Search Results (140)

In this study, a multicomponent composite film system based on alginate, chitosan, κ-carrageenan, agar, and Rapana venosa protein hydrolysate (RVPH) was developed, and the effect of RVPH incorporation (0–1.5%) on molecular interactions, microstructure, and functional performance was evaluated using FTIR, SEM, mechanical testing, optical analysis, and water-related measurements. FTIR results indicated that RVPH interacted with the polysaccharide matrix mainly through hydrogen bonding and ionic interactions without causing chemical degradation. SEM analysis revealed concentration-dependent microstructural changes, with smoother morphologies at low RVPH levels and increased roughness and heterogeneity at higher concentrations. These structural differences were reflected in the functional properties. All films exhibited high swelling and water solubility. Optical properties were significantly affected by RVPH. Mechanical properties exhibited a non-linear response, with numerical variations observed but no statistically significant differences (p > 0.05). The EDAS and SWARA methods were employed to determine the optimal incorporation level of RVPH in the film formulations. Among the RVPH-containing films, the formulation incorporating 1% RVPH was identified as the most suitable alternative. Full article

This study aims to identify and prioritize the key factors influencing export performance among Turkish exporters, based on the resource-based view (RBV) and industrial organization theory (IO), categorizing the factors as internal and external, and employing the Stepwise Weight Assessment Ratio Analysis (SWARA). Twenty-five factors across Internal (IF) and External (EF) categories were evaluated through expert assessments. Results reveal that Internal Factors (58.0%) significantly dominate External Factors (42.0%), indicating that Turkish exporters possess substantial control over their export competitiveness. The top five critical factors are Management and Leadership (9.6%), Strategy (6.2%), Technological Change (5.3%), Industry and Sector Activity (5.0%), and Competitors (5.0%). Surprisingly, traditional factors such as firm size, international experience, and digitalization ranked much lower, challenging conventional assumptions about export success. A leave-one-out (LOO) sensitivity analysis further validated the robustness of these rankings, with Management and Leadership, and Strategy emerging as the most stable and dominant factors across all scenarios. The predominance of management and strategic factors over structural characteristics suggests that even smaller, less experienced companies can achieve export success through effective leadership and strategic planning. These findings contribute theoretically by supporting the notion that the resource-based view has a greater impact on export performance than the industrial organization theory, and they provide practical guidance for companies to focus on managerial and leadership skills, organizational capabilities, and strategic approaches to enhance export investments. The study presents the first comprehensive SWARA-based ranking of export performance factors in the Turkish context, providing empirical evidence to support the internal-external factor debate in the international business literature. Full article

E-commerce has been a rapidly growing sales channel in recent years, with a strong trend toward further expansion. However, logistics companies face significant challenges in the preparation and sorting of orders when delivering shipments purchased through e-commerce platforms. In this process, order pickers play a pivotal role, as their efficiency directly impacts both the operational performance of logistics companies and the quality of service provided to customers. During peak periods of high order volumes, it is common for order pickers to exceed the prescribed work norm, making them eligible for performance-based bonuses. This study aims to develop a model for evaluating order picker efficiency, ranking them, and determining the optimal allocation of bonuses. It addresses a critical gap in the existing literature, as only a handful of studies have explored this issue in depth. To assess the efficiency of 56 order pickers, the DEA method was applied, incorporating three input and five output variables. The analysis identified 18 order pickers as fully efficient. These individuals were then ranked using the IMF SWARA and COPRAS methods, where IMF SWARA was employed to determine the weights of nine evaluation criteria, while COPRAS was used for the final ranking process. Based on the ranking results, a structured bonus allocation model was developed, encompassing four distinct scenarios. Furthermore, a sensitivity analysis and model validation were conducted to ensure the robustness and reliability of the proposed approach. Full article

