Multi-criteria Decision-making Approach Research Articles

Implementation of multiobjective decision-making algorithms and image analysis in HPTLC-guided extraction optimization of natural products

A new, efficient, and low-cost approach for monitoring extraction optimization was proposed based on high-performance thin-layer chromatography (HPTLC) coupled with digital image analysis. Since HPTLC produces rich chromatographic signals corresponding to various analytes which may be differently affected by extraction conditions, four multicriteria decision-making (MCDM) techniques were compared for their ability to aggregate multiple chromatographic responses: Derringer's desirability approach, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE-2), and the Sum of ranking differences (SRD). Ultrasound-assisted extraction (UAE) of green tea leaves with ethanol-water mixtures was used as a model system. The amount of ethanol and extraction time were varied according to the central composite design. Ranking eleven extracts by Derringer's desirability approach, TOPSIS, and PROMETHEE-2 showed the same results. SRD analysis yielded slightly different results from previous methods. Response surface models (RSM) based on the previous three MCDM approaches demonstrated that extraction conditions with moderate amounts of ethanol (73%) and extraction times (46 min) lead to optimal chromatographic profiles. RSM optimization performed on individual peaks, tentatively corresponding to rutin, chlorophyll, and gallic acid, led to different results, which justified the use of MCDM algorithms for aggregation of multiple responses. Aside from natural products, the proposed approach has the potential to be implemented in various extraction optimizations.

Multi-objective model for designing hydrogen refueling station powered using on-grid photovoltaic-wind system

This study formulated a multi-objective model to size a sustainable hydrogen refueling station energized by integrated Photovoltaic-wind system connected to grid. The model takes into account practical constraints alongside three objective functions: maximization of renewable energy fraction, minimization of overall costs, and minimization of greenhouse gas emissions. An exact algorithm is employed to obtain the best solution, and a multi-criteria decision-making approach is utilized to choose the optimal solution from Pareto-optimal solutions. The performance of the model is validated on a hydrogen station that fulfills the daily hydrogen requirements for a fleet of 100 taxis in Tabuk city, Saudi Arabia. Different scenarios are analyzed to demonstrate the trade-offs between the three objectives. For example, the first configuration consists of 6527 photovoltaic panels and 296 wind turbines with capacities of 2.219 MW and 0.888 MW, respectively. The energy deficit is covered by the grid and the excess is exported to the grid. The economic assessment of this configuration reveals a hydrogen cost of 7.53 $/kg. Additionally, this study considered a scenario of powering the station using off-grid Photovoltaic-wind battery system. This analysis explores these differences, focusing on cost-effectiveness and environmental implications of using renewable energy for hydrogen production.

Literature Review on Multi-Criteria Analysis and Application in Education Environment

Due to the increasing complexity of educational data, the use of decision analysis techniques such as Multi-Criteria Decision Making (MCDM) models has become more popular in the education system in recent years. Multi criteria decision making methods provide data analysis methods faster and more efficient for revealing unhidden patterns and other meaningful information from vast educational data those conventional analytics are unable to discover in a reasonable amount of time. Particularly, MCDM techniques have been demonstrated to be effective methods for pattern recognition in educational systems. Motivated by this consideration, the purpose of this paper is to investigate the MCDM approaches applied to education systems through a review of new architectures, applications, and educational trends. The primary objective of this paper is to provide extensive insight into the application of MCDM models to education solutions in order to bridge the gap between MCDM techniques and human-based education interpretability. Then, the application of MCDM to various aspects of education are categorized. Finally, we present the current open challenges and future directions.

Approach Based on the Ordered Fuzzy Decision Making System Dedicated to Supplier Evaluation in Supply Chain Management.

