Multi-criteria Decision Research Articles

Mapping development potential and priority zones for utility-scale photovoltaic on the Qinghai-Tibet Plateau

Sustainable renewable energy is a critical goal of the 2030 Sustainable Development Agenda. The scientific and rational development of solar power in the Qinghai-Tibet Plateau (QTP) is vital for China's carbon peak and carbon neutrality goals. However, more accurate, high spatial resolution assessments are needed to evaluate the utility-scale photovoltaic (PV) development potential on the QTP. To address this, we integrated geographical and technical potentials into a unified framework for assessing PV development potential. Using the spatial multi-criteria decision analysis technique, supported by the Bayesian Best-Worst method, we generated a 30-m suitability map for assessing geographical and technical potentials, along with priority zones. We found that approximately 35.22 % of the study area is suitable for utility-scale PV development, mainly concentrated in the Qaidam Basin in Qinghai and the northern Tibet Plateau in Xizang. The estimated PV technical potential for highly suitable and suitable classes is approximately 42.34 million GWh/a, which is equivalent to reduction of approximately 31.43 billion t/a of carbon emissions. We also formulated four priority development strategies using a spatially explicit model to meet the urgent 2030 target for utility-scale PV installations. Moreover, validation of existing PV projects and sensitivity analysis were conducted, confirming the accuracy and robustness of the results. This study provides practical guidance for informed decision-making by the government, investors, and stakeholders.

Interval-valued picture fuzzy decision-making framework with partitioned maclaurin symmetric mean aggregation operators

Interval-valued picture fuzzy (IVPF) set is an extension of the picture fuzzy set theory used to represent uncertainty and vagueness in the processes of decision-making. This study focuses on exploring the interrelationships among multiple IVPFSs and criteria partitions. We investigate the IVPF partitioned Maclaurin symmetric mean operator and the weighted IVPF partitioned Maclaurin symmetric mean operator and discuss their respective properties. Subsequently, we identify certain special cases of these operators based on IVPF sets. Furthermore, we deploy a multi-criteria decision-making procedure utilizing the suggested IVPF partition operators. Through a numerical example, we demonstrate the practicality and validity of the presented approach. Finally, a thorough comparison with existing approaches is conducted to elucidate the superiority of the proposed method.

Review of The Financial Health of Bank During Increasing Foreclosed Assets and The Application of Analytic Hierarchy Process (AHP) in Selecting Strategies to Reduce Them

PT Bank Laju International Tbk (the Bank) has struggled with its increased foreclosed assets, particularly those originating from mortgage loans, which could affect its financial health. The research assessed Bank’s financial health using Risk Based Bank Rating (RBBR) framework by analysing key financial ratios and investigating the root causes of rising foreclosed assets through Fishbone and Pareto analysis. Key factors include customer income instability, slow progress in selling foreclosed assets, and lack of insurance protection during economic downturns. To select effective strategies for managing foreclosed assets, the research utilized Analytic Hierarchy Process (AHP), a Multi-Criteria Decision-Making method, to evaluate multiple alternatives based on various criteria, which were derived from Focus Group Discussion (FGD) with Bank’s officers, who directly involved in mortgage loans and foreclosed assets activities, also reference with Bank’s SWOT analysis. The research evaluated three alternatives strategy: Protection Insurance for Customer’s Mortgage Loans. Actively Partnering with property agents to accelerate sale of foreclosed assets as-is. Collaborating with property agents to renovate foreclosed assets. The result showed that despite the increase in foreclosed assets, the Bank remains financially sound, with key financial ratios within acceptable regulatory limits. AHP analysis revealed that alternative 1 stands in first place to be prioritized, followed by alternative 2, however between both alternatives indicates a very small difference in prioritization weight, which means that both strategies are important to be executed and prioritized. Proposal to implement both alternatives in parallel would be a very effective strategy, where alternative 1 conduct as Preventive Strategy in preventing customer’s mortgage loans defaults, while alternative 2 as Corrective Strategy in lowering foreclosed assets volume. Alternative 3 acts as a complement strategy, where renovating foreclosed assets could facilitate quicker sales, however it’ll take longer time for renovation itself.

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.

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.

Application of a multi-criteria decision technique to assignation of acoustic classes

Acoustic performance evaluation of buildings prior to the acoustic classification, requires consideration of a number of factors with the corresponding quantitative criteria to be satisfied. It is of question how much the deviations can be compensated and whether all performance factors are equally important. This paper presents the results of a study on assigning priorities for each acoustic factor by using the analytic hierarchy process (AHP) - a multi-criteria decision technique - based on the expert evaluations. Eleven acoustic consultants rated seven acoustic factors, i.e. regulatory criteria of sound insulation, sound level and reverberation time, in pairwise comparisons. The data were aggregated after a consistency check and weights of each acoustic factor were calculated as well as weights of six acoustic performance classes ranging from A to F. The outcomes of this study resulted in a decision matrix, named as "acoustic scorecard" which can be used to obtain a single-number rating index for acoustic quality of buildings.