This study presents an integrated Multi-Criteria Decision-Making (MCDM) approach to select the most suitable sustainable packaging for logistics operations under uncertainty. The aim of this study is to identify the most suitable eco-friendly packaging options for reducing packaging waste, by considering several criteria. The methodology combines the SWARA and TOPSIS methods within a Fermatean Fuzzy Set (FFS) framework to address the ambiguity in expert evaluations and the qualitative nature of decision-making criteria. The research considers various sustainable packaging alternatives, including recycled cardboard, recycled plastic, biodegradable plastic, and compostable plastic, while incorporating criteria such as production cost, environmental impact, reusability, and material specifications. The approach offers a robust and comprehensive decision-making tool for companies aiming to improve sustainability in their logistics operations while mitigating the environmental impact of packaging waste. The results demonstrate that the direct incorporation of fuzzy numbers notably influences the ranking outcomes compared to traditional methods, and comparing the considered approach with different MCDM methods yields various recommendations for sustainable packaging selection. Full article

Land readjustment (LR) constitutes the foundation of orderly and sustainable urbanization, serving as the primary implementation tool for development plans. LR implementations are generally addressed within the framework of development implementation models—namely area-based, value-based, and hybrid models—based on the principle of redistribution. The present study aims to implement an equivalence-based LR model in the Davultepe Neighborhood of Mezitli, Mersin. In addition, it compares an equivalence-based LR implementation with an area-based LR implementation. The area-based LR implementation was conducted according to Article 18 of Law No. 3194 within the scope of Turkish Zoning Legislation. The equivalence-based implementation was performed using the hybridized multi-criteria decision analysis methods—specifically, SWARA and WASPAS. Cadastral and zoning criteria were determined separately. For data related to spatial criteria, walking distances were calculated using network analysis in Geographic Information Systems software. The weighting of the criteria was performed using the SWARA method. Cadastral and zoning parcels were treated as alternatives, and the WASPAS weight for each parcel was determined. The results indicate that, although allocated zoning parcel areas were generally smaller than the original cadastral parcel areas, in some cases, they exceeded the cadastral parcel areas due to the allocation of zoning parcels designated for agricultural use. Full article

The transition to renewable energy technologies is one of the most important ways to achieve the sustainable development goals (SDGs) of affordable and clean energy (SDG7); industry, innovation and infrastructure (SDG9); responsible production and consumption (SDG12); and climate action (SDG13). The widespread use of renewable energy technologies in developing countries will reduce dependence on imported fossil resources, increase industrial competitiveness, and support low-carbon development. Despite all their advantages, the integration of renewable energy technologies into industrial and domestic systems in developing countries remains slow due to a number of barriers. Financial constraints, technical and technological deficiencies, political restrictions and uncertainties, and organizational and managerial inadequacies are some of the barriers to the widespread adoption of renewable energy technologies. This study aims to identify, classify, and prioritize the barriers to the implementation of renewable energy technologies by applying multi-criteria decision-making methods in a fuzzy environment, with Türkiye considered as a case study. The relative importance of the barriers identified using the Single-Valued Spherical Fuzzy SWARA method was assessed, and their interconnections and significance were systematically demonstrated. The findings will contribute to the development of policy and management strategies aligned with global sustainability goals, thereby facilitating a more effective and equitable transition to clean and resilient energy systems. Full article

Blockchain technology has attracted considerable attention in the supply chain literature for its potential to enhance operational traceability, transparency, and trust, as well as to advance greening initiatives. Given current supply chain configurations, exploring barriers to implementation is a consequential agenda, and current studies have devoted substantial effort to identifying and offering guidance to address them. Despite recent findings, insights into how blockchain technology adoption can support green supply chain management are missing, particularly in the maritime sector, which receives limited attention. Thus, this work outlines a methodological approach to examine the suitability of maritime routes for addressing barriers to implementing blockchain technology in green supply chain management. Viewing the evaluation as a multi-criteria decision-making (MCDM) problem, the proposed approach performs the following actions on a case study evaluating four maritime lines. Firstly, from the 13 identified barriers in the literature review and expert interviews, nine relevant barriers were determined after one round of a Delphi process. These barriers eventually comprise the set of evaluation criteria. Secondly, to satisfy the assumption of criterion independence in most MCDM methods, this work proposes a novel extended grey relational analysis (GRA) that allows for the measurement of criterion independence based on the concept of grey relational space. Proposed here for the first time, the extended GRA offers a distribution-free overall independence index for each criterion based on pattern similarity. Finally, an integration of SWARA (Stepwise Weight Assessment Ratio Analysis) and ARAS (Additive Ratio Assessment) methods under Z-information is developed to address the evaluation problem involving expert judgments in a highly uncertain decision-making context. Results show that transaction-level uncertainty is the most critical barrier to blockchain adoption, followed by technology risks and higher sustainability costs. Among the four maritime lines, Line 3 is best prepared for a blockchain-enabled green supply chain. The agreement between these results and those of other MCDM methods is shown in the comparative analysis. Also, ranking remains unchanged even when the criteria weights are adjusted. The proposed approach provides a computationally efficient and tractable framework for maritime managers to make informed decisions about blockchain adoption to promote green supply chains. Full article

