Multi-criteria Decision-making Approach Research Articles

Multi-Criteria Decision-Making for Selecting Solar Window Film Sheets for Energy Saving in Buildings

Recently, there have been several advancements in the field of sustainable energy solutions, particularly in the selection of solar window film sheets. In this research, a multi-criteria decision-making approach was applied to compare three different types of window film sheets, Silver 35, TrueVue 15, and Sterling 40, to aid in selecting the most suitable window film based on the United Arab Emirates market. The primary aim of this work is to provide decision-makers with a structured approach to enhance their choices for selecting window film sheets. The methodology employed involves evaluating various criteria, including visible light transmittance, solar energy rejected, energy transmittance, energy absorptance, cost, glare reduction, visible light reflectance interior, and fade reduction. These criteria are assessed using the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results demonstrate that Sterling 40 is the best choice followed by Silver 35. Based on the final TOPSIS results, the difference between the scores of these two window film sheets was not significant, while they were far from the score of TrueVue 15.

Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

Skin cancer is a type of cancer disease in which abnormal alterations in skin characteristics can be detected. It can be treated if it is detected early. Many artificial intelligence-based models have been developed for skin cancer detection and classification. Considering the development of numerous models according to various scenarios and selecting the optimum model was rarely considered in previous works. This study aimed to develop various models for skin cancer classification and select the optimum model. Convolutional neural networks (CNNs) in the form of AlexNet, Inception V3, MobileNet V2, and ResNet 50 were used for feature extraction. Feature reduction was carried out using two algorithms of the grey wolf optimizer (GWO) in addition to using the original features. Skin cancer images were classified into four classes based on six machine learning (ML) classifiers. As a result, 51 models were developed with different combinations of CNN algorithms, without GWO algorithms, with two GWO algorithms, and with six ML classifiers. To select the optimum model with the best results, the multicriteria decision-making approach was utilized to rank the alternatives by perimeter similarity (RAPS). Model training and testing were conducted using the International Skin Imaging Collaboration (ISIC) 2017 dataset. Based on nine evaluation metrics and according to the RAPS method, the AlexNet algorithm with a classical GWO yielded the optimum model, achieving a classification accuracy of 94.5%. This work presents the first study on benchmarking skin cancer classification with many models. Feature reduction not only reduces the time spent on training but also improves classification accuracy. The RAPS method has proven its robustness in the problem of selecting the best model for skin cancer classification.

An integrated MCDM approach for enhancing efficiency in connected autonomous vehicles through augmented intelligence and IoT integration

The rapid advancement of Connected Autonomous Vehicles (CAVs) equipped with self-powered sensors is poised to revolutionize road safety, efficiency, and the quality of travel. However, the effective integration of these technologies within dynamic road environments poses significant challenges, highlighting the need for innovative multi-criteria decision-making (MCDM) approaches to optimize their deployment. This study tries to solve the integration problem by proposing an MCDM method that uses fuzzy sets to evaluate and rank different scenarios for better performance of augmented intelligence and the Internet of Things (IoT) in CAVs. This research uses two key techniques: the Fuzzy Logarithm of Incremental Weights (F-LMAW) method for criteria evaluation and the Fermatean Fuzzy Weighted Aggregated Sum Product Assessment (FF-WASPAS) method for scenario evaluation. To ensure the reliability and accuracy of the results, a sensitivity analysis was conducted. The analysis confirmed the effectiveness of the proposed approach. The study's results showed that the third scenario (the integration of augmented intelligence and IoT in urban areas via self-powered sensors) got the highest score, which shows how important it is compared to the other choices. The obtained results highlight the importance of integrating augmented intelligence and IoT technologies to enhance public transportation with autonomous vehicles equipped with self-powered sensors.

