Search Results (26)

The growing urgency of resource-efficient construction in water-stressed and rapidly urbanizing regions necessitates integrated decision support frameworks that move beyond isolated sustainability metrics. This study operationalizes the water-energy nexus within building design evaluation by developing a structured hybrid multi-criteria decision-making (MCDM) framework tailored to the Indian construction context. Unlike conventional sustainability assessments that treat water and energy independently, the proposed approach integrates life cycle-based water consumption, operational and embodied energy demand, environmental impacts, economic feasibility, and project constraints within a unified analytical hierarchy. A Delphi-validated criterion structure comprising five main criteria and twenty sub-criteria is weighted using the Analytic Hierarchy Process (AHP), and ranked using the VIKOR compromise solution method. To strengthen methodological robustness, ranking outcomes are validated across three independent MCDM logics including TOPSIS, PROMETHEE, and COPRAS. The framework evaluates four representative building strategies aligned with Indian regulatory and certification systems (NBC, ECBC, IGBC/GRIHA, and net-zero water-energy design). Using expert-informed weights derived from a Delphi–AHP involving a panel of experienced practitioners, the VIKOR compromise ranking consistently identifies the net-zero alternative as the most favorable option within the evaluated framework. The results are therefore interpreted as an expert-informed assessment demonstrating the applicability of the proposed decision support methodology rather than as statistically generalizable priorities for the entire Indian construction sector. The study contributes by (i) embedding nexus-based resource interdependence into building-level MCDM modeling, (ii) enhancing transparency through explicit benefit-cost classification and decision matrix disclosure, and (iii) demonstrating ranking stability across multiple validation techniques. The proposed framework provides a transferable methodological approach that can be adapted to different regional contexts through locally derived expert inputs. Full article

This study presents the Multi-Method Convergence Protocol (MMCP), a decision-making framework designed to overcome the mono-objective limitations of HOMER Pro (Hybrid Optimization Model for Electric Renewables) and the instability commonly observed among traditional MCDM approaches. Applied to a hybrid PV–wind–grid Smart Grid (Intelligent Electrical Power Grid) in the Provence-Alpes-Côte d’Azur region (France), the protocol transforms techno-economic simulation outputs into robust and explainable multi-criteria decisions. MMCP integrates five sequential stages—normalization, AHP-based (Analytic Hierarchy Process) weighting, multi-method ranking (TOPSIS, PROMETHEE II, ELECTRE II (Elimination and Choice Expressing Reality II), and VIKOR), Borda–Copeland (Borda Count Ranking Method–Copeland Pairwise Aggregation Method) co-aggregation, and statistical validation—using Kendall’s τ_b (Kendall’s Rank Correlation Coefficient) and Spearman’s ρ (Spearman’s Rank Correlation Coefficient). Results reveal strong convergence between compensatory and non-compensatory models (τ_b ≥ 0.75; ρ ≥ 0.90), confirming the internal coherence and structural stability of the rankings. Scenario 17 emerges as the optimal configuration, combining low LCOE (Levelized Cost of Energy) with reduced emissions and balanced renewable penetration. The near-linear alignment between aggregation methods validates the protocol’s reliability and methodological transparency. Overall, MMCP provides a scalable and traceable foundation for sustainable Smart Grid planning and evidence-based energy governance. Full article

The purpose of this study is to determine the optimal location for siting an onshore wind farm on the island of Skyros, thereby maximizing performance and minimizing the project’s environmental impacts. Seven evaluation criteria are defined across various sectors, including environmental and economic sectors, and six criteria weighting methods are applied in combination with four multicriteria decision-making (MCDM) ranking methods for suitable areas, resulting in twenty-four ranking models. The alternatives considered in the analysis were defined through the application of constraints imposed by the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD RES), complemented by exclusion criteria documented in the international literature, as well as a minimum area requirement ensuring the feasibility of installing at least four wind turbines within the study area. The correlations between their results are then assessed using the Spearman coefficient. Geographic information systems (GISs) are utilized as a mapping tool. Through the application of the methodology, it emerges that area A9, located in the central to northern part of Skyros, is consistently assessed as the most suitable site for the installation of a wind farm based on nine models combining criteria weighting and MCDM methods, which should be prioritized as an option for early-stage wind farm siting planning. The results demonstrate an absolute correlation among the subjective weighting methods, whereas the objective methods do not appear to be significantly correlated with each other or with the subjective methods. The ranking methods with the highest correlation are PROMETHEE II and ELECTRE III, while those with the lowest are TOPSIS and VIKOR. Additionally, the hierarchy shows consistency across results using weights from AHP, BWM, ROC, and SIMOS. After applying multiple methods to investigate correlations and mitigate their disadvantages, it is concluded that when experts in the field are involved, it is preferable to incorporate subjective multicriteria analysis methods into decision-making problems. Finally, it is recommended to use more than one MCDM method in order to reach sound decisions. Full article

