Multi-criteria Approach Research Articles

An Intelligent Multi-Criteria Decision Approach for Selecting the Optimal Operating System for Educational Environments

The selection of an appropriate operating system (OS) in educational environments is a critical decision that impacts the functionality, user experience, and overall efficiency. Financial factors, along with the availability and functionality of tools used on these systems, play a crucial role in this selection. Furthermore, the OS affects the user experience, security, and adaptability to learning. Previous research in this area is scarce, and this paper contributes to a better understanding of the OS selection process in education. This paper proposes a novel multi-criteria decision-making approach to evaluate and select the most suitable OS for educational institutions. The methodology integrates the PIPRECIA method for assessing weighted criteria, alongside utility analysis (NWA), enabling a balanced decision-making process between institutional management and IT experts. The evaluation considered the following criteria: performance, cost, security, usability, implementation, support, and documentation. The study compared three popular operating systems: Microsoft Windows 11, GNU Linux Ubuntu 22.04 LTS, and Apple macOS 12 using the proposed approach. The results, based on the integrated evaluation of all criteria, indicate that GNU Linux Ubuntu 22.04 LTS (0.562) ranked highest, followed by Microsoft Windows 11 (0.553) and Apple macOS 12 (0.543). This paper emphasizes the importance of objective, group-based decision-making in selecting an OS for education, providing practical insights and guidelines for effective technology integration in academic settings.

Multi-Criteria Optimal Operation Strategy for Photovoltaic Systems in Large-Scale Logistics Parks Concerning Climate Impact

Solar power is widely regarded as one of the most promising renewable resources for generating electricity and reducing building energy consumption. Logistics parks, with their low-rise buildings and extensive rooftop areas, offer significant advantages for solar energy utilization via rooftop photovoltaics (PVs). However, limited research has been conducted on the proper operational principles and optimized control strategies for the PV systems of logistics parks, particularly regarding the mismatch between power generation and the loads of various building types under varying climatic conditions. This study proposes four optimal PV operation strategies for large-scale logistics parks across diverse climatic regions, developed using a multi-criteria optimization approach. The strategies optimize the azimuth and tilt angles of PV panels under four adjustment frequencies: annual, semi-annual, seasonal, and monthly. The investigated strategies are validated in a 5500 m2 logistics park, comprising refrigerated storage, warehouses, sorting centers, and other facilities. The results indicate that the proposed strategies outperform conventional fixed-angle approaches, with the monthly adjustment strategy delivering the best performance. Economic costs are reduced by 9.26–17.02%, while self-sufficiency can be improved by 2.00–7.08%. Cold regions with high solar radiation show particularly significant benefits, with self-consumption increasing by 82.44–359.04%. This study provides valuable insights and practical guidelines for optimizing PV system operations in logistics parks, offering enhanced energy efficiency and cost-effectiveness.

Multi-Criteria Sustainability Analysis for Variable Refrigerant Flow System Manufacturer Selection: An Integrated Multi-Criteria Decision-Making Approach to Environmental and Technical Performance

Multi-Criteria Sustainability Analysis for Variable Refrigerant Flow System Manufacturer Selection: An Integrated Multi-Criteria Decision-Making Approach to Environmental and Technical Performance

Prioritization of Preventive Measures: A Multi-Criteria Approach to Risk Mitigation in Road Infrastructure Projects

Risk management in construction projects is a critical process aimed at identifying, evaluating, and mitigating potential risks that could impact project performance. Preventive measures play a central role in this process, serving as proactive strategies to minimize the likelihood and impact of risks on project outcomes. This study involved 37 experts from multidisciplinary fields related to road infrastructure, ensuring a diverse and comprehensive perspective on risk evaluation and prevention. The DELPHI method was employed to systematically define key risks and their corresponding preventive measures, providing a structured foundation for further analysis. The experts evaluated 302 preventive measures across 56 risks using 4 predefined criteria: implementation costs, time required for implementation, implementation complexity, and probability of success. A multi-criteria decision making (MCDM) approach was then applied to analyze these evaluations, enabling the prioritization of preventive measures and the allocation of resources toward the most effective strategies. Additionally, fuzzy logic was employed to analyze and validate the results, providing a complementary approach to the MCDM methodology. The results of this research provide a robust framework for risk management, offering practical guidance for decision makers in the construction industry. By integrating expert judgment, systematic evaluation, and advanced analytical methods, this study delivers actionable insights and establishes a reliable methodology for enhancing the effectiveness of risk mitigation in road infrastructure projects.

