Search Results (3,169)

Effective project selection is a critical determinant of success for Design for Six Sigma (DFSS), particularly in automotive environments defined by high technical complexity and constrained resources. Because these selection tasks involve competing priorities, they are fundamentally multi-criteria decision-making (MCDA) problems that directly impact a company’s economic performance. This paper proposes a hybrid decision-support framework that integrates the Analytic Hierarchy Process (AHP) with a normalized scoring model. In this approach, classical AHP pairwise comparisons are used to derive consistent criteria weights, while project alternatives are evaluated on a 1–10 normalized scale to ensure the model remains scalable and practical for an industrial setting. The framework was empirically validated through a case study in an automotive company evaluating twelve DFSS project concepts. The results reveal that experts prioritize Product Quality (33%) and Cost/Functionality (33%) above all other factors, with these two criteria accounting for 66% of the total decision weight. Furthermore, the study established classification rules where projects scoring above 7.2 showed high implementation potential, while those below 5.2 were frequently discontinued. This structured approach enables a transparent and justifiable prioritization process that supports economic and operational sustainability by significantly reducing wasted engineering hours and prototype costs. Full article

Addressing building vulnerability to climate hazards requires advanced tools to support adaptation decisions. To this end, the current study presents an Agentic Artificial Intelligence (Agentic AI) Optimization framework to enhance the climate resilience of existing buildings, bridging policy guidelines and a practical tool for optimized and context-aware retrofit strategies. Aligned with EU Guidance, the framework operationalizes a Climate Vulnerability Assessment (CVA) within a Multi-Objective Optimization (MOO) engine through a multi-agent architecture. Specialized subagents, including Requirements, Cost, Strategy, and XAI Agents, collaborate to understand user goals, manage budget constraints, optimize strategies, and produce explainable reports. Two metaheuristic optimizers, such as Multi-Objective Invasive Weed (MO-IWO) and Grey Wolf (MO-GWO), were coupled with Multi-Criteria Decision Making (MCDM) models to minimize building vulnerability and adaptation costs against multiple climate hazards (e.g., heat waves and heavy precipitation). Results show that, despite MO-GWO’s lower computational burden, MO-IWO performed more robustly and is selected as the superior optimizer for integration into the Agentic AI system. Ultimately, the framework provides a scalable approach to asset management, significantly improving decision-making for building retrofits. Full article

Association rule mining is a fundamental data mining technique for uncovering latent relationships among variables in large-scale datasets. However, conventional approaches rely on single-metric filtering strategies, which are insufficient for capturing the inherent multi-criteria nature of rule quality. To address this limitation, this study formulates ARM as a many-objective optimization problem and proposes a hybrid algorithm, NSGA-II/DE-ARM, that simultaneously optimizes four rule-quality measures: support, confidence, lift, and NetConf. The proposed algorithm enhances the NSGA-II framework by integrating binary differential evolution operators, an adaptive operator selection mechanism, lift-weighted tournament selection, and a constraint-domination principle combined with a dynamic minimum support threshold. Its performance was evaluated using two datasets: a SIPRI–World Bank panel dataset consisting of defense industry and macroeconomic indicators covering 46 items over the 2002–2023 period, and the UCI Mushroom benchmark dataset consisting of 118 items. Across 30 independent runs on the SIPRI–World Bank dataset, NSGA-II/DE-ARM outperformed the Apriori baseline in all four metrics (mean lift = 4.748, confidence = 0.853, support = 0.146, NetConf = 0.789), with large effect sizes (Cohen’s d = 1.77–5.77, p < 0.001 in each case). On the Mushroom benchmark dataset, the proposed method also achieved substantial improvements, with Cohen’s d values ranging from 0.93 to 6.16. NSGA-II/DE-ARM generated 68 Pareto-optimal rules in a representative run and achieved the highest hypervolume values on both datasets, with HV = 3.231 for SIPRI–World Bank and HV = 6.262 for Mushroom. These results suggest that NSGA-II/DE-ARM offers decision-makers a broader and more balanced multi-criteria solution set than single-metric filtering approaches. Full article