Scaling Agile in industrial software projects is a process control problem that must balance governance, scalability, and adaptability while keeping decisions auditable. We present a hybrid fuzzy multi-criteria decision-making (MCDM) framework that combines Fuzzy Analytic Hierarchy Process (FAHP) for uncertainty-aware weighting with a tunable VIKOR–PROMETHEE ranking stage. Weighting and ranking are kept distinct to support traceability and parameter sensitivity. A three-layer hierarchy organizes twenty-two criteria across organizational, project, group, and framework levels. In a single-enterprise validation with two independent expert panels (n = 10 practitioners), the tuned hybrid achieved lower rank error than single-method baselines (mean absolute error, $\mathrm{MAE}$ = 1.03; Spearman $\rho$ = 0.53) using pre-specified thresholds and a transparent $\alpha +\beta$ = 1 control. The procedure is practical for process governance: elicit priorities, derive fuzzy weights, apply the hybrid ranking, and verify stability with sensitivity analysis. The framework operationalizes modeling, optimization, control, and monitoring of scaling decisions, making trade-offs explicit and reproducible in industrial settings. Full article

The growing trend of using artificial intelligence models in many areas increases the need for a proper understanding of their functioning and decision-making. Although these models achieve high predictive accuracy, their lack of transparency poses major obstacles to trust. Explainable artificial intelligence (XAI) has emerged as a key discipline that offers a wide range of methods to explain the decisions of models. Selecting the most appropriate XAI method for a given application is a non-trivial problem that requires careful consideration of the nature of the method and other aspects. This paper proposes a systematic approach to solving this problem using multi-criteria decision-making (MCDM) techniques: ARAS, CODAS, EDAS, MABAC, MARCOS, PROMETHEE II, TOPSIS, VIKOR, WASPAS, and WSM. The resulting score is an aggregation of the results of these methods using Borda Count. We present a framework that integrates objective and subjective criteria for selecting XAI methods. The proposed methodology includes two main phases. In the first phase, methods that meet the specified parameters are filtered, and in the second phase, the most suitable alternative is selected based on the weights using multi-criteria decision-making and sensitivity analysis. Metric weights can be entered directly, using pairwise comparisons, or calculated objectively using the CRITIC method. The framework is demonstrated on concrete use cases where we compare several popular XAI methods on tasks in different domains. The results show that the proposed approach provides a transparent and robust mechanism for objectively selecting the most appropriate XAI method, thereby helping researchers and practitioners make more informed decisions when deploying explainable AI systems. Sensitivity analysis confirmed the robustness of our XAI method selection: LIME dominated 98.5% of tests in the first use case, and Tree SHAP dominated 94.3% in the second. Full article

Integrating phase change materials (PCMs) into buildings and HVAC systems improves thermal comfort and energy efficiency. This study presents a climate-responsive methodology for selecting optimal PCMs using a multi-criteria decision-making (MCDM) framework. AHP was employed to determine the relative importance of key thermophysical properties, including melting point (47.5%), latent heat of fusion (25.7%), volumetric latent heat (13.5%), thermal conductivity (6.8%), specific heat capacity (3.3%), and density (3.3%). These weights were applied across five MCDM techniques—COPRAS, VIKOR, TOPSIS, MOORA, and PROMETHEE II—to evaluate 16 PCM alternatives for three representative climate zones: temperate (18 °C), subtropical (23 °C), and tropical hot/desert (28 °C). The results consistently identified n-Heptadecane (C17) as the most suitable PCM for temperate and subtropical climates, while n-Octadecane (C18) and hydrated salts such as CaCl₂·6H₂O and Na₂CO₃·10H₂O were optimal for tropical zones. Results show that n-Heptadecane (C17) is optimal for temperate and subtropical zones (COPRAS K = 1.00; TOPSIS C = 0.79–0.82; PROMETHEE φ = 0.21–0.22), while n-Octadecane (C18) and hydrated salts such as CaCl₂·6H₂O and Na₂CO₃·10H₂O perform best in tropical climates (TOPSIS C = 0.85; PROMETHEE φ = 0.26). These PCMs offer high latent heat (up to 254 kJ·kg⁻¹) and volumetric storage (up to 381 MJ·m⁻³), enabling significant reductions in HVAC loads and improved indoor temperature stability. The convergence of rankings across methods and alignment with existing literature validate the robustness of the proposed approach. This framework supports informed material selection for sustainable building design and can be adapted to other climate-sensitive engineering applications. The framework introduces methodological innovations by explicitly mapping PCM melting points to climate-specific comfort bands, incorporating volumetric latent heat, and validating rankings through cross-method convergence (Spearman ρ > 0.99). Sensitivity analysis confirms robustness against weight perturbations. The approach supports practical PCM selection for both new and retrofit buildings, contributing to EU and US energy goals (e.g., 40% building energy use, DOE’s 50% reduction target). Full article