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.

A hybrid-fuzzy-decision-making framework for digital technologies selection

In the era of Industry X.0, manufacturers are increasingly adopting digital technologies such as robotics, big data, mobile communications, and information technology to enhance productivity and profitability. However, navigating this digital revolution presents significant challenges, particularly in selecting the most suitable technologies amidst uncertainties and the lack of precise quantitative data. Fuzzy logic, initially introduced to manage vagueness and imprecision, has proven to be an essential tool in various domains for handling these uncertainties. However, as systems and environments become more complex, traditional fuzzy logic exhibits limitations in effectively representing and managing deeper levels of uncertainty. This realization has driven the development of several extensions to fuzzy logic, designed to address challenges associated with uncertainty representation. Yet, a central question remains: Which fuzzy logic extension is the most relevant for better dealing with uncertainty? This study introduces a hybrid-fuzzy decision-making framework for technology selection, which allows for more than one representation of fuzzy information. Using a multi-expert, multi-criteria decision-making (MCDM) strategy, the study seeks to support organizations in selecting the most suitable digital technology for their operations, particularly when precise quantitative data is unavailable, and decisions must rely on expert opinions. The suggested framework is designed to handle decision-makers' hesitancy, subjectivity, and ambiguity in the decision-making process. To showcase the practical application of the proposed methodology, a case study was conducted to select appropriate technologies in the Moroccan automotive sector. The results demonstrate that, regardless of the fuzzy logic extension used, digital automation and automatic identification consistently rank among the top technologies, underscoring their importance according to most experts. Our proposed approach is particularly effective in resolving conflicts between different fuzzy logic extensions, thereby facilitating the ranking of the remaining technologies. Notably, additive manufacturing ranked third, followed by the other technologies.

A HYBRID MEREC-TOPSIS APPROACH FOR THE IMPROVEMENT OF PHYSICAL VAPOUR DEPOSITED TITANIUM CARBO-NITRIDE COATING

Titanium carbo-nitride (TiCN) comes under the metal nitride group which can provide a combined property of titanium carbide (TiC) and titanium nitride (TiN) coating. TiCN Coating possess an excellent mechanical and tribological, properties which leads its application in many industries particularly in the Automotive and aerospace industries. TiCN films can be very hard and strong based on the composition and synthesis process. Physical Vapor Deposition (PVD) technique is widely used for the preparation of thin film coating because of its ability to precisely control the composition and thickness of the coatings. The properties of the developed coating significantly affect by the PVD process parameters and their levels. Multi-Criteria Decision Making (MCDM) methods are widely used in many sectors for the selection of process parameters based upon some specific criteria. In the present work TiCN coatings were synthesized by using one of the most widely used PVD method called magnetron sputtering. L16 orthogonal array was used as a design of experiment for the experimental work by varying the sputtering process parameters like bias voltage, N2 flow rate and substrate to target distance. After the synthesis, the developed films were tested using atomic force microscopy (AFM), Scanning electron microscopy (SEM), and Nanoindentation. From the characterization three response parameters such as hardness (H), Modulus of elasticity (E) and surface roughness (Ra) of the coating has been considered. A hybrid method based on improved removal effects of criteria- technique for order performance by similarity to ideal solution (MEREC-TOPSIS) approach has been considered for the for the process parameters selection. The results have also been compared with other weight calculation techniques. In all the cases it is observed that experiment no 16 and 15 have got the 1st and second rank respectively. However, experiment no 1 is the last rank in all the cases. From the correlation analysis it is found a strong positive correlation between MEREC-TOPSIS and other methods. The method provides a significant advancement in the selection of optimal parameters, ensuring enhanced coating properties crucial for industrial applications.

Multicriteria risk assessment of water inrush in underground mines with large-scale curtain grouting: a mine disaster risk reduction strategy

This paper presents a synthetic method to assess the risk of mine water inrush before and after large scale curtain grouting based on GIS platform. Firstly, nine factors were selected from three aspects: geological conditions, hydrogeological conditions and curtain grouting parameters, to construct a multi factor assessment model. The combined weight of every factor was determined using the minimum deviation comprehensive weighting method, which can reduce the polarization effect of various assessment methods in multicriteria decision. Then, the effectiveness of the synthetic method was validated through the Maoping lead–zinc mine in Southwest China. The mining area was divided into five zones, namely safe zone, relatively safe zone, transitional zone, relatively dangerous zone, and dangerous zone. The assessment results show that the relatively dangerous and dangerous zones were significantly reduced, while the safe, relatively safe and transitional zones were significantly increased after large scale curtain grouting. Finally, we analyze the impact of curtain grouting on the risk of mine water inrush and the mechanism of zoning evolution from three aspects: water source control, pathway sealing, drainage, and pressure reduction. This work provides an effective strategy for mine disaster risk reduction.

