This paper identifies the essential characteristics of a civilized corporate governance system within Iran's capital market, using the multi-criteria decision-making (MCDM) method, specifically polar matrix analysis. The goal of this research is to aid policymakers, corporate boards, and regulatory bodies in creating customized governance frameworks. The study emphasizes the significance of understanding and applying governance practices suited for emerging markets like Iran, contrasting these with established models in markets such as the US and Europe. Conducted between 2018-2022, the study uses a meta-composite analysis process to explore governance practices tailored to this emerging market, recognizing significant differences from established markets like the US or Europe. The analysis follows a three-step procedure. First, qualitative analysis identifies six key characteristics essential for an effective governance structure. In the second step, descriptive ranking evaluates the effectiveness of existing systems in Iran's capital market, revealing that among the six identified characteristics, adhocracy demonstrates the highest effectiveness percentage. Finally, quantitative analysis assesses the strengths and weaknesses of Iran's civilized corporate governance systems. This comprehensive approach provides a nuanced understanding of governance practices that align with the unique attributes of Iran's capital market, contributing to the development of localized, robust governance frameworks. • Identifies the key dimensions of civilized corporate governance. • Applies MCDM techniques to evaluate governance structures. • Integrates expert judgment with quantitative decision-making models. • Provides a structured framework for governance assessment.

Quantitative sustainability assessment and application of remediation technologies for heavy metal-contaminated sites based on multi-criteria decision-making method

A deck of cards-based co-constructive approach for modeling higher-order uncertainty in fuzzy decision-making

Application of multi-criteria decision-making (MCDM) methods to optimize sulfuric acid derivatives

Water temperature plays a pivotal role in shaping riverine ecosystems, exerting significant influence on a range of water quality parameters. However, accurately forecasting multi-temporal daily data remains challenging due to the non-stationary and nonlinear characteristics of hydrological time series. To address these challenges, the study proposes a novel deep learning framework that integrates Recursive Feature Elimination (RFE) with Multivariate Variational Mode Decomposition (MVMD), a multi-channel decomposition scheme that extracts meaningful sub-signals across multiple correlated variables. The decomposed features are processed using an Elman neural network integrated with a Bidirectional Gated Recurrent Unit (BIGRU) to capture both bidirectional and feedback-driven temporal dynamics. The model is applied to Fanno Creek and the McKenzie River in the western United States, demonstrating superior predictive performance under dynamically evolving hydrological conditions. RFE selected key input variables (discharge, pH, specific conductance, dissolved oxygen) from five years of data (2017-2021). The MVMD multi-channel scheme decomposes input lags into sub-sequences for each forecast horizon. The primary model (MVMD-ELMAN-BIGRU) was validated using elastic net (ELNET) regression and a Convolutional neural network coupled with BIGRU (CNN-BIGRU) as comparative machine learning (ML) models. Evaluation facilities include statistical indices, vulnerability assessments, and diagnostic visualizations. Additionally, for a reasonable evaluation of the models, a novel Multi-criteria decision-making (MCDM) method, namely the Multi-Objective Optimisation method based on Ratio Analysis (MOORA), was adopted to consolidate the metric performance across scenarios. The results indicated that MVMD-ELMAN-BIGRU, regarding the least value of MOORA (T+1:0.1096; T+3: 0.0096; T+7: 0.0478) for the Fanno Creek Rivers (T+1:0.00; T+3: 0.0296; T+7: 0.0932) for the McKenzie River, was superior to the MVMD-CNN-BIGRU and MVMD-ELNET models, respectively. This approach presents a promising solution for multi-temporal water temperature forecasting, which is crucial for effectively managing river ecosystems and water resources.

