Search Results (57)

As product complexity and customization levels continue to rise in high-end manufacturing, optimizing and controlling multistage manufacturing processes (MMPs) presents growing challenges. However, existing MMP research has largely focused on optimizing relatively fixed process routes, while limited attention has been paid to the route selection problem itself, particularly the global selection of process routes under real-world conditions where MMPs stages are mutually coupled and characterized by uncertainty. Therefore, the present study focuses on the fundamental challenge of process route decision-making for complex products within MMPs. A hybrid decision model is developed that incorporates expert knowledge and explicitly quantifies uncertainty arising from decision inconsistency and linguistic ambiguity. The proposed model consists of three main components: expert weighting, criterion weighting, and comprehensive ranking of process schemes. Expert and criterion weights are derived using the Enhanced Analytic Hierarchy Process (EAHP) to address inconsistency in expert judgments, while the ranking of alternatives is performed using a novel Combined Compromise Solution (CoCoSo) rule within an Interval Type-2 Fuzzy Sets (IT2FS) linguistic environment. Furthermore, the effectiveness of the proposed framework is validated through a case study on the multistage manufacturing process of compact aerospace heat exchangers. The results demonstrate that the proposed approach provides effective decision support for selecting robust process schemes during the initial planning phase of MMPs. 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

Regions with insufficient resilience in their agriculture industry can usually be exposed to threats of unstable supply of food and agricultural products. Therefore, agricultural resilience is important for regional development and welfare. To support the development of agricultural resilience, proper policies and incentives need to be implemented. To achieve this, the first step is to appropriately evaluate the regional agricultural resilience levels. In this study, a novel agricultural resilience evaluation method was developed based on hybrid weighting approaches and dynamic CoCoSo (i.e., Combined Compromise Solution). The method can capture the temporal change in resilience levels, integrate richer information, and provide more robust output. To confirm its effectiveness, the method was applied to the evaluation of regional agricultural resilience in 21 cities of Sichuan province in China across five years. Over a recent five-year period, the annual average levels of agricultural resilience in Sichuan have increased, although this trend became less significant in more recent years. Also, the resilience levels among cities are diverse, and some cities have experienced significant changes of resilience across years. When considering temporal effects integrating five years, Liangshanzhou city ranks the first and Bazhong city ranks the last in terms of their resilience levels, but such results can depend on CoCoSo parameters and time weight parameters, with the latter having more significant influence. This study can contribute to the existing literature by providing new methodological tools for agricultural resilience research and regional management studies. Also, this study can help identify cities with different agricultural resilience levels and dynamics, informing practitioners’ new perspectives for agricultural policy evaluation as well as business strategy planning. Full article

Traditional multi-criteria decision-making (MCDM) methods face problems in sustainable product design evaluation, including weak semantic expression, single weight modeling, and insufficient ranking robustness. To address these issues, this paper proposes an evaluation framework based on Trapezoidal Intuitionistic Fuzzy (TrIF), named TrIF-DEC, which integrates Decision-Making Trial and Evaluation Laboratory (DEMATEL), Entropy, and Combined Compromise Solution (CoCoSo). Firstly, design criteria across four dimensions—social, economic, technological, and environmental—are identified based on sustainability considerations. Then, TrIF is used to capture the fuzziness and hesitation in expert judgments. The DEMATEL and Entropy methods are combined to extract causal relationships between criteria and quantify data variation, enabling the collaborative weighting of subjective and objective factors. Finally, multi-strategy integrated ranking is performed through TrIF-CoCoSo to enhance decision stability. An empirical case study on nursing bed design demonstrates the effectiveness of the proposed framework. Results demonstrate that TrIF-DEC can systematically integrate uncertainty information with multidimensional sustainability goals, providing reliable support for complex product design evaluation. Full article