An Integrated Two-Step Optimization Model and Aggregative Multi-Criteria Approach for Establishing Sustainable Tram Transportation Plan

The choice of the most appropriate sustainable scheme for the organization of tram transportation in cities is of great importance for tram operators, for users of transportation services, and for the protection of the environment from harmful emissions. This study aims to propose a methodology for formulating a tram transportation plan considering technological, environmental, economic, and social indicators. The variant schemes represent the routes of a tram in the tram network. The methodology includes four stages. The first stage involves the determination of variant schemes for a transportation plan of service with trams. In the second stage, a two-step optimization model is proposed to determine the number and trams by types for each tram route for each variant scheme, and also to establish the distribution of trams by depots. The third stage includes ranking the variant schemes by applying the sequential interactive model for urban systems (SIMUS) multi-criteria method. Eleven quantitative and qualitative criteria for evaluating the tram transportation plan were introduced. A verification of the results is performed in the fourth stage. For this purpose, a comparison of the preference ranking organization method for enrichment of evaluations (PROMETHEE) method and the technique for order of preference by similarity to ideal solution (TOPSIS) method is made. Both methods have different approaches for decision making and differ from the SIMUS method. Two strategies were proposed to determine the criteria weights. One is based on the Shannon entropy method and the other uses the objective weights obtained through the SIMUS method. Finally, in the fifth stage, the results obtained through the SIMUS, PROMEHEE and TOPSIS methods are combined using the expected value obtained by applying the program evaluation and review technique method (PERT). The proposed methodology was applied to study tram transportation in Sofia, Bulgaria. Five variant schemes were considered. The schemes are optimized through the criterion of minimum energy consumption. The number of trams by routes and by type was determined. An improved scheme for tram transportation in Sofia was proposed. The scheme makes it possible to increase the frequency of the trams by 13%, to reduce the zero mileage of rolling stock, and to reduce carbon dioxide pollution by 11%.

Sustainable solar energy deployment: a multi-criteria decision-making approach for site suitability and greenhouse gas emission reduction.

Conventional power generation methods have led to adverse environmental impacts. Thus, the need for a strategic transition to alternative energy sources arises. This study presents a comprehensive approach to sustainable solar energy deployment using multi-criteria decision-making (MCDM) techniques. The research aims to identify suitable sites for utility-scale solar photovoltaic (PV) installations, estimate potential energy output, and assess solar PV deployment's environmental and economic impacts. The methodology integrates analytic hierarchy process (AHP), fuzzy logic, and geographic information systems (GIS) to evaluate land suitability across four scenarios. The analysis considers technical, economic, environmental, and social factors, including solar radiation, proximity to infrastructure, land use, topography, and stakeholder opinions. Results reveal that Scenario 1 analytic hierarchy process multi-criteria-decision-making (AHP-MCDM) identified 2824.1 km2 of suitable land, while the combined approach in Scenario 3 yielded 666.9 km2. The study estimates potential annual energy generation ranging from 19.69 to 109.15 GWh/km2/year, depending on the scenario and solar PV technology used. Environmental impact assessments indicate potential annual CO2 emission reductions of up to 51,365.84 tons, with associated cost savings of US $3.28 million. The research provides valuable insights for policymakers and investors, highlighting 16 optimal sites for utility-scale solar farm development across Jamaica, with the most promising in the Westmoreland, Manchester, and St. Mary parishes. These findings contribute to Jamaica's renewable energy goals and offer a replicable, sustainable solar energy planning framework in similar geographical contexts.