This study presents a comprehensive analysis of road accidents in India by using official government data sets from 53 major cities across 28 states. This research is conducted under a Type-2 picture fuzzy environment with advanced aggregation operators such as the arithmetic mean, geometric mean, hamy mean, Einstein weighted operator, and Aczel-Alsina operator. To enhance decision-making under uncertainty, a novel distance measure is proposed and theoretically validated. Five prominent distance-based multi-criteria decision-making (MCDM) methods such as TOPSIS, VIKOR, WASPAS, CODAS, and COPRAS were applied to rank the cities based on 35 critical road accident-related criteria. The results of the model revealed that Chennai consistently secured the top ranking in TOPSIS, COPRAS, and FIR. Amritsar also performed exceptionally, ranking 1st in VIKOR and achieving 3rd in TOPSIS. In contrast, cities such as Kanpur, Lucknow, and Ahmedabad consistently ranked in the bottom quartile, with Kanpur receiving the lowest position (53rd) in TOPSIS and FIR, indicating they are in critical risk zones. Mid-ranked cities such as Hyderabad, Pune, and Rajkot showed variations across methods but maintained stable FIR positions. To validate the robustness of the rankings, a comprehensive sensitivity analysis was conducted by various decision parameter q, confirming the stability of rankings under fluctuating conditions. The proposed framework improved the precision of accident severity and demonstrated adaptability to broader decision-making scenarios involving uncertainty. This study contributes a novel, integrated, and mathematically rigorous methodology for assessing urban safety for policymakers to reduce the road accident risks across Indian cities.

From resource to innovation: A decision framework for sustainable boron research infrastructure

Life cycle assessment of recycling food-contaminated polylactic acid packaging: net-energy, carbon emissions, and cost-based scenario assessment.

Integrating machine learning algorithms and game theory for optimized shipyard site selection in Istanbul

This research aims to optimize polylactic acid (PLA) materials with varying process parameter levels using the Taguchi method, compare tensile strengths, generate stress-strain curves, and achieve high-strength structures efficiently in a shorter production time while minimizing material use via fused deposition modeling (FDM). For this purpose, three factors were considered, and experiments were carried out using the Taguchi L9 orthogonal array. At the first stage, relationships between the measured tensile strength, the filament used, the production time, and the input factors were analyzed. The results revealed that tensile strength is predominantly influenced by wall line count (WLC), with a contribution of 56.2 %, followed by infill density (ID) at 33.5 % and print speed (PS) at 8.5 %. Conversely, ID emerges as the principal factor in material consumption, accounting for 95.7 % of the contribution margin. Similarly, printing time was largely determined by PS (71.5 %), followed by ID (27.7 %), indicating that significant reductions in production time can be achieved through PS optimization. At the second stage, a multi-criteria optimization and the VIKOR compromise solution, a multi-criteria decision-making (MCDM) method, were applied, with the trials as decision alternatives and the responses as criteria. As a result, the optimal combination was found to be WLC 6, ID 50 %, and PS 40 mm/s. Remarkably, the MCDM methodology has yet to be applied to Additive Manufacturing processes, with criteria importance determined through intercriteria correlation (CRITIC) approaches. This establishes a novel research pathway for multi-criteria optimization in 3D printing using PLA materials by addressing this gap through a unique optimization methodology.

Opioid analgesic drugs are widely used in modern medicine for pain management, anesthesia, and palliative care, making the study of their physicochemical properties essential for drug design and optimization. This study aims to investigate the potential of domination degree-based topological indices (DTIs) in quantitative structure–property relationship (QSPR) modeling and to apply a decision-making approach for ranking opioid analgesic drugs. QSPR models were developed using DTIs of chemical graphs, with logarithmic and multilinear regression analyses employed to establish correlations between DTIs and key physicochemical properties. In addition, the VIKOR multi-criteria decision-making method was integrated with QSPR analysis to systematically rank twenty opioid analgesic drugs. The results revealed strong correlations between DTIs and physicochemical properties, confirming their predictive ability, while the VIKOR-based ranking was found to be highly consistent across properties. These findings highlight the reliability of DTIs in predicting drug characteristics and demonstrate the practical utility of combining QSPR modeling with decision-making tools for drug characterization, discovery, and prioritization.

Only economic methods are not sufficient to assess the sustainability of management methods, especially in the management of natural resources such as water reservoirs or dams. The various dimensions of sustainability must be taken into account, while not obscuring any of them in the aggregation process. In such a context, if used correctly, multi-criteria decision-making (MCDM) methods are well suited in assessing the sustainability of small dam management methods in order to make better choices with regard to sustainable management policy. Therefore, we propose and apply a methodology based on a joint use of PROMETHEE I, II and KEMIRA methods to evaluate the sustainability of seven management methods of small dams, still called water reservoirs, in the city of Ouagadougou. The “Rationalization of uses” has proved to be the best sustainable management method. These results confirm the effectiveness of our methodology for assessing the sustainability of natural resource management methods.