The 6 February 2023 Kahramanmaraş earthquakes highlighted the importance of emergency assembly areas for disaster response, evacuation safety, and urban resilience in earthquake-prone cities. Although GIS-based multi-criteria decision-making approaches are widely used to assess spatial suitability, relatively few studies integrate suitability, capacity adequacy, and accessibility within a single framework, particularly in cities directly affected by the 2023 earthquakes. This study evaluates emergency assembly areas in Malatya, Türkiye, using an integrated GIS–Best–Worst Method (BWM) framework. Nine criteria—geology, population density, building density, elevation, slope, distance to roads, distance to rivers, distance to fault lines, and distance to buildings—were weighted based on the judgements of 15 experts involved in Provincial Disaster Risk Reduction Plan (İRAP) processes. The BWM results show that geology and distance to fault lines received the highest weights, whereas distance to roads had the lowest weight. The spatial analysis indicates that highly suitable areas are concentrated mainly in the city centre, while several peripheral neighbourhoods are constrained by geological, topographical, and accessibility-related factors. Existing official emergency assembly areas cover only 27.9% of the population and are located in 13 of 88 neighbourhoods. Estimated access times range from 0 to 5 min in central areas to 10–15 min, or beyond effective service coverage, in peripheral neighbourhoods. Although integrating parks and green spaces substantially increases potential capacity, it does not fully eliminate neighbourhood-level inequalities. The findings provide a spatial decision-support framework for emergency planning in earthquake-prone cities. Full article

Conventional fuzzy multi-criteria decision-making (MCDM) methods support ranking under uncertainty but often provide limited explanation of why alternatives are preferred. This study proposes an explainable fuzzy decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy TOPSIS with surrogate modeling, SHAP-based analysis, and linguistic rule extraction. The main contribution is an explanation layer that preserves the original FAHP–FTOPSIS ranking structure while decomposing ranking scores into criterion-level contributions and transforming recurrent attribution patterns into IF–THEN rules. The framework is evaluated through a supplier-selection case study using expert fuzzy evaluations, local perturbation analysis, leave-one-supplier-out cross-validation, and a synthetic benchmark. The results show that the fuzzy MCDM layer produces discriminative rankings and that the top-ranked supplier remains comparatively stable under perturbations. Among the tested surrogates, the Random Forest Regressor achieved the strongest local fidelity, outperforming linear regression and a shallow decision tree. SHAP analysis showed ordinal alignment between FAHP weights and global criterion importance, while the extracted rules achieved high coverage, consistency, and threshold stability. The framework is useful for researchers, decision analysts, procurement managers, and supply chain professionals who require transparent, interpretable, and auditable multicriteria decisions under uncertainty. Full article

Improving pedestrian safety at urban intersections is a key challenge for achieving safer and more sustainable urban transport systems. This study develops a multi-criteria decision-making model (MCDM) for selecting the most appropriate traffic signal type at pedestrian crossings in different urban zones. Traffic conditions, illegal pedestrian crossings and the number of traffic accidents were taken into account during the modelling, as well as the characteristics of the urban environment. The research involved 66,616 pedestrians at 22 pedestrian crossings located in three urban zones: school zones, central zones, and non-central zones. The data were aggregated using Bayesian (beta-binomial) and classical statistical methods. The OPA-Group method was then used to develop the model. In the decision-making phase, the Ordinal Priority Approach (OPA) was applied as the core MCDM method. It was then extended to the OPA-Group framework to incorporate group-based evaluation in accordance with the model requirements. Additionally, a comprehensive sensitivity analysis was conducted, confirming the robustness and stability of the proposed model. The results show that traditional traffic signals are most suitable for school and non-central zones, whereas countdown traffic signals are recommended for central zones. Push-button traffic signals were identified as the least efficient solution for regulating pedestrian movement at pedestrian crossings. Full article