This study presents a comprehensive multi-criteria decision-making (MCDM) framework for evaluating and selecting optimal non-destructive testing (NDT) techniques for oil and gas facility maintenance operations. This research used a Fuzzy Analytic Hierarchy Process (FAHP) integrated with multiple MCDM methods to assess eight NDT techniques including radiographic testing, ultrasonic testing, and thermographic testing. The evaluation framework incorporated seven technical criteria and seven economic criteria. The FAHP results revealed spatial resolution (0.175) as the most critical technical criterion, followed by depth penetration (0.155) and defect characterization (0.143). For economic criteria, downtime costs (0.210) and operational costs (0.190) emerged as the most significant factors. This study used TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment of Evaluations), and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) methods to rank NDT techniques, with results consolidated using the CRITIC (CRiteria Importance Through Intercriteria Correlation) method. The final techno-economic analysis identified radiographic testing as the most suitable NDT method with a score of 0.665, followed by acoustic emission testing at 0.537. Visual testing ranked lowest with a score of 0.214. This research demonstrates the effectiveness of combining fuzzy logic with multiple MCDM approaches for NDT method selection in offshore welding operations. Full article

Multi-criteria Decision Making (MCDM) presents a novel approach towards truly holistic green sustainability, particularly within the context of chemical process plants (CPPs). ASPEN Plus v12.0 was utilised for two representative CPP cases: isopropanol (IPA) production via isopropyl acetate, and green ammonia (NH₃) production. An integrated Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was modelled in MATLAB v24.1 to prioritise the holistically green and sustainable pathways. Life cycle assessments (LCAs) were employed to select the pathways, and the most suitable sub-criteria per the four criteria are as follows: social, economic, environmental, and technical. In descending order of optimality, the pathways were ranked as follows for green NH₃ and IPA, respectively: Hydropower (HPEA) > Wind Turbine (WGEA) > Biomass Gasification (BGEA)/Solar Photovoltaic (PVEA) > Nuclear High Temperature (NTEA), and Propylene Indirect Hydration (IAH) > Direct Propylene Hydration (PH) > Acetone Hydrogenation (AH). Sensitivity analysis evaluated the FAHP–TOPSIS framework to be overall robust. However, there are potential uncertainties within and/or among sub-criteria, particularly in the social dimension, due to software and data limitations. Future research would seek to integrate FAHP with VIKOR and the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE-II). Full article

Photovoltaic systems are increasingly recognized as one of the most advanced, efficient, and rapidly developing methods of electricity generation, utilizing the limitless potential of solar radiation while offering environmentally sustainable solutions to contemporary energy challenges. However, despite their clear benefits, issues such as high initial investment costs and relatively low energy efficiency must be carefully addressed during the design phase. Key considerations include the quantity and type of panels, battery capacity and number, environmental conditions, site-specific factors, and the mathematical models and interconnection strategies of system components. This study proposes a two-stage optimization approach for standalone photovoltaic systems, employing three distinct optimization algorithms—NSGA-II, DEMO, and Particle Swarm Optimization—to minimize both the Loss of Load Probability (LLP) and the life cycle cost (LCC). In the second stage, optimal solutions from the Pareto front are evaluated using three multi-criteria decision-making techniques: the hybrid AHP-TOPSIS method, VIKOR, and PROMETHEE. The proposed framework is applied to systems with storage batteries designed for deployment in three Cypriot cities, aiming to meet energy demands of 10, 15, and 20 kWh. The findings reveal a strong correlation between economic and energy performance and the degree of load coverage, with the combination of the DEMO algorithm and the AHP-TOPSIS method emerging as the most effective solution. Full article