In ethics-sensitive product development, Generative AI can improve the efficiency of concept generation, but it also raises challenges related to accountability, value alignment, and decision transparency. To address limitations in current human-AI co-design processes, including unclear allocation of decision-making authority, insufficiently structured translation from design requirements to design constraints, and limited explainability in scheme evaluation, this study proposes an explainable Human–Computer Interaction (HCI)-based decision support framework for human-AI co-design, termed GAGT. The framework integrates Generative AI with multi-criteria decision-making methods. Specifically, the Analytic Hierarchy Process (AHP) is used to structure design requirements and determine their priorities, Grey Relational Analysis (GRA) is used to compare candidate schemes, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to support transparent final ranking. Within the framework, human designers are mainly responsible for requirement confirmation, priority judgment, review at key checkpoints, and final scheme selection, while AI mainly supports information organization, candidate scheme generation, and quantitative comparison. The framework was applied to the design of a community medical vehicle through a small-sample, case-based, quasi-experimental study. Compared with the human-only condition, the GAGT-supported condition reduced design time by 56.1%. Compared with the AI-autonomous condition, it showed no observed HIPAA violations and a Value Drift Index of 16.1%, indicating better consistency with human-defined priorities. The results suggest that the proposed framework may improve design efficiency while supporting clearer human oversight and decision explainability in Generative AI-assisted design, and may provide a structured approach to organizing human and AI roles in ethics-sensitive design tasks.

Equitable prioritization of public investments is increasingly critical as municipalities face constrained budgets, heterogeneous neighborhood needs, and demands for transparent decisions. This paper proposes a fairness-aware group multi-criteria decision-making (MCDM) framework for ranking municipal infrastructure investments when budgets are constrained, and neighborhood needs differ. Six alternatives are assessed in the Istanbul case study: flood risk mitigation, inclusive public realm and cooling, smart and energy-efficient municipal assets, walking and cycling infrastructure, healthcare access improvements, and seismic retrofitting of public buildings. The criteria system combines efficiency, implementability, socio-environmental performance, and equity-oriented priorities through five main dimensions and 23 sub-criteria. In addition to cost, feasibility, and service effectiveness, the framework incorporates fairness-related criteria such as baseline need and deficit severity, vulnerability-targeting effectiveness, minimum service guarantee for the worst-off, and priority for low-accessibility centers. Public acceptance and environmental performance are also included. Stakeholder panels provide expert judgments using intuitionistic fuzzy sets, capturing membership, non-membership, and hesitation to reflect uncertainty. Criteria weights are derived with Intuitionistic Fuzzy Step-wise Weight Assessment Ratio Analysis (IF-SWARA), enabling importance elicitation and group aggregation without forcing crisp consensus. Alternatives are then ranked using Intuitionistic Fuzzy Combined Compromise Solution (IF-CoCoSo), which blends additive and multiplicative compromise solutions to balance overall performance with equity objectives. Robustness is assessed through sensitivity analysis by varying the γ parameter within the IF-CoCoSo procedure. A municipal case study demonstrates that healthcare access improvements achieve the highest compromise performance, followed by flood risk mitigation and seismic retrofitting of public buildings, while smart and energy-efficient municipal assets rank last. The findings confirm that explicitly embedding fairness criteria can shift municipal priorities toward alternatives that more directly reduce deprivation, risk, and spatial inequality. The main contribution of this study is not merely empirical application, but the development of a fairness-aware group MCDM framework that operationalizes distributive justice in municipal investment prioritization through a structured set of criteria.

Cause and Effect Analysis of Ship Accidents Using Multi-Criteria Decision-Making Methods

This study aims to examine the strengths and weaknesses of AHP, SWARA, BWM, PIPRECIA and FUCOM, which are subjective criteria weighting methods, to reveal their advantages, and to develop an improved subjective criterion weighting procedure in this context. As a new method proposal, it is aimed to show the reliability of the method by giving application examples and comparison analyzes within the framework of ICWM (Improved Criteria Weighting Method). According to the analysis results, it was observed that the correlations between the criterion weights found with ICWM and the criterion weights found with AHP, SWARA, BWM, and FUCOM, which are considered reliable in the literature, were very strong. It has also been determined that ICWM provides advantages in terms of number of pairwise comparisons, consistency, and practicality.