Financial stability and banking sector performance have become critical concerns for policymakers and regulators in the aftermath of global financial crises. This study aims to evaluate the financial soundness of banking sectors across OECD countries by employing an integrated multi-criteria evaluation framework based on Financial Soundness Indicators (FSIs) for the year 2024. The analysis focuses on key dimensions such as profitability, asset quality, capital adequacy, and liquidity conditions. To enhance methodological robustness, objective criterion weights are derived using the Modified Standard Deviation (MSD) and Modified Preference Selection Index (MPSI) methods and then combined within a unified weighting scheme. Country rankings are obtained through the MABAC method, and the stability of the results is further examined using sensitivity analysis. This integrated approach provides a more balanced evaluation by reducing the potential bias associated with relying on a single weighting method. The findings indicate that the ratio of non-performing loans to total gross loans plays a dominant role in differentiating banking sector soundness among OECD economies, highlighting the importance of credit risk and balance-sheet resilience in comparative macroprudential evaluations. In addition, the results reveal relatively distinct performance patterns between countries characterized by stronger capital structures and lower credit risk exposure and those exhibiting comparatively weaker resilience indicators. Overall, the study contributes to the literature by providing a structured and robust framework for comparative banking sector assessment and offers policy-relevant insights for comparative macroprudential monitoring and the assessment of banking sector resilience across OECD countries. Full article

Vermicomposting is an environmentally sustainable, economically viable, and agronomically valuable method for converting organic waste into nutrient-rich soil amendments, thereby supporting sustainable development. However, the fertilization efficiency of vermicompost can vary significantly depending on the physicochemical properties of the feedstock used. This study aims to compare different feedstocks on vermicompost and evaluate their performance on soil fertility and plant nutritional status. Organic matter (OM), pH, salinity (EC), total Kjeldahl nitrogen (TKN), total phosphorus (TP) and total potassium (TK) of various vermicompost samples were taken into consideration to evaluate their fertilization efficiency as performance determinants in terms of plant growth, plant nutritional status, yield, crop quality and cost with the aim of determining the weights of the specific parameters in the total performance using multi-criteria decision-making (MCDM) methods. The integrated ENTROPY-TOPSIS method was used. Twenty-one different vermicompost feedstock analyses were collected from the literature and compared in order to create an agronomic performance ranking based on the selected criteria. The ENTROPY method revealed that the TP was the most influential factor (21.6%), followed by the EC (20.7%) and the TK (18.5%), while the OM had the lowest impact (11.3%). Based on the TOPSIS ranking, vermicompost from brewer’s spent grain achieved the highest performance, followed by cow manure plus rice straw and olive pruning waste, whereas paper waste ranked at the bottom. A comparative analysis with other objective MCDM weighting methods proved strong correlations, particularly with WENSLO, MPSI and LODECI methods, confirming the robustness of the ENTROPY method. Full article

Autonomous mobile robots (AMRs) are increasingly used in warehouse intralogistics to improve material flow, flexibility, productivity, and operational continuity. However, selecting an appropriate AMR is no longer limited to mechanical performance or acquisition cost, since modern warehouse robots operate as networked cyber-physical systems that must interact with enterprise software, fleet management platforms, communication infrastructures, and cybersecurity mechanisms. This study proposes an integrated Pythagorean fuzzy multi-criteria decision-making (MCDM) framework for evaluating AMR alternatives in warehouse operations by jointly considering economic, technical, physical, software-related, integration-oriented, and security-related criteria. Expert judgments obtained from eight specialists, including four academics and four private-sector professionals, were modeled using Pythagorean fuzzy numbers to capture uncertainty and hesitation in linguistic assessments. The Pythagorean Fuzzy Indifference Threshold-Based Attribute Ratio Analysis (PF-ITARA) method was employed to determine criterion weights based on threshold-sensitive discrimination among alternatives, while Pythagorean Fuzzy VIšekriterijumsko KOmpromisno Rangiranje (PF-VIKOR) was used to rank four AMR alternatives according to a compromise solution logic. The results show that investment cost, maneuverability, total cost of ownership, integration and validation requirements, and ease of programming and commissioning are the most influential criteria. Cybersecurity-related criteria, particularly data confidentiality, system integrity, monitoring and incident response readiness, authentication control, and role-based access control, also received notable importance levels. Among the evaluated alternatives, MiR250 achieved the best overall performance and emerged as the most suitable compromise solution, followed by OMRON LD-250, HIKROBOT Forklift AGV, and KUKA KMP 600-S diffDrive. The proposed framework provides a transparent and practically applicable decision-support tool for AMR procurement by integrating operational performance, digital interoperability, and cybersecurity readiness into a unified evaluation structure. Full article