Evaluation of energy economic optimization models using multi-criteria decision-making approach

Achieving high performance in energy systems is crucial for sustainability. Energy economy optimization (EEO) models offer transparent analysis for energy policy decision-making. However, evaluating and benchmarking these models is a complex multicriteria decision making (MCDM) problem. Challenges include multiple criteria, data variation, and the importance of diverse criteria. This study develops an integrated MCDM approach to evaluate and benchmark EEO models. The methodology involves three phases. First, 12 commonly used EEO models and five evaluation criteria (software licenses, public source code, redistribution, public source data, and commercial software) are identified to create an evaluation decision matrix. Second, the fuzzy-weighted zero-consistency method (FWZIC) is used to evaluate and assign weights to the criteria. These weights are utilized in the benchmarking phase. Third, individual and group fuzzy decision by opinion score method (FDOSM) techniques are integrated to benchmark the EEO models based on the weights acquired. The FWZIC weighting reveals that the public source code criterion has the highest weight (0.3347), while redistribution has the lowest weight (0.1021). The group FDOSM results show that the OSeMOSYS model ranks first with the highest score (0.1595), while the DNE21+, MARIA, and MESSAGE models have the lowest score (0.0646), ranking them last. Systematic ranking, sensitivity ranking, and comparative analysis verify the proposed evaluation and benchmarking framework.

Improving the crashworthiness of bi-tubular architectures with ABS cores under axial loading: Experimental and numerical investigation

In this study, quasi-static axial compression tests were conducted on mild steel bi-tubular architectures with rectangular nested tube (RNT) and square nested tube (SNT) geometries to evaluate their crushing and crashworthiness performance. A multi-criteria decision-making approach was employed to identify the optimal energy-absorbing architecture. The SNT structure, with the smallest gap size between the inner and outer tubes, exhibited the most desirable energy absorption characteristics among the considered cases. Acrylonitrile butadiene styrene (ABS) cores, with either rhombic or square cell configurations, were used to enhance the energy absorption performance of the SNT structure. A finite element model was created to evaluate the responses of the SNT structure filled with ABS cores. The validity of finite element simulations of the ABS cores and optimal architecture under axial compression were confirmed by comparing them with experimental results. The integration of the cores into the nested architecture enhanced crashworthiness performance and contributed to the control of the structure deformation. The SNT structure filled with rhombic ABS core exhibited superior crashworthiness performance compared to the counterpart filled with square core. The energy absorption of nested SNT structures filled with rhombic ABS core can be 116.93% greater than the corresponding non-filled structure. The crashworthiness indices of ABS-filled structures were highly sensitive to the number of cells and wall thickness of the core. A nested architecture with an ABS core could serve as a novel architecture for energy-absorbing devices.

A novel interval rough model for optimizing road network performance and safety

This paper introduces a novel Integrated Interval Rough Pivot Pairwise Relative Criteria Importance Assessment (IRN PIPRECIA) model combined with Interval Rough Combined Compromise Solution (IRN CoCoSo), marking a significant advancement in sustainable traffic flow management for commercial vehicles. This innovative merger is a first in literature, methodologically enhancing the evaluation of road sections based on critical parameters including passenger car equivalent (PCE) 85%, AADT, road conditions, and accident data. Our model systematically fills the research gap in holistic traffic performance analysis, providing a unique tool for prioritizing road safety and efficiency. The key scientific contribution is the model’s ability to integrate causal and consequential traffic factors into a single framework, offering a novel multi-criteria decision-making (MCDM) approach. Results, validated through various verification models, show the integrated model’s effectiveness in real-world scenarios, confirming its robustness and stability. With strong engineering application potential, our work supports urban planners and traffic managers in making informed, sustainable decisions. The model extends beyond traditional traffic analysis, promising a shift towards more adaptive, data-driven infrastructure management. Future research will aim to refine the criteria basis and explore real-time decision-making through advanced MCDM applications.