Soil classification and land suitability evaluation for tomato cultivation using analytic hierarchy process under different land uses.

Population growth has led to excessive land use, affecting soil suitability and sustainability. Detailed soil characterization and land evaluation for various land uses are essential steps toward achieving food security and sustaining the environment. This study classifies soils and assesses their suitability for tomato cultivation using the FAO Land Assessment Framework and Analytical Hierarchy Process (AHP) model. The land use requirements were matched with land suitability criteria, and the Analytical Hierarchy Process (AHP) was used to determine the relative importance of each evaluation criterion. Soil morphological and physicochemical properties were analyzed across different land uses. Based on USDA Soil Taxonomy, the soils under land use HK1 were identified as Typic Endoaqualfs, HK2 as Typic Albaqualfs and Inceptic Haplustalfs, while HK3 was categorized as Typic Haplustalfs and Typic Plinthaqualfs. The World Reference Base classification system identified these soils as Gleysols for soil unit HK1, Luvisols for pedon HK2P1, and Lixisols for the remaining soil units. This study demonstrates the effectiveness of the AHP method in logically determining the weights of multiple parameters in a multi-criterion approach. The results show that 89.73% of the area is highly suitable for tomato cultivation, while 10.27% is moderately suitable. The primary limitations of these soils are low fertility and poor drainage. The study suggests that management techniques including incorporation of organic matter, inorganic fertilizers and improved drainage conditions are needed for sustainable productivity.

Enhancing urban agriculture networks: A clustering and multicriteria decision-making approach to sustainability indicators and governance

Enhancing urban agriculture networks: A clustering and multicriteria decision-making approach to sustainability indicators and governance

Comparative assessment of Watershed Hydrological Health (WHH) using multi-criteria decision-making approach based on PSR framework.

The health of a watershed is a crucial aspect that involves evaluating ecological, hydrological, and human factors to determine its overall well-being and functionality. Hydrological components are key parts of a watershed. Therefore, the present study aims to assess Watershed Hydrological Health (WHH) using the Pressure-State-Response (PSR) framework and to compare WHH priorities derived from Multi-Criteria Decision Making (MCDM) approaches in the Gorganroud Watershed, Iran. To do this, with a problem-oriented approach, 16 hydrological criteria were conceptualized in three indices of Pressure (P), State (S), and Response (R) (PSR). In the next step, each of the mentioned criteria was quantified in 16 sub-watersheds of the Gorganroud Watershed. Finally, WHH was calculated based on the PSR framework. According to the criteria Importance Through Intercriteria Correlation (CRITIC) method, the criteria were then weighted, and WHH was calculated using three MCDM methods. To compare the results, the results of the PSR framework were used as the basis and compared with three MCDM methods. The results showed that WHH values in the Gorganroud Watershed ranged from 0.31 to 0.67 in S15 and S9, respectively. Overall, the weighted average WHH in the watershed was 0.52, indicating moderate conditions. Weighting results showed that criteria C4 and C14 had the highest and lowest weights, respectively. WHH priorities based on MCDM methods showed that in the ELimination Et Choice Translating REality (ELECTRE) and Simple Additive Weighting (SAW) methods, sub-watershed S9 had the highest WHH priority, while in the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, sub-watershed S7 achieved the highest priority. Among these, the ELECTRE and SAW methods performed better in determining WHH priorities and are recommended for future research.

Assessing power-to-heat technologies for industrial electrification: A multi-criteria decision analysis approach

Assessing power-to-heat technologies for industrial electrification: A multi-criteria decision analysis approach

Sustainability assessment of low-impact development methods for urban stormwater management: A multi-criteria decision-making approach