The structural design of prestressed arched trusses presents a complex challenge due to the need to balance multiple conflicting objectives such as structural performance, weight, and constructability. This complexity is further compounded by the interdependent nature of the structural elements, which necessitates a comprehensive optimization approach. Addressing this challenge is crucial for advancing construction practices and improving the efficiency and safety of structural designs. The integration of advanced optimization algorithms and decision-making techniques offers a promising avenue for enhancing the design process of prestressed arched trusses. This study proposes the use of three advanced multi-objective optimization algorithms: NSGA-III, CTAEA, and SMS-EMOA, to optimize the structural design of prestressed arched trusses. The performance of these algorithms was evaluated using generational distance and inverted generational distance metrics. Additionally, the non-dominated optimal designs generated by these algorithms were assessed and ranked using multiple multi-criteria decision-making techniques, including SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR. This approach allowed for a robust comparison of the algorithms and provided insights into their effectiveness in balancing the different design objectives. The results of the study indicated that NSGA-III exhibited superior performance with a GD value of 0.215, reflecting a closer proximity of its solutions to the Pareto front, and an IGD value of 0.329, indicating a well-distributed set of solutions across the Pareto front. In comparison, CTAEA and SMS-EMOA showed higher GD values of 0.326 and 0.436, respectively, suggesting less convergence to the Pareto front. However, SMS-EMOA demonstrated a balanced performance in terms of constructability and structural weight, with an IGD value of 0.434. The statistical significance of these differences was confirmed by the Kruskal–Wallis test, with p-values of $2.50\times {10}^{-15}$ for GD and $5.15\times {10}^{-06}$ for IGD. These findings underscore the advantages and limitations of each algorithm, providing valuable insights for future applications in structural optimization. Full article

The advancement toward sustainable infrastructure presents complex multi-objective optimization (MOO) challenges. This paper expands the current understanding of design frameworks that balance cost, environmental impacts, social factors, and structural integrity. Integrating MOO with multi-criteria decision-making (MCDM), the study targets enhancements in life cycle sustainability for complex engineering projects using precast modular road frames. Three advanced evolutionary algorithms—NSGA-II, NSGA-III, and RVEA—are optimized and deployed to address sustainability objectives under performance constraints. The efficacy of these algorithms is gauged through a comparative analysis, and a robust MCDM approach is applied to nine non-dominated solutions, employing SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR decision-making techniques. An entropy theory-based method ensures systematic, unbiased criteria weighting, augmenting the framework’s capacity to pinpoint designs balancing life cycle sustainability. The results reveal that NSGA-III is the algorithm converging towards the most cost-effective solutions, surpassing NSGA-II and RVEA by 21.11% and 10.07%, respectively, while maintaining balanced environmental and social impacts. The RVEA achieves up to 15.94% greater environmental efficiency than its counterparts. The analysis of non-dominated solutions identifies the $A_4$ design, utilizing 35 MPa concrete and B500S steel, as the most sustainable alternative across 80% of decision-making algorithms. The ranking correlation coefficients above 0.94 demonstrate consistency among decision-making techniques, underscoring the robustness of the integrated MOO and MCDM framework. The results in this paper expand the understanding of the applicability of novel techniques for enhancing engineering practices and advocate for a comprehensive strategy that employs advanced MOO algorithms and MCDM to enhance sustainable infrastructure development. Full article

Using a multi-criteria decision support method (WASPAS) to analyze and rank alternatives, this article proposes a method to assist in the selection of helicopter models that are the most suitable for police air activity in the State of Rio de Janeiro. A robust technical basis for defining the essential requirements of an aircraft is established, and solutions that can ensure the effective and safe execution of missions are indicated. Helicopter models were evaluated by considering predefined criteria, and the weights of these criteria were attributed using a questionnaire that was administered to pilots and aerostatic operators of Public Air Units (UAP) in several states of the federation. As a result of the evaluation of the 15 helicopter models used by police services in the State of Rio de Janeiro, the modeling with the WASPAS method ranked the Sikorsky UH-60 (Black Hawk) model in first place, the Leonardo AW 139 model in second place, and the Bell 412 model in third place. Based on the available data, we suggest that a comparative study integrating the Entropy and CRITIC methods be conducted to measure the weights of the criteria associated with the application of other multi-criteria techniques, such as COMET, MACAB, SPOTIS, VIKOR, SAPEVO, and PROMETHEE. Full article