The selection of suppliers represents a pivotal aspect of supply chain management and has a considerable impact on the success and competitiveness of the organization in question. The selection of a suitable supplier is a multi-criteria decision making (MCDM) problem based on a number of qualitative, quantitative, and even conflicting criteria. The aim of this paper is to propose a novel MCDM approach dedicated to the supplier evaluation problem using an ordered fuzzy decision making system. This study uses a fuzzy inference system based on IF-THEN rules with ordered fuzzy numbers (OFNs). The approach employs the concept of OFNs to account for potential uncertainty and subjectivity in the decision making process, and it also takes into account the trends of changes in assessment values and entropy in the final supplier evaluation. This paper's principal contribution is the development of a knowledge base and the demonstration of its application in an ordered fuzzy expert system for multi-criteria supplier evaluation in a dynamic and uncertain environment. The proposed system takes into account the dynamic changes in the value of assessment parameters in the overall supplier assessment, allowing for the differentiation of suppliers based on current and historical data. The utilization of OFNs in a fuzzy model then allows for a reduction in the complexity of the knowledge base in comparison to a classical fuzzy system and makes it more accessible to users, as it requires only basic arithmetic operations in the inference process. This paper presents a comprehensive framework for the assessment of suppliers against a range of criteria, including local hiring, completeness, and defect factors. Furthermore, the potential to integrate sustainability and ESG (environmental, social, and corporate governance) criteria in the assessment process adds value to the decision making framework by adapting to current trends in supply chain management.

Flood Vulnerability Mapping of the Kosi River Basin using a Multi-Criteria Decision-Making Approach

Flood Vulnerability Mapping of the Kosi River Basin using a Multi-Criteria Decision-Making Approach

Biweight midcorrelation-based CRITIC method: a case study for electrification of BIPV in Iran

Criteria weights are essential in various multi-criteria decision-making (MCDM) approaches. The amount of weight indicates the criterion's importance, and the performance of MCDM techniques depends on the correct estimation of criteria weights and criteria amount in the decision matrix. The distance correlation-based CRiteria Importance Through Inter-criteria Correlation (CRITIC) technique is a modified version of the CRITIC method. Since Pearson's correlation is sensitive to noisy and outlier data, this paper proposes using the biweight midcorrelation as a robust alternative to Pearson's correlation. The weights obtained from the proposed biweight midcorrelation method make using the full range of MCDM techniques possible. This study examined the performance of the proposed method in comparison with eight criteria weighting techniques on the electric supply for a Building-Integrated PhotoVoltaic (BIPV) problem. Results from three different tests (i.e. distance correlation, Spearman rank-order correlation, and symmetric mean absolute percentage error (sMAPE)) indicated that the proposed method could produce more valid criteria weights and ranks than other methods. The value of the sMAPE criterion for the proposed method is 0.1096. Sensitivity analysis also revealed that the proposed method provides more robust criteria weights and ranks with a larger decision matrix.

Identification of optimal waste-to-energy strategies for sustainable development in Iran: SWOT analysis, hybrid MCDM methods, and game theory

The growing global focus on environmental sustainability and the pressing need for efficient waste management and energy crisis mitigation have prompted extensive investigations into sustainable waste management strategies. Given Iran's economic development and population growth, the country faces challenges in energy supply and waste management, making waste-to-energy (WtE) applications a promising solution for sustainable waste management. This study employs a combination of SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis, Multi-Criteria Decision-Making (MCDM) approaches, and game theory to explore WtE technologies and processes, evaluate their applicability within Iran's context, and identify the most effective WtE strategies for the country. Furthermore, game theory and fuzzy Shapley value analysis were utilized to determine the optimal combination of WtE development strategies. The strategy player “SO” held the highest position of power, with a fuzzy Shapley value of (0.391, 1.744, 3.264), resulting in the identification of the optimal strategy combination of SO2ST2WO1WT2, with a value of (0.429, 0.679, 0.893). The recommended combination emphasizes landfill with gas capture, development of incineration power plants, and integrated resource recovery through residue-derived fuel (RDF) implementation. Achieving these objectives necessitates energy policy reforms, pricing adjustments, improvements in the energy subsidy system, and infrastructure and market development.

