, and the hybrid electricity–hydrogen synergy system exhibits significant potential for renewable energy accommodation and multi-scenario applications. To comprehensively explore the potential of such systems, this study proposes a two-stage design methodology that integrates HOMER simulation with multi-criteria decision-making (MCDM). Using Baotou, Inner Mongolia as a case study, HOMER is employed for simulation and optimization, and a comprehensive evaluation index system encompassing energy, economic, and environmental dimensions is established to assess the potential Cases and identify the optimal one. This study proposes an innovative weighting model combining CRITIC, Grey-DEMATEL, and Huber loss function. The model effectively resolves conventional methods’ deficiencies in balancing subjective–objective factors. Furthermore, an enhanced GRA-VIKOR model is developed to overcome the inherent constraints of conventional VIKOR approaches, particularly their excessive dependence on indicator weights and decision-maker preferences. The experimental results reveal that systems with 50% wind power integration demonstrate the optimal comprehensive development potential, while the developed MCDM framework successfully confines indicator weight deviations within the range of 0.016–0.019. Full article

To systematically address the intricate multiple criteria decision-making (MCDM) challenges to practical situations where uncertain and hesitant information plays a critical role in guiding optimal choices. In this article, we introduce the concept of $\mathfrak{m}$-polar Q-hesitant fuzzy ($\mathcal{MPQHF}$) $\mathfrak{B}\mathrm{C}\mathbb{K}/\mathfrak{B}\mathrm{C}\mathbb{I}$ algebras, combining $\mathfrak{m}$-PFS theory with Q-hesitant fuzzy set theory in the framework of $\mathfrak{B}\mathrm{C}\mathbb{K}$/$\mathfrak{B}\mathrm{C}\mathbb{I}$ algebras. This innovative approach enhances the attitudes of uncertainty, vagueness, and hesitance of data in decision-making processes. We investigate the features and actions of this proposed hybrid approach to fuzzy sets and hesitant fuzzy sets, focusing on $\mathcal{MPQHF}$ subalgebras, and explore the characteristics of several kinds of ideals under $\mathfrak{B}\mathrm{C}\mathbb{K}$/$\mathfrak{B}\mathrm{C}\mathbb{I}$ algebras. It also showed that it can better represent complex levels of uncertainty than regular sets. The proposed method’s theoretical framework offers a better way to show uncertain data in areas like engineering, computer science, and computational mathematics. By linking theoretical advancements of $\mathcal{MPQHF}$ sets with practical applications, we highlight the benefits and challenges of this approach. Demonstrating the practical uses of the $\mathcal{MPQHF}$ sets aims to encourage broader adoption. Symmetry plays a vital role in algebraic structure and is used in various fields like decision-making, encryption, pattern recognition problems, and automata theory. Furthermore, this work enhances the understanding of algebraic structures and offers a robust tool for career exploration and development through improved decision-making methodologies. Full article