Rapid progress in sustainable and intelligent transportation has intensified interest in smart airport initiatives, driven by the need to support environmentally responsible and technology-enabled aviation development. As complex sociotechnical subsystems of smart aviation, smart airports integrate advanced digital, operational, and organizational technologies to enhance efficiency, resilience, and passenger experience. With increasing emphasis on such transformations, multiple strategic development plans have emerged, each with distinct priorities and implementation pathways, which necessitates a rigorous and transparent evaluation mechanism to support informed decision-making under uncertainty. This study proposes an integrated spherical fuzzy multi-criteria decision-making (MCDM) framework for assessing and ranking smart airport development plans. Subjective expert judgments are modeled using spherical fuzzy sets, allowing for the simultaneous consideration of positive, neutral, and negative membership degrees to better capture linguistic and ambiguous information. Expert importance is determined through a hybrid weighting scheme that combines a social trust network model with an entropy-based objective measure, thereby reflecting both relational credibility and informational contribution. Criterion weights are computed through an integrated approach that merges criteria importance through the inter-criteria correlation (CRITIC) method with the stepwise weight assessment ratio analysis (SWARA) method, balancing data-driven structure and expert strategic preferences. The weighted evaluations are aggregated using a spherical fuzzy extension of the combined compromise solution (CoCoSo) method to obtain the final rankings. A case study involving smart airport development planning in China is conducted to illustrate the applicability of the proposed approach. Sensitivity, ablation, and comparative analyses demonstrate that the framework yields stable, discriminative, and interpretable rankings. The results confirm that the proposed method provides a reliable and practical decision support tool for smart airport development and can be adapted to other smart transportation planning contexts. Full article

The slab system can account for a substantial portion of the structural cost; an optimized choice is essential for the financial success of a project. Despite its importance, existing research often relies on limited pairwise comparisons or single-criterion analyses (e.g., cost only), failing to provide a holistic framework. A significant gap exists in the application of a formal, quantitative Value Engineering (VE) approach that systematically balances function against cost. This study aims to fill this gap by developing a robust multi-criteria decision-making (MCDM) model to determine the optimal structural slab system for high-rise buildings based on the principles of Value Engineering. Unlike previous studies limited to pairwise comparisons or single-criterion analyses, this research simultaneously evaluates eight diverse slab alternatives across eight weighted performance criteria, providing a comprehensive value-based framework for systematic slab selection. First, eight key evaluation criteria were identified and weighted using the Step-wise Weight Assessment Ratio Analysis (SWARA) method, based on input from a panel of industry experts. Subsequently, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to evaluate the performance of eight distinct slab alternatives, including conventional, voided, and precast systems. The TOPSIS ranking scores were then integrated with normalized cost data to calculate a Value Engineering index, enabling quantitative comparison and final ranking of alternatives. The main finding revealed that the Post-Tension Slab offers the highest value (VE score = 2.467), achieving a superior balance of high performance—particularly in speed and structural efficiency—and low normalized cost. Interestingly, the traditional Solid Slab ranked a close second (VE score = 2.418). Practically, this study provides project managers, developers, and engineers with a transparent, data-driven decision-making tool to justify slab selection beyond mere cost-cutting, ensuring an optimal balance between cost, schedule, and functional performance. The study provides project managers, developers, and engineers with a transparent, data-driven decision-making tool to justify slab selection beyond cost considerations. Full article