As Industry 4.0 transforms manufacturing operations, maintenance organizations face a group decision-making problem: how to consolidate diverse expert judgments into a defensible, transparent ranking of the competencies that maintenance personnel most need. This paper applies an integrated Delphi–AHP framework—with explicit notation, operators, and diagnostics—to prioritize maintenance competencies in advanced-manufacturing settings. The Delphi stage consolidates expert-generated items under median–interquartile-range consensus and round-to-round stability rules, while the Analytic Hierarchy Process (AHP) transforms validated pairwise comparisons into ratio-scale priority weights through geometric-mean Aggregation of Individual Judgments (AIJ) and eigenvector derivation. Consistency screening (CI/CR), inter-rater agreement (Kendall’s W), and perturbation-based sensitivity analysis accompany the resulting weight vector. A bounded AI-assisted consistency-check step supports terminology harmonization during Delphi statement consolidation, subject to explicit human-validation constraints. A panel of fifteen industry experts participated in the study; five competency dimensions and twenty-nine indicators were retained through three Delphi rounds. AHP weighting identified Basic Knowledge and Skills as the highest-priority dimension, followed by Safety and Regulation Awareness and Problem-Solving Ability. Aggregated pairwise comparison matrices, local and global weights, and sensitivity results are reported to support reproducibility. The study contributes a rigorously specified application of combined Delphi–AHP to a domain—Industry 4.0 maintenance asset management—where multi-criteria decision analysis has seen limited formal application, and closes common specification gaps in published Delphi–AHP implementations. Full article

This manuscript creates a framework for decision support based on green urbanism to direct the sustainable development of Gamasa, an Egyptian seaside city. The paper aims to convert the concepts of green urbanism into a multi-criteria evaluation that can support strategic urban development and prioritize spatial interventions. Sustainable mobility, green and blue infrastructure, energy and resource efficiency, urban form and density, social livability and public space quality, and governance and implementation feasibility are the six dimensions that are defined. These dimensions are derived from international sustainability literature and tailored to Gamasa’s particular challenges. The study’s methodology combines a multi-criteria decision-making approach based on the AHP with spatial analysis of land use, street hierarchy, building shape, and green space distribution. Weights for these dimensions are determined by expert-based pairwise comparisons, which are backed by a SWOT analysis. To prioritize priority zones for green transformation, the weighted framework is applied to four important urban areas: residential districts, a large urban park, the waterfront, and the main urban corridor. The top priorities, according to the results, are climate-responsive coastal design, increased green and blue infrastructure, and sustainable transportation. For quickly urbanizing coastal cities, the method demonstrates how the AHP operationalizes green urbanism into quantifiable, context-sensitive goals. Full article

Despite advances in using multi-criteria decision-making (MCDM) methods and their fuzzy set extensions for human evaluations of large language models (LLMs), several gaps remain in the literature, particularly in task-specific evaluations that offer a more tractable and interpretable approach. Thus, this work develops a generalized intuitionistic fuzzy MCDM approach that bridges methodological gaps by outlining two contributions. First, the integration of SWARA (Stepwise Weight Assessment Ratio Analysis) and WINGS (Weighted Influence Non-linear Gauge System) is demonstrated to compute the priority weights of the evaluation criteria, thereby augmenting the independence limitation in prior relevant studies. Second, we introduce a newly improved IF-EDAS (intuitionistic fuzzy Evaluation based on Distance from Average Solution) that preserves more uncertain information and provides a more natural extension of the canonical EDAS framework, starting with the adoption of the IFWAM (intuitionistic fuzzy weighted arithmetic mean) operator for a more intuitive approach in generating the intuitionistic fuzzy average solution vector. Also, the proposed IF-EDAS variant employs three decision rules and the Hamming distance metric in its novel computational approach. The proposed hybrid approach was deployed in two case studies evaluating five popular LLMs for text summarization across seven interdependent criteria. Results show that SWARA initially prioritizes accuracy, coherence, and consistency, but these were revised when accounting for criteria interdependence, with coherence and language quality emerging as the most preferred criteria. Both case studies suggest that Gemini may perform favorably, while Copilot may consistently rank last. The findings of the case studies share similar insights with those of three other similar IF-EDAS variants, although our claims may have limited external validity, which requires more case studies and experts in future task-specific human evaluations. The proposed approach, along with its deployment in two case studies, demonstrates human evaluations of LLMs with greater computational interpretability, which contribute to the general MCDM literature. Full article