Low-impact development (LID) methods are used in urban areas as sustainable stormwater management solutions. However, previous studies often lack situational scenario-based assessments and cover a limited range of criteria and indices, overlooking key factors, especially in developing countries facing significant sustainability challenges. This study addresses these gaps by assessing the sustainability of four LID alternatives, including novel combinations of rain barrels (RBs), rain gardens (RGs), permeable pavements (PPs), infiltration trenches (ITs), and vegetative swales (VSs), for inside and outside the house in a case study located in Tehran, Iran. In addition, 22 quantitative/qualitative indices were selected across environmental, social, economic, and technical criteria. The three-phase indices evaluation involved 1) simulating the study area by the stormwater management model (SWMM), 2) collecting quantitative data, and 3) scoring qualitative indices through questionnaires. A modified analytical hierarchy process model was employed to rank LID alternatives in difficult, poor, transition, satisfied, and ideal scenarios. Considering situational scenarios, the results showed that IT and VS for outside the house consistently performed best, adding novel contributions. Sensitivity analysis confirmed the robustness of this finding despite changing criteria weights. This study provides a new framework for urban decision-makers in developing countries to adopt resilient stormwater solutions.

Evaluation of Vision-Language Transformers for Multimodal News Authenticity and Integrity in Journalism: A Multi-Criteria Decision-Making Approach

Evaluation of Vision-Language Transformers for Multimodal News Authenticity and Integrity in Journalism: A Multi-Criteria Decision-Making Approach

A GIS-based on application of Monte Carlo and multi-criteria decision-making approach for site suitability analysis of solar-hydrogen production: Case of Cameroon.

This article analyzes and compares three methodologies for identifying suitable regions for solar hydrogen production using photovoltaic panels: AHP (Analytic Hierarchy Process), FAHP (Fuzzy Analytic Hierarchy Process), and MC-FAHP (Monte Carlo FAHP), integrated with GIS (Geographic Information Systems). The study employs ten criteria across technical (Global Horizontal Irradiance, temperature, slope, elevation, orientation), economic (distance from transportation and electrical networks), and social (population density, proximity to residential areas) factors. Environmental and exclusion criteria define restrictive zones. The analysis reveals that while all three methods agree on areas of low suitability, they diverge in their classification of "Suitable," "Highly Suitable," and "Most Suitable" regions. FAHP identifies 229.573km2 as "Highly Suitable," compared to AHP's 222.048km2 and MC-FAHP's 230.299km2 for "Suitable" areas. Despite these differences, the energy potential is consistent across methods, totaling around 79,000TWh/year, with MC-FAHP estimating the highest hydrogen production potential at 1.51 billion tons/year. The study concludes that fuzzy-based methods (FAHP and MC-FAHP) better handle uncertainties than traditional AHP. The MC-FAHP method, in particular, performs well in managing stochastic variability and yielding more reliable results. The findings are validated through a case study in Guider and Maroua, highlighting the importance of socio-economic and environmental criteria in decision-making. A sensitivity analysis reveals that economic and social criteria significantly influence land suitability, underscoring the importance of criteria selection in decision-making.

Integrated Multi-Criteria Decision-Making Approach for Power Generation Technology Selection in Sustainable Energy Systems

Integrated Multi-Criteria Decision-Making Approach for Power Generation Technology Selection in Sustainable Energy Systems

Multi-criteria Bayesian optimization of Empirical Mode Decomposition and hybrid filters fusion for enhanced ECG signal denoising and classification: Cardiac arrhythmia and myocardial infarction cases

This paper introduces a new advanced model for denoising and classification of ECG signals, focusing on the use of a hybrid filter and Bayesian optimization. The hybrid filter synergistically combines enhanced empirical mode decomposition (EEMD), Chebyshev Type II filters, Butterworth, Daubechies Wavelet, and Savitzky-Golay filters, leveraging their respective advantages for effective noise reduction while preserving the essential features of the ECG signal. We employ a multi-criteria Bayesian optimization process, using cross-correlation and mean squared error (MSE) as key metrics, to refine the filter parameters to further improve the signal quality. Additionally, we deploy a residual and parallel deep learning architecture adapted for the precise classification of cardiac conditions, such as arrhythmia and myocardial infarction. The results show a significant improvement in classification accuracy, from 96.63%–97.86% to 97.24%–98.62% after filtering, and reaching 98.03%–99.61% after optimization for arrhythmia. For myocardial infarction, accuracy increases from 96.29%–97.42% to 97.34%–98.44% after filtering, and from 98.13%–99.07% after optimization. This holistic and multi-criteria approach enhances the reliability of medical diagnostics and represents a significant advancement in the processing of biomedical signals.