With the impact of globalization, logistics has evolved beyond mere goods transportation to become an indispensable component of international trade and a strategic force that provides a competitive advantage. Through logistics companies with strong financial performance, the sector plays a decisive role in enhancing industry efficiency and supporting global economic sustainability. In this context, measuring and improving the financial performance of logistics companies has become critically important. This study introduces an innovative approach to evaluating the financial performance of logistics companies listed on the Borsa Istanbul (BIST) Transportation Index by integrating four distinct Multi-Criteria Decision-Making (MCDM) methods. The SIWEC, MEREC, and LODECI methods, recognized for objective weighting, were used to assign weights to financial criteria for logistics companies. The obtained criteria weights were combined for each research period using the Heron mean, and then the performance rankings of logistics companies were determined using the CoCoSo method. The consistency of the results obtained was also evaluated through sensitivity analysis, and the reliability of the model was tested. It has been determined that the methods used are moderately to highly sensitive to changes in parameters. Full article

In this study, aluminium matrix hybrid composites reinforced with boron carbide (B₄C) and silicon carbide (SiC) were fabricated using the direct hot-pressing technique under 35 MPa pressure at 600 °C for 5 min. Particle size distribution and scanning electron microscope analysis were conducted for the input powders. The microstructure, mechanical properties, and drillability of the fabricated composites were examined. As the SiC content increased, the density decreased, hardness improved, and transverse rupture strength declined. Drilling experiments were performed based on the Taguchi L₁₈ orthogonal array. The control factors included cutting speed (25 and 50 m/min), feed rate (0.08, 0.16, and 0.24 mm/rev), point angle (100°, 118°, and 136°), and SiC content (0%, 5%, and 10%). Quality characteristics such as thrust force, torque, surface quality indicators, diameter deviation, and circularity deviation were evaluated. The Taguchi method was applied for single-response optimization, while the Entropy-weighted, Taguchi-based CoCoSo method was used for multi-response optimization. Analysis of Variance was conducted to assess factor significance, and regression analysis was used to model relationships between inputs and responses, yielding high R² values. The optimal drilling performance was achieved at 50 m/min, 0.08 mm/rev, 136°, and 10% SiC, and the confirmation tests verified these results within the 95% confidence interval. Full article

The exponential growth of lithium-ion battery consumption has amplified the urgency of identifying sustainable and economically viable recycling solutions. This study proposes an integrated decision-making framework based on the T-Spherical Fuzzy Einstein Interaction Aggregator DEMATEL-CoCoSo approach to comprehensively evaluate and rank battery recycling technologies under uncertainty. Ten key evaluation criteria—encompassing environmental, economic, and technological dimensions—were identified through expert consultation and literature synthesis. The T-Spherical Fuzzy DEMATEL method was first applied to analyze the causal interdependencies among criteria and determine their relative weights, revealing that environmental drivers such as energy consumption, greenhouse gas emissions, and waste generation exert the most systemic influence. Subsequently, six recycling alternatives were assessed and ranked using the CoCoSo method enhanced by Einstein-based aggregation, which captured the complex interactions present in the experts’ evaluations and assessments. Results indicate that Direct Recycling is the most favorable option, followed by the Hydrometallurgical and Bioleaching methods, while Pyrometallurgical Recycling ranked lowest due to its high energy demands and environmental burden. The proposed hybrid model effectively handles linguistic uncertainty, expert variability, and interdependent evaluation structures, offering a robust decision-support tool for sustainable technology selection in the circular battery economy. The framework is adaptable to other domains requiring structured expert-based evaluations under fuzzy environments. Full article

The assessment of company performance requires a holistic approach, encompassing both financial and non-financial metrics. Accordingly, we develop a comprehensive airline performance evaluation model utilizing the Balanced Scorecard (BSC)-based multi-criteria decision-making (MCDM) framework. Based on contingency theory, we use 30 Key Performance Indicators (KPIs) derived from the literature and develop a novel performance model by combining the BSC framework with the Method based on the Removal Effects of Criteria (MEREC) for KPI weighting and the Combined Compromise Solution (CoCoSo) for ranking. The focus on Turkish Airlines, serving as a comparative benchmark, over the period 2020–2023 reveals that while financial KPIs hold the greatest weight, non-financial KPIs have the most significant impact on performance. The lowest performance is recorded in 2020, most probably attributable to the COVID-19 pandemic, followed by a remarkable recovery in 2021. We offer a methodological contribution for managers, decision-makers, and scholars—an objective, data-driven tool to assess airline performance. Furthermore, we furnish policymakers with tangible data for more effective industrial incentives and convenient regulatory strategies. In contrast to most of the literature emphasizing financial indicators and subjective weighting approaches that might yield biased rankings, we suggest a novel integrated performance evaluation model tailored for the airline industry. Full article