Effectively managing Construction and Demolition Waste (CDW) requires prioritizing multi-dimensional risks, a task complicated by the inherent uncertainty and subjectivity of expert judgments. While classical methods like Failure Modes and Effects Analysis (FMEA) and Fine–Kinney (FK) provide a diagnostic structure, they struggle to capture the vagueness in subjective assessments. This study addresses this gap by developing an integrated framework that couples the classical FMEA/FK criteria (Severity, Exposure, Probability, Detectability, Frequency) with Picture-Fuzzy (PiF) multi-criteria decision making. The methodology first elicits criterion importances from 15 experts using PiF Stepwise Weight Assessment Ratio Analysis (PiF-SWARA), which retains approval, indeterminacy, rejection, and refusal degrees to reduce information loss. Subsequently, it ranks 40 risk factors using the PiF Technique for Order Preference by Similarity to Ideal Solution (PiF-TOPSIS). Results show severity is the most influential criterion, followed by exposure and probability. The framework identifies the highest-priority risks as cumulative pollution with rising complaints, groundwater leakage, and insufficient investment/operating budgets. A sensitivity analysis confirms that environmental and financial risks remain consistently prominent across various weighting scenarios. This harmonized FMEA/FK–PiF-SWARA–TOPSIS approach yields a transparent and defensible prioritization, offering a practical tool for managers to allocate resources effectively, focusing on critical environmental controls and addressing core financial deficiencies in CDW systems. Full article

Medicine and vaccine supply chains in Nigeria are socio-technical systems exposed to persistent uncertainty and disruption. Existing studies rarely integrate systems thinking with uncertainty-aware decision tools to jointly prioritize challenges and policy responses. This study asks which policy mix most effectively strengthens these supply chains while balancing multiple, conflicting criteria and stakeholder judgments. We develop a two-stage Fermatean fuzzy framework that first weights 35 challenges using Fermatean Fuzzy Stepwise Weight Assessment Ratio Analysis (FF-SWARA) and then ranks four policy alternatives via Fermatean Fuzzy VIšeKriterijumska Optimizacija I Kompromisno Resenje (FF-VIKOR), based on expert elicitation and linguistic assessments. Results identify interruption of drug supplies, limited vaccine funding, cold-chain potency loss, human resource shortages, and product damage as the most critical challenges. FF-VIKOR prioritizes Effective Implementation of Existing Policies as the best alternative, followed by Improving Access to Medicines and Vaccines, indicating that governance quality and access-enabling infrastructure are complementary levers for resilience. To further enhance robustness, we embed the VIKOR outcomes into a policy-oriented game-theoretic analysis, where strategic weighting scenarios (e.g., cost-focused, infrastructure-driven, human-capital focused) interact with policy choices. The equilibrium results reveal that a mixed strategy combining Effective Implementation of Existing Policies and Strengthening Distribution and Storage Systems guarantees the best compromise performance across adversarial scenarios. The proposed framework operationalizes systems thinking for uncertainty-aware and strategically robust policy design and can be extended with real-time data integration, scenario planning, and regional replication to guide adaptive supply chain governance. Full article

This study develops a comprehensive decision-making approach to sustainable product selection for chemical industry supply chains under uncertainty. Five product categories -enamel, ceramics, pigments, non-stick coatings, and glass- were evaluated through fifteen criteria along environmental, economic, and social sustainability dimensions. The hybrid methodology combines Fuzzy SWARA, which weights criteria based on expert opinion, with Fuzzy ARAS, which ranks the alternatives accordingly. The study found that occupational health and safety, consumer safety and health, and water usage are the most important criteria, reflecting a human-centered approach to sustainability decision-making. Ceramics had the best performance score, followed by enamel and non-stick coating. Sensitivity analysis confirmed the robustness of these rankings across various weighting scenarios. The findings indicate that decision-makers in the chemical industry prioritize worker and consumer protection alongside environmental resource stewardship. This framework provides practitioners with a structured method for integrating sustainability considerations into supply chain product portfolio decisions, balancing environmental impact, economic performance, and social responsibility. Full article