Accelerating depression intervention: identifying critical psychological factors using MCDM-MOORA technique for early therapy initiation

BackgroundA thorough psychosocial assessment is time-consuming, often requiring multiple sessions to uncover the psychological factors contributing to mental illness, such as depression. The duration varies depending on the severity of the patient’s condition and how effectively the psychotherapist can establish rapport. However, prolonged assessment periods pose a significant risk of patient deterioration.MethodsThe comprehensive psychosocial intervention, led by the Multi-Criteria Decision-Making (MCDM) approach utilizing the Multi-Objective Optimization by Ratio Analysis (MOORA) method, played a pivotal role in identifying the key psychological factors contributing to the depression of the client among the 21 factors specified by BDI-II analysis.ResultsThe integration of the MOORA strategy compared to traditional psychotherapy on 254 samples demonstrates a Jaccard similarity coefficient of 0.8, with a minimum error margin of 7% (vulnerability index = 0.57), indicating a significant agreement between the two approaches, both converging towards a similar solution. For patients with extreme depression, the number of sessions reduced from 18 ± 2 to 11 ± 2, showing a 33–35% reduction (χ2 = 6.94, p = 0.008). Severe depression patients experienced a reduction from 14 ± 2 to 8 ± 1 sessions i.e., 34–39% reduction (χ2 = 8.32, p = 0.004). Moderate depression patients saw sessions drop from 9 ± 1 to 5 ± 1, i.e., 37–43% reduction (χ2 = 0.29, p = 0.001). The accuracy for detecting dominant psychological factors improved to 82.88% for extreme, 86.74% for severe, and 90.34% for moderate depression, respectively. ConclusionThe implementation of MOORA facilitated the identification and prioritization of key psychosocial intervention strategies, making the process significantly faster compared to traditional methods. This acceleration greatly enhanced the precision and efficacy of the work. Additionally, critical vulnerable factors were identified through ordered statistics and correlation analysis [Pearson (r) = 0.8929 and Spearman’s rank (ρ) = 0.7551] on the Beck Depression Inventory-II model. These findings were supported by other MCDM schemes such as EDAS and TOPSIS, demonstrating high stability and robustness in dynamic decision-making environments, maintaining consistency across scenarios adapted by different psychotherapists. Overall, the combined application of MCDM (MOORA) and targeted psychological interventions yielded substantial positive outcomes in enhancing the well-being of individuals with psychological illnesses, such as depression, cognitive, affective, and somatic syndromes.

Smart supply chain collaboration maturity evaluation model based on a q-Rung orthopair fuzzy decision making methodology

In today’s IT-intensive business context, evaluating supply chain (SC) collaboration maturity (CM) is crucial for guiding enterprises toward integrated processes that collaborate across enterprise boundaries. This study develops an SC CM evaluation model by integrating a conceptual maturity model with a q-Rung Orthopair Fuzzy Set (q-ROFs)-based decision-making methodology, including a new scoring method. The major aim of this study is to facilitate a qualitative multi-criteria evaluation of SC CM. The conceptual model develops criteria sets based on smart technologies (such as sensors, robotics, cloud, ERP) and business processes. The proposed methodology is applied to 10 generic case companies, demonstrating its ability to provide multi-criteria scorecards and comparative rankings within each maturity level. The results show that the suggested approach can be used to guide the future efforts of individual enterprises, serving for benchmarking purposes across multiple companies by providing an overall objective evaluation for managers and policymakers. The proposed methodology is original by: (a) conceptual model development, (b) developing a multi-criteria quantitative decision-making approach with a new scoring method and integrating it with unique process-based and IT-based criteria sets of the conceptual maturity model, and (c) facilitating managerial scorecards and evaluations.

An updated multi-criteria decision-making method for the sustainable renovation of buildings including environmental, economic and social life-cycle metrics

The ambitious targets of carbon emission reduction set worldwide for the coming years entail the commitment of structural engineers towards the sustainable renovation of existing buildings. The life cycle thinking (LCT) approach offers a holistic perspective for the design of sustainable retrofitting strategies. It envisages the reduction of the environmental impact at all building life cycle phases, while also addressing economic and social issues. Multi-criteria decision-making (MCDM) approaches support the choice of optimal retrofitting solutions, thus being relevant tools for boosting the actual adoption of LCT in current practices. In this study, a previously proposed MCDM approach is enriched with additional LCT-inspired decision parameters (module D beyond-life impacts, environmental payback period, invasiveness), and is then scrutinised and discussed through its application to a case-study building. Four integrated retrofitting scenarios are investigated using state-of-art seismic loss and energy performance assessment methods, including four different types of exoskeletons, made of timber, steel, and concrete. The end-to-end integrated assessment shows that the inclusion of additional LCT-inspired decision criteria effectively favours LCT-based retrofitting interventions over more traditional ones.