A Multi-Criteria Decision-Making Approach for Assessing the Sustainability of an Innovative Pin-Connected Structural System

A Multi-Criteria Decision-Making Approach for Assessing the Sustainability of an Innovative Pin-Connected Structural System

Assessment and selection of a micro-hybrid renewable energy system for sustainable energy generation in rural areas of Zambia

This research focuses on the implementation of micro-hybrid renewable energy systems (MHRES) in rural Zambia, where a large part of the population lacks adequate electrical infrastructure. Using a multi-criterion decision-making approach (MCDM), specifically the Technique for Preference Order by Similarity to an Ideal Solution (TOPSIS) with an entropy-weighted method, this research aims to identify an optimal MHRES solution to replace dependence on wood and fossil fuels. Analysis is carried out in 14 scenarios, taking into account technical, economic, social, and environmental criteria. The results show that a biogas-solar photovoltaic integrated with Energy System Power Safe Storage Battery Systems 190 (SBS 190) is the most visible option, demonstrating a relative proximity of 0.98556. In contrast, a diesel generator-solar PV system is the least favorable, with a relative proximity of 0.02576. Validation of the results through a convergent distance assessment (CODAS), an evaluation based on the distance from the average solution (EDAS), and the weighted aggregate sum of the product assessment (WASPAS) confirm its robustness. The proposed MHRES solution is designed to meet the energy needs of rural communities using local biomass and solar resources, free of obstacles by high buildings and structures. This research highlights the potential of MHRES to meet energy needs and promote sustainable development in developing countries, providing valuable insights for policymakers and energy stakeholders.

A Comprehensive Approach to Biodiesel Blend Selection Using GRA-TOPSIS: A Case Study of Waste Cooking Oils in Egypt

The transition to sustainable energy sources is critical for addressing global environmental challenges. In 2017, Egypt produced about 500,000 tons of waste cooking oil from various sources including food industries, restaurants and hotels. Sadly, 90% of households choose to dispose of their used cooking oil by pouring it down the drain or into their village’s sewers instead of using proper disposal methods. The process involves converting waste cooking oil (WCO) into biodiesel.This study introduces a multi-criteria decision-making approach to identify the optimal biodiesel blend from waste cooking oils in Egypt. By leveraging the grey relational analysis (GRA) combined with the technique for order preference by similarity to the ideal solution (TOPSIS), we evaluate eight biodiesel blends (diesel, B5, B10, B20, B30, B50, B75, B100) against various performance metrics, including carbon monoxide, carbon dioxide, nitrogen oxides, hydrocarbons, particulate matter, engine power, fuel consumption, engine noise, and exhaust gas temperature. The experimental analysis used a single-cylinder, constant-speed, direct-injection eight cylinder diesel engine under varying load conditions. Our methodology involved feature engineering and model building to enhance predictive accuracy. The results demonstrated significant improvements in monitoring accuracy, with diesel, B5, and B20 emerging as the top-performing blends. Notably, the B5 blend showed the best overall performance, balancing efficiency and emissions. This study highlights the potential of integrating advanced AI-driven decision-making frameworks into biodiesel blend selection, promoting cleaner energy solutions and optimizing engine performance. Our findings underscore the substantial benefits of waste cooking oils for biodiesel production, contributing to environmental sustainability and energy efficiency.

Evaluation of Urban Complex Utilization Based on AHP and MCDM Analysis: A Case Study of China

In the context of intensive urban development, urban complexes have emerged as crucial public spaces that address the needs of urban populations. However, current research on urban complexes is predominantly qualitative and lacks a rigorous scientific and quantitative analysis. Therefore, this study employs the analytic hierarchy process (AHP) to construct a standardized system encompassing five dimensions: spatial function, spatial perception, architectural style, surrounding environment, and energy-saving technology. The objective is to determine the weights of the indices that influence people’s use of urban complexes under the goal of “humanization”. Additionally, the study quantitatively analyzes key indices using spatial syntax and other analytical methods. Subsequently, we employ multi-criteria decision making (MCDM) analysis to examine three real-world cases in China, aiming to validate further the importance of the AHP + MCDM approach, which incorporates the TOPSIS method based on grey correlation. This methodology considers both the subjective factors of crowd evaluations of urban complex usage and the interrelationships among indicators, ensuring that the statistical calculations of the indicators remain objective and scientifically robust. The results indicate that (1) the degree of facility improvement has the greatest impact on the crowd’s use of urban complexes; (2) there is a discrepancy between the results of the TOPSIS method and the MCDM evaluation model, with the MCDM evaluation method aligning more closely with real-world scenarios; and (3) the Shanghai MOSCHINO received the highest evaluation score, while the Nanjing Central Emporium received the lowest. Finally, we discuss the experimental results and propose targeted strategies for optimizing the design of urban complexes to achieve the goal of “humanization”.