Implications of critical success factors for digital transformation in Vietnam using a hybrid neutrosophic analytic hierarchy process

ABSTRACT The digital transformation of Vietnam is a crucial strategic priority for the country’s rapid and sustainable development. This article presents a new method that combines the neutrosophic analytical hierarchy process (NAHP) with the technique for order of preference by similarity to ideal solution (TOPSIS) to analyse the impact of critical success factors (CSFs) on digital transformation. The NAHP was employed to ascertain the weights of the criteria, which serve as the most important indications of success in digital transformation. After obtaining the criteria weights, the alternatives were ranked based on their performance relative to the criteria provided by the TOPSIS technique. With the success of digital transformation activities, firms have substantial acceptance of the reliability of technological, organisational, and environmental (TOE) factors. The study investigated several factors, including leadership, organisational culture, technology infrastructure, and customer-centricity. The proposed methodology required evaluation by implementing a practical case study involving TOE critical factors in the Vietnamese digital transformation sector. Several methodologies and multi-criteria approaches have been developed to assess the key factors that contribute to the successful digital transformation of enterprises. The findings highlighted the validity of the hybrid NAHP-TOPSIS methodology in determining CSFs necessary for achieving objectives relevant to digital transformation.

Strategic Site Selection for Bio-LNG Plant in Indonesia: A Multi-Criteria Scoring Method Approach

In Indonesia, the energy sector is still predominantly reliant on fossil fuels, with renewable energy, including liquefied biomethane (Bio-LNG), playing a limited role. Nonetheless, Indonesia has significant potential for Bio-LNG development due to its abundant organic waste resources such as Palm Oil Mill Effluent (POME) from palm oil mills. Having approximately 891 Palm Oil Mills in Indonesia and being spread mostly in Sumatra, this study aims to select a strategic location for a Bio-LNG plant that can enhance logistical efficiency and economic viability for the plant. This study uses Multi-Criteria Decision Making (MCDM) methodologies to assess potential sites based on key criteria: Land Availability, Distance to Source of Feedstock, Distance to Customer Locations, Jetty Availability, and Future Business Developments. This study has narrowed down into focusing on provinces with substantial raw material potential, Riau Province, particularly Dumai City and its surroundings, which emerges as a pivotal hub. Secondary data are gathered to conduct a systematic scoring process evaluates and integrates each criterion to generate a comprehensive ranking of potential sites. The selected site for the Bio-LNG plant is Location 1, at the coordinates of 1.662056, 101.707092, Pelintung, Medang Kampai Subdistrict, Dumai City, Riau. Its strategic positioning, proximity to key transportation routes, availability of abundant feedstock, and access to necessary infrastructure make it an ideal choice. This study aims to advance Bio-LNG infrastructure in Indonesia, promoting cleaner energy solutions, and enhanced supply chain efficiency.

Leveraging fuzzy embedded wavelet neural network with multi-criteria decision-making approach for coronary artery disease prediction using biomedical data