Analysis of the ‘Good’ performance indicators of Non-Governmental Development Organizations

The current economic and political crisis has brought about a change in the environment in which Non-Governmental Development Organizations (NGDOs) have traditionally operated. This change can be summed up as a reduction in the funds received accompanied by an increase in the population they must serve. It is thus important to possess mechanisms that allow an analysis of the excellent work performed by NGDOs. A knowledge of the NGDOs’ efficiency in managing previous projects can contribute to improving their future achievements. This research aims to establish some objective indicators that permit an evaluation of the efficiency of these organizations. Firstly, a detailed analysis of the Spanish Agency for International Development Cooperation (AECID) regulation is conducted. This allows us to synthesise the indicators of a good performance by an NGDO based on a study of the eligibility criteria of public donors. The study concludes that the internal good practices criteria of the NGDOs of the AECID can be grouped into four criteria: experience, transparency and internal operations, human resources, and financial resources. Subsequently, a global performance measure of the NGDOs is built using a multi-criteria decision-making approach that minimises the distance from an ideal point whilst maximising the distance from an anti-ideal point. Our approach has been applied in the evaluation of 20 Spanish NGDOs.

New forest fire assessment model based on artificial neural network and analytic hierarchy process or fuzzy-analytic hierarchy process methodology for fire vulnerability map

Forest fires significantly disrupt global ecosystems. Many forecasting techniques predict fire activity and allocate prevention resources, but various factors are missing from the assessments, and multi-criteria decision approaches alone are insufficient. This work introduced a novel methodology combining artificial neural networks (ANN) with the analytical hierarchy process (AHP), fuzzy AHP multi-criteria methods, and spatial data to detect potential forest fire vulnerability areas using twenty variables. The results from AHP or fuzzy AHP, were processed using a multilayer perceptron with a backpropagation algorithm. The final ANN model has two objectives: first, to create a forest fire vulnerability with four classes (low, moderate, high, and very high), and second, to classify burned area sizes in regions highly or very highly vulnerable to fire. Evaluation metrics were also applied for validation. The fire model was tested using both literature review data and in situ observations. Comparative analysis showed that the burned area size model performed better than other machine learning methods, achieving an accuracy score of 89%. Meanwhile, the fire vulnerability model scored 82%. The study addresses the problem of prediction and provides an algorithm for classifying fire risk based on historical data in the Czech Republic, offering a master model for future forest management. Therefore, the ANN model, once trained, validated, and tested, does not require resetting and is effective for estimating forest fire vulnerability. Moreover, it produces results quickly, providing rapid insights into complex forest ecosystems, saving time and enhancing understanding for decision-makers.

Enhancing supply chain performance by integrating knowledge management and lean, agile, resilient, and green paradigms

Due to global market competition, there is an increasing emphasis on utilizing lean, agile, resilient, and green (LARG) paradigms to improve Supply Chain (SC) performance. Furthermore, as technology advances, Knowledge Management (KM) is essential in improving SC performance through information sharing, improving processes, and increasing coordination and collaboration. Thus, this research proposes a novel approach to integrating KM with LARG paradigms to enhance SC performance. Also, the decision framework is presented using Multi-Criteria Decision-Making (MCDM) approaches, including the Grey Step-wise Weight Assessment Ratio Analysis (SWARA-G) and Grey Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS-G) method. The presented approach is implemented in an automobile enterprise to improve SC performance. The findings indicated that building coordination and collaboration to improve efficiency, facilitate operations, and reduce communication and exchange costs was introduced as the most critical strategy in the automotive industry.