Decision making is the process of making choices by organizing relevant information and evaluating alternatives. MCDMs (Multi-Criteria Decision Methods) help to select and prioritize alternatives step by step. These tools can help in many engineering fields where the problem is complex and advanced. However, there are some limitations of the different MCDMs that reduce the reliability of the decision that needs to be improved and highlighted. In this study, Elimination and Choice Expressing Reality (ELECTRE) NI (Net Inferior), NS (Net Superior), Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), VIekriterijumsko KOmpromisno Rangiranje (VIKOR), Multi-Objective Optimization Ratio Analysis (MOORA), Weight Sum Method (WSM) and Weighted Product Method (WPM) are applied for the selection of urban heat mitigation measurements under certain criteria. The models were applied using weighting criteria determined by two ways, (i) the direct weighting method and (ii) the Analytic Hierarchy Process (AHP), for precise weighting factoring through pairwise comparison. This numerical research evaluated the reliability of MCDMs using the same decision matrix under different normalization techniques and shows the impact of AHP on the decision. The results show that WSM and PROMETHEE provided reliable and consistent results for all normalization techniques. The combination of AHP with applied MCDMs improved the frequency of consistent ranking, except with ELECTRE-NS. Full article

The Network Slice Selection Function (NSSF) in heterogeneous technology environments is a complex problem, which still does not have a fully acceptable solution. Thus, the implementation of new network selection strategies represents an important issue in development, mainly due to the growing demand for applications and scenarios involving 5G and future networks. This work presents an integrated solution for the NSSF problem, called the Network Slice Selection Function Decision-Aid Framework (NSSF DAF), which consists of a distributed solution in which a part is executed on the user’s equipment (for example, smartphones, Unmanned Aerial Vehicles, IoT brokers) functioning as a transparent service, and another at the Edge of the operator or service provider. It requires a low consumption of computing resources from mobile devices and offers complete independence from the network operator. For this purpose, protocols and software tools are used to classify slices, employing the following four multicriteria methods to aid decision making: VIKOR (Visekriterijumska Optimizacija i Kompromisno Resenje), COPRAS (Complex Proportional Assessment), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and Promethee II (Preference Ranking Organization Method for Enrichment Evaluations). The general objective is to verify the similarity among these methods and applications to the slice classification and selection process, considering a specific scenario in the framework. It also uses machine learning through the K-means clustering algorithm, adopting a hybrid solution in the implementation and operation of the NSSF service in multi-domain slicing environments of heterogeneous mobile networks. Testbeds were conducted to validate the proposed framework, mapping the adequate quality of service requirements. The results indicate a real possibility of offering a complete solution to the NSSF problem that can be implemented in Edge, in Core, or even in the 5G Radio Base Station itself, without the incremental computational cost of the end user’s equipment, allowing for an adequate quality of experience. Full article

Multicriteria methods have gained traction in academia and industry practices for effective decision-making. This systematic review investigates and presents an overview of multi-criteria approaches research conducted over forty-four years. The Web of Science (WoS) and Scopus databases were searched for papers on multi-criteria methods with titles, abstracts, keywords, and articles from January 1977 to 29 April 2022. Using the R Bibliometrix tool, the bibliographic data was evaluated. According to this bibliometric analysis, in 131 countries over the past forty-four years, 33,201 authors have written 23,494 documents on multi-criteria methods. This area’s scientific output increases by 14.18 percent every year. China has the highest percentage of publications at 18.50 percent, followed by India at 10.62 percent and Iran at 7.75 percent. Islamic Azad University has the most publications with 504, followed by Vilnius Gediminas Technical University with 456 and the National Institute of Technology with 336. Expert Systems with Applications, Sustainability, and the Journal of Cleaner Production are the top journals, accounting for over 4.67 percent of all indexed works. In addition, E. Zavadskas and J. Wang have the most papers in the multi-criteria approaches sector. AHP, followed by TOPSIS, VIKOR, PROMETHEE, and ANP, is the most popular multi-criteria decision-making method among the ten nations with the most publications in this field. The bibliometric literature review method enables researchers to investigate the multi-criteria research area in greater depth than the conventional literature review method. It allows a vast dataset of bibliographic records to be statistically and systematically evaluated, producing insightful insights. This bibliometric study is helpful because it provides an overview of the issue of multi-criteria techniques from the past forty-four years, allowing other academics to use this research as a starting point for their studies. Full article