A Review of the Sustainable Siting of Offshore Wind Farms

The continued technical and economic development of offshore wind farms needs to match their sustainable siting transparently and fairly. Aiming to assess existing methodologies widely used in the field of OWFs spatial planning, as well as to identify the proposed enhancements for the improvement of such methods, this study examines 80 peer-reviewed papers over the past eight years. The analysis encompasses articles from 34 scientific journals, with a notable concentration in the journals Renewable Energy, Renewable and Sustainable Energy Reviews, and Energies, and it sheds light on geographical distribution, journal classification, funding sources, and the various methodological approaches. Most of the studies were conducted in Turkey, China, and Greece; half of the surveyed papers utilize multi-criteria decision-making approaches, predominantly addressing bottom-fixed technologies for offshore wind farms, which currently dominate the field. The 80 papers are categorized into five methodological domains: Marine Spatial Planning, Feasibility Analysis, Probabilistic Methods, Meteorological Data, and Multi-Criteria Decision Making. One hundred and seventy criteria were identified and condensed into a final set of 41 critical criteria. This article provided an overview of the site selection process and the most crucial findings and recommendations.

Unveiling Afghanistan's wind and hydrogen potential: a comparative study.

Given the detrimental environmental impacts of fossil fuels, there is a gradual worldwide shift towards renewable energy sources. Wind power, renowned for its cost-effectiveness and simplicity, has been widely embraced. Despite Afghanistan facing significant challenges in its energy sector, its considerable wind energy potential offers a chance to mitigate some of these issues. This study employed a multi-criteria decision-making approach to evaluate potential wind-hydrogen project sites in Afghanistan, encompassing economic, technical, social, risk, and environmental considerations. Five criteria and sub-criteria for wind-hydrogen project implementation were analyzed using the Step-wise Weight Assessment Ratio Analysis (SWARA) method. The Weighted Aggregated Sum Product Assessment (WASPAS), Additive Ratio Assessment (ARAS), Evaluation based on Distance from Average Solution (EDAS), and Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) methods were then applied to prioritize provinces for wind-hydrogen project implementation. The analysis found that the Duration of the Payback Period and Levelized Cost of Electricity (LCOE) with weight of 6.6% and 5.6% were critical sub-criteria. Farah, Herat, and Nimroz emerged as Afghanistan's most promising provinces for wind-hydrogen energy development. In Farah, utilizing 900-kW turbines, it is feasible to generate 2679.8 MWh of electricity and produce 43.4 tons of hydrogen annually, with a Levelized Cost of Electricity of 0.0690 $/kWh and Levelized Cost of Hydrogen of 1.747 $/kg.

Spatial Suitability Index for Sustainable Urban Development in Desert Hinterland Using a Geographical-Information-System-Based Multicriteria Decision-Making Approach

This study uses the Aswan Governorate model in southern Upper Egypt to determine the spatial suitability of sustainable urban development in the Egyptian deserts, ensure resource sustainability, and contribute to environmentally sustainable urban and economic growth in line with Egypt’s Vision 2030. To achieve these goals, a GIS-based multicriteria decision-making (GIS-MCDM) approach was used, through an innovative approach for integrating three main indicators: the risk index, environmental sensitivity, and economic resources. These indicators were divided into twenty-four criteria, and their weight was determined according to preference through the analytic hierarchy process (AHP). This study serves developmental, national, and expected regional and local development efforts for developing desert regions in the Aswan Governorate, which exceed 80% of the governorate’s area and represent the future of sustainable urban development. This study found three spatial suitability categories, with the largest between 50 and 70% suitability. The first category, with suitability greater than 70%, covers 27.2% of the Aswan Governorate in the north, on both sides of the Nile. Areas with medium spatial suitability, ranging from 50% to 70%, constitute the second category, accounting for 40.3% of the Aswan Governorate, with a concentration in the central regions. The third category includes areas with low spatial suitability, at less than 50%, which make up 32.5% of the governorate.