Abstract Typhoons lead to meteorological disasters along the coastal region of China. Typhoons induce secondary hazards in cities such as flooding, landslides, vibration of high-rise buildings etc. This paper introduces an improved multi-criteria decision-making (MCDM) method, called the ECC model, for assessing the distribution of vulnerability to typhoon-induced flooding. The novelty of this method is its integration of weights calculated initially using the traditional entropy weight method (EWM), coefficient of variation method (CVM), and criteria importance through intercriteria correlation (CRITIC) method through a matrix-based weight combination approach; the vulnerability level is reflected through comprehensive scores. By applying historical data related to typhoon-induced flood disasters to this method, the vulnerability distribution in the Hainan Island region was estimated. The collected statistical data demonstrated that the proposed improved assessment method achieved results that corresponded highly with actual disaster situations, indicating the reliability of this method. Moreover, the results showed that the proposed ECC model predicted the disaster site due to the Yagi event accurately and reflected the field situation. This study serves as a valuable reference for disaster prevention/mitigation measurement.

Increasing apprehension regarding climate change, resource scarcity, and ecological deterioration has propelled the global momentum toward sustainable energy development. In this context, green energy has become an essential part of the shift away from fossil fuels and toward a future with lower carbon emissions. However, choosing the best green energy source for a certain application or specific area is a difficult task with many facets requiring consideration. Green energy solutions necessitate careful evaluation of a wide range of qualitative criteria in contrast to traditional energy sources. We put forward a hybrid fuzzy Multi-Criteria Decision-Making (MCDM) method using interval valued Pythagorean fuzzy numbers in this paper. The possibility degree method is used in the suggested approach to derive the weights of the evaluation criteria. Next, the matrix of decisions is created, and the preferred alternative is selected by entropy theory and cosine similarity theorem. Ultimately, our goal is to develop innovative, reliable techniques using these various theorems. Combining the advantages of each approach improves decision-making proceses as they increase precision and resilience and streamline the ability to handle complicated data in a variety of situations. We utilized the proposed method to evaluate green energy alternatives in Sweden to demonstrate applicability. A sensitivity analysis of the results is conducted to test how changes in input parameters affect the final ranking. Finally, a comparison analysis is provided.

Toddler nutritional status is an important indicator of child health and development and requires accurate assessment. In Posyandu, nutritional evaluation is often performed manually, which may lead to inefficiencies and inconsistencies when processing large amounts of data. Decision Support Systems (DSS) can assist health workers in conducting more systematic and objective assessments. Previous studies have applied multicriteria decision-making methods such as Simple Additive Weighting (SAW) and Weighted Product (WP) in various decision-making contexts. However, most studies mainly focus on producing ranking results and rarely examine how sensitive these methods are when criteria weights change. In addition, only limited research evaluates these methods using real anthropometric data collected from community health services such as Posyandu. Therefore, this study aims to analyze and compare the sensitivity of the SAW and WP methods in determining toddler nutritional status using empirical anthropometric data. The dataset consists of 412 toddlers collected from Posyandu activities, including gender, age, weight, height, and body mass index, which were converted into nutritional indicators. Sensitivity was assessed by modifying each criterion weight under two scenarios (0.5 and 1) and measuring the percentage change in the resulting preference values. The results show that the SAW method produced a change of 4%, whereas the WP method showed a change of 0.0028%. These findings indicate that SAW is more responsive to weight variations, while WP produces more stable preference values. The results provide empirical insight into the behavior of different multicriteria decision-making methods when applied to real nutritional monitoring data.

ABSTRACT Post-disaster temporary shelter construction involves both humanitarian and technical dimensions. Evaluating different systems requires considering multiple criteria such as rapid deployment, space efficiency, and cost. This study aims to determine the most suitable construction system for temporary shelters using multi-criteria decision-making (MCDM) methods. A literature review based on the PRISMA protocol was conducted to identify evaluation criteria, and the Analytic Hierarchy Process (AHP) was applied to determine their relative weights based on expert opinions. The consistency ratio (CR) was calculated as 0.0303, indicating reliable judgments. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank four construction systems according to five criteria. In addition, a sensitivity analysis was conducted to examine the robustness of the results under different criterion weight scenarios. The results revealed that modular systems were the most suitable alternative, followed by membrane, compact and traditional systems. Modular systems stand out for rapid installation and durability; membrane systems perform well in early disaster stages but offer limited comfort. Compact systems provide portability at higher costs, while traditional systems promote local materials but lack speed. Overall, modular systems represent the most appropriate solution, while other systems may be used complementarily depending on context.