The emergence of Industry 5.0 brings a paradigm shift towards collaborative environments where humans and intelligent robots work side-by-side, enabling personalized, flexible, and resilient manufacturing. However, integrating humans and robots introduces new operational and safety risks that require proactive and adaptive control strategies. This study proposes an intelligent multi-criteria decision-making framework for smart risk assessment and the selection of optimal adaptive control strategies in human–robot collaborative manufacturing settings. The proposed framework integrates advanced risk analytics, real-time data processing, and expert knowledge to evaluate alternative control strategies, such as real-time wearable sensor integration, vision-based dynamic safety zones, AI-driven behavior prediction models, haptic feedback, and self-learning adaptive robot algorithms. A cross-disciplinary panel of ten experts structures six main and eighteen sub-criteria spanning safety, adaptability, ergonomics, reliability, performance, and cost, with response time and implementation/maintenance costs modeled as cost types. Safety receives the most significant weight; the most influential sub-criteria are collision avoidance efficiency, return on investment (ROI), and emergency response capability. The framework preserves linguistic semantics from elicitation to aggregation and provides a transparent, uncertainty-aware tool for selecting and phasing adaptive control strategies in Industry 5.0 collaborative cells. Full article

Smart grid operators face escalating cyber threats and tight resource constraints, demanding the transparent, defensible prioritization of security controls. This paper asks how to select cybersecurity controls for smart grids while retaining picture fuzzy evidence throughout and supporting policy-sensitive “what-if” analyses. We propose a hybrid Picture Fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) and Combinative Distance-based Assessment (CODAS) framework that carries picture fuzzy evidence end-to-end over a domain-specific cost/benefit criteria system and a relative-assessment matrix, complemented by multi-scenario sensitivity analysis. Applied to ten prominent solutions across twenty-nine sub-criteria in four dimensions, the model highlights Performance as the most influential main criterion; at the sub-criterion level, the decisive factors are updating against new threats, threat-detection capability, and policy-customization flexibility; and Zero Trust Architecture emerges as the best overall alternative, with rankings stable under varied weighting scenarios. A managerial takeaway is that foundation controls (e.g., OT-integrated monitoring and ICS-aware detection) consistently remain near the top, while purely deceptive or access-centric options rank lower in this context. The framework contributes an end-to-end picture fuzzy risk-assessment model for smart grid cybersecurity and suggests future work on larger expert panels, cross-utility datasets, and dynamic, periodically refreshed assessments. Full article

The increasing carbon footprint of civil aviation has made the use of Sustainable Aviation Fuel (SAF) a strategic necessity in line with the sector’s sustainability goals. This study evaluates the existing SAF types based on environmental, economic, technical and social criteria, determines the criteria weights with Fuzzy-Step-Wise Weight Assessment Ratio Analysis (F-SWARA) and selects the most suitable alternative through Spherical Fuzzy-Multi Objective Optimization on the basis of Ratio Analysis plus full MULTIplicative form (SF-MULTIMOORA) method. The alternative evaluation process was carried out on a Python-based online platform and sensitivity analysis was performed on five different scenarios. According to the findings, the Hydroprocessed Esters and Fatty Acids (HEFA-SPK) alternative stands out as the most suitable option in all scenarios, followed by the Fischer-Tropsch Synthetic Paraffinic Kerosene (FT-SPK) alternative. In contrast, Alcohol-to-Jet (ATJ-SPK) and Power-to-Liquid (PtL) options seem to be more variable and less stable. The study provides methodological contributions for the evaluation of SAF alternatives with fuzzy multi-criteria decision making (MCDM) methods and provides strategic implications for manufacturers and airlines in achieving the low-carbon targets of the aviation sector. Full article