This paper provides an overview of recent developments in quickest change detection (QCD) for high-dimensional multi-sensor systems, with an emphasis on settings involving structural constraints and limited sensing resources. Classical QCD methodologies, while well understood in low-dimensional and fully observed regimes, face significant challenges when extended to modern applications characterized by large-scale data, constrained sampling or communication, and heterogeneous signal structures. We review key approaches for handling high dimensionality, including methods that exploit sparsity, and other forms of signal heterogeneity. Additionally, we discuss sampling constraints, where observations must be selected or acquired sequentially under resource limitations. Multi-stream applications can require making multiple detections, for example when detecting changes separately in different streams. The underlying assumptions on probability models, the types of changes taking place, commonly used decision-making criteria, performance indices, and error types are described. We also briefly discuss the application of machine learning in cases where the underlying probability models are not known, or there is a need to select which sensors should monitor the phenomena because of the large scale of the system. Full article

Conventional multi-criteria decision-making approaches for battery energy storage system (BESS) dispatch evaluation treat regulatory and policy conditions as compensable criteria within a single aggregate score. This becomes problematic when institutional admissibility functions as a prerequisite for deployment rather than a tradeable attribute. This study aims to develop and test a sequential gate framework. The methodological contribution lies in the evaluation architecture itself: the framework distinguishes sequential admissibility gating from conventional compensatory Multi-Criteria Decision-Making (MCDM). Deployment feasibility is conceptualized as a layered construct in which regulatory admissibility defines the feasible solution space and technical performance differentiates among admissible options. The framework integrates systematic literature screening, quantitative policy and regulatory assessment, and technical ranking using a hybrid Best-Worst Method, Entropy weighting, and TOPSIS approach. A Danish case study covering twelve dispatch strategies compares the proposed sequential design with two flat alternatives. The results show that the evaluation architecture materially affects outcomes: sequential gating excludes an institutionally incomplete strategy and reorders the upper tier by removing compensatory policy effects. Coordinated multi-BESS control at Electric Vehicle charging parks achieves the highest combined feasibility (closeness coefficient 0.891, ranked 1st), while mobile BESS is excluded by the admissibility gate. The sequential design reorders the upper tier relative to flat MCDM, with S4 and S6 rising and S2 and S10 falling once policy compensation is neutralized after the gate. The top-ranked strategy remains robust across sensitivity analysis, Monte Carlo simulation, score perturbation, and VIKOR cross-validation. The framework is presented as an analytical pre-simulation screening tool rather than a validated implementation instrument; external validation against real deployment outcomes is identified as a priority for future research. The framework provides a structured, decision-consistent approach for evaluating deployment feasibility in regulated energy systems. Full article

Sustainability assessment of road pavements requires the combined consideration of environmental and mechanical performance, since conventional mass-based Life Cycle Assessment (LCA) may lead to misleading conclusions. This study proposes a multi-criteria decision-support framework that integrates LCA results with key mechanical indicators through structural normalisation, enabling the comparison of asphalt mixtures on an equivalent structural basis. Three sustainable asphalt mixtures were analysed, namely Hot Recycled Mix Asphalt (HRMA), Half-Warm Mix Asphalt (HWMA), and Cold Recycled Mixture (CRM), and compared with a reference Hot Mix Asphalt (HMA). Environmental impacts were quantified using a cradle-to-gate LCA, while mechanical performance was characterised through stiffness, fatigue resistance, rutting, and moisture susceptibility. These indicators were integrated into a Structural Contribution index and a Material Environmental Impact Ratio. The results show that, although CRM benefits from cold production and high recycling rates, its lower structural performance reduces its advantage when equivalent thickness is considered. HWMA emerges as the most favourable compromise within the adopted framework, combining lower environmental impacts with competitive structural performance, while HRMA offers the greatest structural contribution with competitive environmental performance. Sensitivity analysis confirms the robustness of the framework under realistic variations in weighting assumptions. The study demonstrates that incorporating structural performance into environmental assessment is essential to avoid misleading conclusions and to support more reliable decision-making in sustainable pavement design. Full article