Coronary artery disease (CAD) is the main cause of death. It is a complex heart disease that is linked with many risk factors and a variety of symptoms. In the past few years, CAD has experienced a remarkable growth. Prompt risk prediction of CAD would be capable of decreasing the death rate by permitting timely and targeted treatments. Angiography is the most precise CAD diagnosis technique; however, it has several side effects and is expensive. Multi-criteria decision-making approaches can well perceive CAD by analysing main clinical indicators like ChestPain type, ST_Slope, and HeartDisease presence. By assessing and evaluating these factors, the model improves diagnostic accuracy and aids informed clinical decisions for quick CAD detection. Mainly machine learning (ML) and deep learning (DL) use plentiful models and algorithms, which are commonly employed and very useful in exactly detecting the CAD within a short time. Current studies have employed numerous features in gathering data from patients while using dissimilar ML and DL models to attain results with high accuracy and lesser side effects and costs. This study presents a Leveraging Fuzzy Wavelet Neural Network with Decision Making Approach for Coronary Artery Disease Prediction (LFWNNDMA-CADP) technique. The presented LFWNNDMA-CADP technique focuses on the multi-criteria decision-making model for predicting CAD using biomedical data. In the LFWNNDMA-CADP method, the data pre-processing stage utilizes Z-score normalization to convert an input data into a uniform format. Furthermore, the improved ant colony optimization (IACO) method is used for electing an optimum sub-set of features. Furthermore, the classification of CAD is accomplished by utilizing the fuzzy wavelet neural network (FWNN) technique. Finally, the hyperparameter tuning of the FWNN model is accomplished by employing the hybrid crayfish optimization algorithm with the self-adaptive differential evolution (COASaDE) technique. The simulation outcomes of the LFWNNDMA-CADP approach are investigated under a benchmark database. The experimental validation of the LFWNNDMA-CADP approach portrayed a superior accuracy value of 99.49% over existing techniques.

Challenges in Transitioning from Industry 4.0 to Industry 5.0: A Multicriteria Analysis

In this study, we evaluate the transition from Industry 4.0 (I4.0) to Industry 5.0 (I5.0) using a multicriteria approach that integrates the Analytic Hierarchy Process (AHP) and Fuzzy Inference Systems (FISs). Key criteria identified through a comprehensive literature review include sustainability, human factors, resilience and organizational management, which are critical for the successful implementation of I5.0. AHP is utilized to rank these criteria and assign relative weights, reflecting their importance in the overall evaluation. FIS is employed to manage the inherent uncertainty and imprecision in decision-making within the context of I5.0. The results provide a holistic view of how companies navigate this transition, highlighting their strengths and weaknesses. This study offers valuable insights into the challenges and opportunities associated with adopting I5.0, emphasizing the utility of combining multicriteria approaches for strategic decision-making in dynamic industrial environments. These findings aim to support organizations in making informed decisions, fostering a more innovative, sustainable and human-centric industry. The combination of these methodologies provides a robust foundation for future research and practical applications in advancing I5.0.

Optimizing Site Selection for Construction: Integrating GIS Modeling, Geophysical, Geotechnical, and Geomorphological Data Using the Analytic Hierarchy Process

Identifying suitable sites for urban, industrial, and tourist development is important, especially in areas with increasing population and limited land availability. Kharga Oasis, Egypt, stands out as a promising area for such development, which can help reduce overcrowding in the Nile Valley and Delta. However, soil and various environmental factors can affect the suitability of civil engineering projects. This study used Geographic Information Systems (GISs) and a multi-criteria decision-making approach to assess the suitability of Kharga Oasis for construction activities. Geotechnical parameters were obtained from seismic velocity data, including Poisson’s ratio, stress ratio, concentration index, material index, N-value, and foundation-bearing capacity. A comprehensive analysis of in situ and laboratory-based geological and geotechnical data from 24 boreholes examined soil plasticity, water content, unconfined compressive strength, and consolidation parameters. By integrating geotechnical, geomorphological, geological, environmental, and field data, a detailed site suitability map was created using the analytic hierarchy process to develop a weighted GIS model that accounts for the numerous elements influencing civil project design and construction. The results highlight suitable sites within the study area, with high and very high suitability classes covering 56.87% of the land, moderate areas representing 27.61%, and unsuitable areas covering 15.53%. It should be noted that many settlements exist in highly vulnerable areas, emphasizing the importance of this study. This model identifies areas vulnerable to geotechnical and geoenvironmental hazards, allowing for early decision-making at the beginning of the planning process and reducing the waste of effort. The applied model does not only highlight suitable sites in the Kharga Oasis, Egypt, but, additionally, it provides a reproducible method for efficiently assessing land use suitability in other regions with similar geological and environmental conditions around the world.