Macroeconomic indicators are essential measures that reflect the overall economic wellbeing of a region or country and have a significant impact on investment decisions. The data on macroeconomic indicators for Turkish development regions facilitate a comparison of macroeconomic performance between these regions. This kind of analysis can help enhance the development levels of the regions while ensuring resources are used efficiently. This study compares the macroeconomic performance of Turkish development regions between 2019 and 2022 using a hybrid multi-criteria analysis method. A total of 26 regions were evaluated based on seven criteria: GDP, GDP per capita, employment rate, number of enterprises, export, unemployment rate, and import. The criteria were weighted using the Step-Wise Weight Assessment Ratio Analysis (SWARA) method and ranked using the Combined Compromise Solution (CoCoSo) method. This study addresses a gap in the literature by analyzing the macroeconomic performance of Turkish regions, aiming to reduce economic disparities. The results showed that the Istanbul region had the best performance over the 4-year period, while Eastern Anatolia experienced a consistently declining performance, ranking last. Some regions had fluctuating performances, while others maintained steady outcomes. This study advances research by offering a more reliable and comprehensive analysis, thereby contributing to the improvement of future studies on regional economic development. Full article

Despite manufacturing firms recognizing the potential benefits of polymer-based smart materials (PBSM) in additive manufacturing (AM), their large-scale integration remains limited. As manufacturing firms strive toward net-zero emissions (NZE) and sustainable manufacturing, integrating PBSM into AM could be pivotal for manufacturing firms striving to achieve NZE and more sustainable production. In this regard, this study uses a mixed-method approach: a systematic literature review (SLR) to address the current trends and critical challenges associated with the “development, processing, and scalability” of PBSM adoption for AM. Further, the study analyzes 100 responses from Indian manufacturing firms, employing exploratory factor analysis (EFA) to develop a framework. This framework is further validated by determining the priority order of challenges using the Combined Compromise Solution (CoCoSo) through a case study. The outcome highlights that end-of-life management and lack of standardization are the most critical challenges for manufacturing firms, restricting the adoption of PBSM for AM. This research provides valuable insights for industry professionals and academia, guiding a strategic roadmap toward net-zero manufacturing. With this transformation, industries can align with global net-zero targets and contribute to India’s net-zero economy (NZE) goal by 2070. Full article

Assessing the urban green transportation development level (UGTDL) is of great significance for addressing traffic issues in megacities and promoting urban sustainable development. An evaluation framework for the UGTDL is proposed based on Multi-Criteria Decision Analysis (MCDA) methods. Firstly, from both macro and micro perspectives, a comprehensive evaluation indicator system is constructed, covering multiple dimensions such as traffic spatial organization efficiency, green travel, new energy vehicle development, traffic safety, and the traffic environment. Secondly, to address the uncertainties and fuzziness in the evaluation process, the Probability Language Term Set (PLTS) is introduced to represent expert evaluation information, thereby reducing the information loss. Thirdly, the improved Step-wise Weight Assessment Ratio Analysis (SWARA) method is employed to calculate the weights of the indicators, improving the computational efficiency. Finally, the extended Combined Compromise Solution (CoCoSo) method is used to calculate the UGTDL, avoiding the compensatory issues in the traditional decision-making methods. The proposed approach is applied to assess the UGTDL in Guangzhou from 2020 to 2023. The results show that the UGTDL scores for Guangzhou from 2020 to 2023 are 1.6367, 2.2325, 2.1141, and 1.8575, respectively. Sensitivity analysis verifies the effectiveness and stability of the approach. Further obstacle analysis shows that the promotion of new energy vehicles (NEVs) has led to a marginal decrease in the utility of Guangzhou’s UGTDL. In the future, Guangzhou should take further measures to improve the traffic space organization efficiency and traffic safety. Full article