Data-driven landfill suitability mapping in Lagos State using GIS-based multi-criteria decision making

AbstractCareful selection of landfill sites is essential because improper dumping of wastes can negatively impact health and degrade the environment. Therefore, this research presents a Geographic Information System based—Fuzzy Analytic Hierarchy Process multicriteria decision-making approach for landfill zonation in Lagos State, Nigeria. Due to the rapid urbanisation leading to urban expansion and conversion of the landfills to built-up areas in Lagos State, the functioning landfills have been reduced. After a comprehensive literature review, this study considers nine factors: slope, elevation, land use and land cover, lithology, soil type, Normalised Difference Vegetation Index, the distances to roads, distance to settlements, and distance to water bodies. From the decision matrix, the distance to water bodies, distance to roads, distance to settlements, and land use and land (LULC) cover were ranked with percentage weights of 22%, 19%, 17% and 11%, respectively. Afterwards, potential landfill sites were mapped and classified into five classes: very low (626.48 km2, 16.66%), low (1277.56 km2, 33.97%), moderate (1227.97 km2, 32.65%), high (500.52 km2, 13.31%), and very high (128.13 km2, 3.41%). The low and moderate suitability classes have the highest areal coverage due to the state's increased population and urbanisation. A large percentage of the high to very high suitability classes are located in Epe, Ikorodu, and Ibeju-Lekki local government areas (LGAs) which have lower urbanisation levels compared to most of the other LGAs. Therefore, governments and stakeholders should explore these areas for siting of landfills.

Prioritizing Sustainable Denim Fabric through Integrated Decision-Making Framework.

In this study, a combined multi-criteria decision-making (MCDM) approach that integrates the logarithm methodology of additive weights (LMAW) and the double normalization-based multiple aggregation (DNMA) methods has been utilized to determine the optimal fabric structures considering the performance characteristics of denim fabrics containing recycled cotton. This approach focuses on sustainability and performance criteria, applying advanced decision-making methodologies to provide in-depth analysis and guidance for denim fabric selection. In this research, 15 distinct criteria were taken into account. Alternatives were ranked based on outcomes obtained from these methods. Although it was not anticipated that the top-ranked alternatives would simultaneously fulfill the beneficial or non-beneficial orientation of all criteria, an examination of the top three alternatives (A12, A5, and A15) for both garment groups revealed that they indeed aligned with the pre-determined criterion orientation. This highlights the effectiveness of the multi-criteria decision-making approach in the context of this study.

Enhancing Decision-Making Processes in the Complex Landscape of the Taiwanese Electronics Manufacturing Industry through a Fuzzy MCDM Approach

This research proposes a hybrid multi-criteria decision-making (MCDM) framework for workforce recruitment in Taiwan’s electronics manufacturing companies, an area with limited research. First, a comprehensive review of existing literature and interviews with industry experts were conducted to compile a list of decision-making criteria and sub-criteria relevant to workforce selection in Taiwan’s electronics industry. The Fuzzy Delphi Method (FDM) was then applied to identify and retain the most critical criteria while eliminating the less important ones. Next, Interpretive Structural Modelling (ISM) was used to calculate the interdependencies among the identified factors. Finally, based on these relationships, the Fuzzy Analytic Network Process (FANP) was employed to calculate the relative importance weights of the criteria and sub-criteria. These weights were then used to rank the criteria, identifying the most important ones and aiding in decision-making. The findings indicate that the proposed method provides a structured and assessable model for making informed decisions in workforce recruitment, particularly in the challenging environment of Taiwan’s electronics manufacturing industry, which faces a shortage of skilled labor. The research presents three primary contributions: the development of a systematic criteria selection technique using FDM, the establishment of consistent criteria relations for decision-makers using ISM, and the proposal of an application model employing the FANP method to identify and rank appropriate criteria for hiring new employees. The study highlights work attitude, adaptability to the environment, and work ability as major criteria. It also emphasizes the importance of discipline compliance, a positive attitude, and adherence to health and safety protocols as the top sub-criteria for workforce selection.