Evaluation of Renewable Energy Sources for a Sustainable Future: A Multi-Criteria Decision-Making Approach

The urgent global challenges of climate change, energy security, and environmental degradation highlight the need for sustainable energy solutions. Renewable energy sources (RES) present a viable pathway towards sustainability by mitigating greenhouse gas emissions, reducing reliance on fossil fuels, and fostering economic resilience. Purpose: The purpose of this paper is to propose an advanced Multi-Criteria Decision-Making (MCDM) approach to evaluate various RES by integrating environmental, economic, technological, social acceptance, and resource availability criteria, to identify the most suitable RES for sustainable energy solutions. Methodology: The study employs a hybrid method combining Type-2 Neutrosophic Numbers (T2NN) with LOPCOW (Logarithmic Percentage Change Operator Weighting) and MAIRCA (Multi-Attributive Ideal-Real Comparative Assessment) to rank the suitability of different RES, including solar, wind, hydropower, and geothermal energy. Findings: The case study results reveal wind energy as the top-ranked alternative, supported by consistent findings across comparative methods such as COPRAS, MABAC, EDAS, and TOPSIS. Sensitivity analysis further confirms the stability of the proposed model under various scenarios. Originality: The originality of this study lies in the integration of T2NN, LOPCOW, and MAIRCA to address the limitations of traditional MCDM approaches in handling uncertainty and imprecision in data. The study demonstrates the efficacy of the proposed framework in providing a robust evaluation of RES, and its value lies in its potential to inform decision-making in the field of sustainable energy solutions.

Strategic deployment of GIS-optimized solar charging stations for electric vehicles: A multi-criteria decision-making approach

Climate change and the rise in carbon dioxide levels due to gasoline vehicles are global challenges that require innovative and sustainable solutions; this study presents an innovative strategy to promote electric vehicles (EVs) adoption through the establishment of solar-powered charging stations. Utilizing ArcGIS10.8.2 software, a comprehensive analysis was conducted to identify optimal locations for these stations, considering technical, economic, environmental, and geological data. This research, unlike other past research, leverages public spaces such as gas stations, shopping centers, and parking lots for station construction, minimizing operational costs and fostering a sustainable infrastructure. This approach applies not only to the study area of this research, which is Khuzestan province, but can also be extended to other regions and serves as a model for reducing CO2 emissions in the transportation sector worldwide. This research evaluates the location for establishing electric vehicle charging stations using solar energy innovatively, from both technical and operational perspectives. By using the systematic and new method presented in this research, it is possible to identify the highest potential for the construction of electric car charging stations that simultaneously use solar energy all over the world, and the results of applying this new method in the studied area showed that 90% of the cities within this province have the potential to establish charging stations for electric vehicles utilizing sunlight. Specifically, Mahshahr County in this province can supply 90.55% of the required energy for charging stations through solar energy. Operationally, 70% of the cities in the region possess such potential. The study demonstrated that this area has the capacity to convert 11% of vehicles to electric cars by 2040 and can reduce CO2 emissions by more than 30 tons.

A novel hybrid spherical fuzzy multi-criteria decision-making approach to select the best hydroelectric power plant source in India

Hydroelectric power plants (HPPs) play a crucial role in India’s energy portfolio by providing renewable energy and decreasing dependency on fossil fuels. HPPs assist in flood control, irrigation, and water management, thereby supporting agricultural and economic advancement. However, persistent challenges such as environmental consequences, habitat depletion, and resettlement complications impede project advancement. Striking a balance between these advantages and addressing social and environmental issues is essential for shaping India’s energy trajectory. In this manuscript to achieve these goals, the research paper incorporates the opinions of three decision-makers along with 20 criteria. Each criterion is associated with 5 alternatives and further categorized as beneficial and non-beneficial. To determine the optimal power plant, a novel hybrid Multi-Criteria Decision Making (MCDM) method is employed within a spherical fuzzy environment to handle the uncertainties occurred in the HPPs. This paper also introduces a novel formula for calculating the weights of decision-makers and a novel score function for converting spherical fuzzy numbers into crisp values. Initially to examine the interrelationship between these criteria the spherical fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) method is utilized. The weight of the criteria is determined using the spherical fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) method, followed by determining the rank of the alternatives using the novel spherical fuzzy Multiple Criteria Ranking by Alternative Trace (MCRAT) method. Subsequently, the alternative with the highest score value is determined as the optimal choice for the hydroelectric power plant source in India. Furthermore, this research paper also discusses sensitivity analysis, comparative analysis, and the managerial implications of this study.