Emergency medical services (EMSs) are critical to reducing fatalities and improving patient outcomes in emergencies such as traffic accidents, where response time is a decisive factor. This study proposes a comprehensive and systematic approach to designing and optimizing EMS systems tailored for urban traffic accidents. By integrating Geographic Information Systems (GISs), hypercube queuing models, Economic Value Added (EVA) calculations, and multi-criteria decision-making (MCDM) techniques, we developed a model that balances service efficiency, financial sustainability, and equitable access to emergency care. The hypercube queuing model was applied to estimate key performance metrics, such as response time, coverage, and the GINI index for equity, under varying numbers of ambulances and demand scenarios. In addition, EVA was calculated for different configurations of leased and owned ambulances, offering a financial perspective to assess the viability of public–private partnerships (PPPs) in EMSs. Using the fuzzy Analytic Hierarchy Process (AHP) and CoCoSo (Combined Compromise Solution) methods, this study identified the optimal number of ambulances required to minimize response time, maximize coverage, and ensure financial sustainability. The proposed approach has been applied to a real case in Colombia. Furthermore, integrating leased ambulances offers a financially viable solution with positive EVA values that guarantee the long-term sustainability of the public–private partnership. This paper advances the literature by providing a practical framework for optimizing EMS systems, particularly in developing countries where financial constraints and resource limitations represent significant challenges. The proposed methodology improves service efficiency and economic sustainability and ensures equity in access to life-saving care. Full article

This paper proposes a novel hybrid model that integrates failure mode and effects analysis (FMEA), Rank Order Centroid (ROC), and Combined Compromise Solution (CoCoSo) to improve risk assessment and prioritization of failure modes. A case study in the healthcare sector will be conducted to validate the effectiveness of the proposed model. ROC is used to assign weights to the FMEA criteria (severity, occurrence, and detectability). CoCoSo is then applied to create a robust ranking of failure modes by considering multiple criteria simultaneously. The results of the case study show that the hybrid FMEA-ROC-CoCoSo model improves the accuracy and objectivity of risk prioritization. It effectively identifies critical failure modes, outperforming traditional FMEA. The hybrid approach not only improves risk management decision making, leading to better mitigation strategies and higher system reliability, but also reduces the complexity typically found in FMEA hybrid models. This model provides a more comprehensive risk assessment tool suitable for application in different industries. Full article

In today’s competitive conditions, firms compete in every aspect. It is essential to meet the quality requirements in all processes and to meet customer needs quickly. It should be ensured that all processes in the enterprises, all the technology used, and all the workforce employed are included in the total quality of the enterprise; necessary controls and corrections are made; and the quality is sustainable. In this study, (1) one of the critical processes of an enterprise, the process of a material arriving at the warehouse after its procurement and the process of its storage in the warehouse, is discussed. (2) The basic processes in storing raw materials or finished products have been redesigned based on quality with the help of the Blockchain (BC) method from Industry 4.0 (I-4.0) technologies. (3) A model has been developed for the BC-based Quality 4.0 (Q-4.0). This model was applied to the warehouse management processes of an enterprise and compared with the enterprise’s existing system. (4) As a result of the comparison, it has been seen that the developed Q-4.0 model is more effective and more comprehensive. (5) Due to the originality of the developed model, such a study is not encountered in the literature. Full article

More and more investments are being made in energy conversion projects from renewable energy sources (RESs), and a large number of investors are entering this sector. The focus of this study is the decision-making by the investor BD Green Energy in the Brčko District of Bosnia and Herzegovina. In order to choose the RES system that would realize this investment in the most efficient way, expert decision-making based on the fuzzy–rough approach and the Bonferroni mean operator was used. Determining the importance of the criteria was conducted using the fuzzy–rough SiWeC (simple weight calculation) method. The results of this method showed that all used criteria have similar importance for the investor. RES system selection was conducted using the fuzzy–rough CoCoSo (combined compromise solution) method. The results of this method showed that investing in photovoltaic (PV) energy is the best for the investor. This research provided guidance on how investors should make investment decisions in RES systems with incomplete information and uncertainty in the decision-making process. Full article