Application of multi-criteria group decision-making for water quality management.

As waste discharge into numerous river systems escalates, the pollution of water bodies typically rises. Given the limited capacity of rivers to withstand pollution and their constrained self-cleaning capabilities, treated pollutants from waste discharge must be released into the river. Despite numerous models and algorithms proposed for managing river water quality to meet standards, literature, to our awareness, lacks the utilization of a comprehensive multi-criteria group decision-making approach for water quality management, particularly in river systems. Therefore, this research introduces a new, comprehensive multi-criteria group decision-making for the management of water quality in the Haraz River basin, located in Iran. To do so, the water quality of the basin, a one-dimensional water quality model, QUAL2Kw, was employed to simulate and calibrate the water quality along the river. The simulation results revealed that the downstream water quality violates the water quality standards. To mitigate this issue, various scenarios for waste load allocation (WLA) were evaluated, including no wastewater treatment, primary wastewater treatment, advanced secondary wastewater treatment utilizing the activated sludge (AS) method, and advanced wastewater treatment via the membrane bioreactor (MBR) method. Utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Fuzzy TOPSIS group decision-making model, it was determined that the optimal solution was the implementation of secondary wastewater treatment utilizing the activated sludge method for the 11 PS of pollution, while still adhering to Iranian water quality standard. In addition, the findings of the present study indicate that the implementation of primary wastewater treatment, advanced secondary wastewater treatment utilizing AS, and advanced wastewater treatment through MBR within the study area led to a significant enhancement in water quality. This enhancement ranged from 35 to 105% across various scenarios when compared to conditions where no actions were taken to the treatment of water.

Selection of digital fabrication technique in the construction industry—A multi-criteria decision-making approach

Selection of digital fabrication technique in the construction industry—A multi-criteria decision-making approach

Spatial modeling of soil erosion risk: a multi-criteria decision-making (MCDM) approach in the paguyaman watershed, gorontalo, Indonesia

Spatial modeling of soil erosion risk: a multi-criteria decision-making (MCDM) approach in the paguyaman watershed, gorontalo, Indonesia

Selection of an alternate cementitious mortar using ceramic tile dust waste: a hybrid MCDM approach.

Selection of a suitable alternative material from a pool of alternatives with many conflicting criteria becomes a Multi-Criteria Decision Making (MCDM) problem. In the present study, ternary blended mortars were prepared using ceramic tile dust waste (CTD), fly ash (FA), and ground granulated blast furnace slag (GGBFS) as binder components. Crusher dust (CD) was used as a fine aggregate component. Binder to aggregate ratios of 1:3 and 1:1 were prepared considering suitable flow. A total of 16 mortar mixes were cast. These mortars were tested for various conflicting criteria compressive strength, flexural strength, porosity, water absorption, bulk density, thermal conductivity, specific heat, thermal diffusivity, and thermal effusivity whose weightages obtained were 29.09%, 20.08%, 12.77%, 10.60%, 8.74%, 6.74%, 5.54%, 4.47%, and 1.97%, respectively, as per AHP analysis. Later, considering these different criteria and alternate mortars, it was observed that a 1:1 mortar with 20% CTD, 30% FA, and 50% GGBFS (RC20F30G50) is found to be the suitable mortar with the highest relative closeness coefficient of 0.861 and the highest net outranking flow of 0.316 with respect to MCDM techniques: technique for order of preference by similarity to ideal solution (TOPSIS) and preference ranking organization method for enrichment of evaluations (PROMETHEE-II), respectively. The ranking of the mortar in both methods complies with the relative weightages of the criteria and the performance of the mortars with respect to the above criteria.