Material Selection and Optimization of Green Drilling of Aluminium Alloys: MCDM-Pareto ANOVA and T-SRC

Abstract The widespread use of aluminium alloys in the automotive, aerospace, and structural sectors necessitates the establishment of exploratory selection criteria to harness optimality at the various stages of product development. This study utilizes multi-criteria decision-making approaches to determine the most suitable aluminium alloy for thermal friction drilling. Additionally, the process optimization was done using the Taguchi super ranking concept (T-SRC), and the confirmation test using explicit dynamics simulation using ANSYS software. The ideal material selection was evaluated based on several factors, including yield strength, tensile strength, thermal conductivity, impact resistance, density, specific heat capacity, coefficient of thermal expansion, and percentage elongation. The best-ranked AA2024-T4 aluminium alloy underwent an integrated Taguchi Design of Experiments and optimization using Pareto ANOVA and subsequently ANSYS Explicit Dynamics process simulation. The MCDM Expert System derived from AHP, and ranking of materials using TOPSIS, EDAS, and VIKOR proved to be an effective material selection strategy from a set of alternatives for the thermal friction drilling process. By combining Multi-Criteria decision making (MCDM) with process simulation, material selection concerns in different industrial processes can be resolved, therefore eliminating the need for laborious tests.

Multi-Criteria Decision Approach for Lightpath Restoration After Resource Crunch

Multi-Criteria Decision Approach for Lightpath Restoration After Resource Crunch

Sustainable Thermoplastic Material Selection for Hybrid Vehicle Battery Packs in the Automotive Industry: A Comparative Multi-Criteria Decision-Making Approach

This research study employs a comparative Multi-Criteria Decision-Making (MCDM) approach to select optimal thermoplastic materials for hybrid vehicle battery packs in the automotive industry, addressing the challenges posed by high-temperature environments. Through a detailed evaluation of materials based on criteria such as thermal stability, mechanical strength, chemical resistance, and environmental impact, the research identifies materials that enhance battery efficiency, longevity, and vehicle performance. Utilizing SWARA-ARAS, SWARA-EDAS, and SWARA-TOPSIS methods, the study systematically assesses and ranks various polymers, providing recommendations that prioritize safety, performance, and sustainability. The findings offer valuable insights for manufacturers in making informed material selection decisions, contributing to the advancement of sustainable automotive technologies. This research not only highlights the importance of material selection in the context of hybrid vehicle battery packs but also sets a foundation for future studies to explore emerging materials and decision-making frameworks, aiming to further enhance the efficiency and sustainability of hybrid vehicles.

Location selection for logistics centre using PROMETHEE method

Logistics and distribution centres are essential to the supply chains of many manufacturing and logistics companies. Efficiently locating logistic centres involves thorough search for optimal place, prioritizing proximity to suppliers and minimizing costs. Companies’ management often solves the location problem of new halls, mostly to minimise the associated costs. Such a problem is solved in the automotive industry as part of the launch of a new international project. The key factor for the decision of where to locate the logistics centre is the location of the suppliers, since material deliveries generate a large part of the project costs. The aim of this study is to define a methodology for the location of the logistics centre, considering several alternative locations and relevant criteria. The location alternatives and criteria are defined in terms of minimising project costs and sustainability elements. The problem is solved using the multi-criteria decision-making approach. First, the AHP method is used to assign weights to the criteria. Then the PROMETHEE method is applied to find a suitable location for the logistics centre and to perform a thorough sensitivity analysis. The sensitivity analysis is focused on the impact of values of weights on the solution. Consequently, the analysis proves the correctness of the selected alternative. Based on the case study, a general methodology for locating a logistics centre is proposed.