Decision-making strategies for organizational processes

This research aims to explore decision-making strategies in organizational processes and their relevance in today's business environment, using a comprehensive methodology based on a review of the scientific literature. Different approaches and methodologies used for decision making are presented, such as data-driven decision making, multi-criteria decision making, collaborative decision making and decision making based on artificial intelligence and machine learning. In addition, recent applications of these strategies in various organizational contexts, such as manufacturing, logistics and supply chain, project management, and product innovation and development, are discussed.

Managing transit-oriented development: A comparative analysis of expert groups and multi-criteria decision making methods

A key challenge for transport managers and planners in sustainable development is evaluating transit facilities' performance. While Multi-Criteria Decision-Making (MCDM) tools are often used, they can be influenced by experts' subjective biases. This study applies MCDM to assess Mass Rapid Transit (MRT) stations in Jakarta, Indonesia, focusing on Transit-Oriented Development (TOD). The primary goal is to compare stakeholder perspectives and MCDM methods, complemented by a sensitivity analysis and validation with real-world smart card data. The findings reveal significant differences in criteria weighting between Indonesian and non-Indonesian experts, and between academic and non-academic experts, especially in transit connectivity and land use diversity. The study also shows variations in station rankings across different MCDM methods. Sensitivity analysis identifies transit connectivity as the most critical criterion. Simple Additive Weighting (SAW) with linear normalisation aligns well with actual usage data and shows robustness in sensitivity analysis, making it the most reliable method for TOD evaluation. The study highlights the need for continuous TOD performance monitoring and the regular collection of real-world data on ridership and TOD indicators.

A Novel Multi-criteria Decision Making Approach Based on Bipolar Neutrosophic Set for Evaluating Financial Markets in Egypt

In the evolving landscape of financial markets, assessing company performance with precision and reliability is crucial for investors and analysts. This paper introduces an innovative framework for evaluating the financial performance of companies listed on the Egyptian Exchange (EGX) by integrating Bipolar Neutrosophic Sets (BNS) with Multi-Criteria Decision-Making (MCDM) methods. The proposed framework utilizes BNS to incorporate the inherent uncertainty and imprecision of financial data, and integrates MCDM methods to provide a comprehensive assessment of multiple performance indicators. Specifically, the evaluation focuses on seven critical criteria: Stock Returns, Volatility, Liquidity, Market Capitalization, Earnings Growth, Revenue Growth, and Analyst Ratings. By utilizing Bipolar Neutrosophic Weighted Average (BNWA) operators and similarity measures, the study effectively ranks companies through a nuanced analysis of these criteria. The results highlight the effectiveness of the proposed approach in providing a robust and accurate financial performance evaluation framework tailored to the unique dynamics of the Egyptian market. The study demonstrates that the hybrid model not only accommodates variations in criteria weights but also offers valuable insights for investment decisions in emerging markets. The approach exhibits resilience and adaptability, providing a more informed and reliable basis for evaluating financial performance. This research contributes to the advancement of financial performance assessment methodologies by showcasing the potential of combining BNS with MCDM techniques. The research also emphasizes the framework's practical applications and suggests future research directions for refining the model and exploring its use in diverse financial contexts.

Holistic Methodology of Hypersoft in Tandem with Uncertainty-based Triangular Neutrosophic for Wave Energy Converter Optimization

Global warming, environmental degradation, and climate change have recently emerged as compelling arguments against the combustion of fossil fuels. As well Eliminating and forgoing the intake of fossil fuels is also encouraged by the Sustainable Development Agenda's goals. The healthy substitute for fossil fuels, renewable energy is one of the main devoid power sources found in our natural surroundings. An effective proposition for harnessing wave energy with an emphasis on increasing energy and efficiency are wave energy converters, or WEnCos. It contributes to producing a clean, limitless energy source that might drastically lessen our need for fossil fuels. Generally speaking, the selecting of the appropriate converter is an important endeavor that this study takes on. The process of selecting the optimal converter has been conducted based on effective benchmarks or criteria and its attributes. Multiple Criteria Decision Making (MCDM) techniques seem to be effective tools when it comes to handling the complex issue of benchmarking WEnCos. These approaches consider a variety of criteria, allowing for a comprehensive and nuanced examination. Each utilized technique plays vital role in our decision problem. Hence, CRiteria Importance Through Inter-criteria Correlation (CRITIC) method—which determines the weights of criteria and its attributes in decision making problems. These weights are utilized in Weighted Sum Product (WISP) method. Totally, these techniques are implemented under uncertainty theory of Neutrosophic which combined with soft sets notion to generate Triangular Neutrosophic HyperSoft (TrNSHSS). Totally, all these techniques are contributed to constructing soft decision-making model for analyzing and recommending optimal WEnCo.

A Comprehensive Analysis of Apricot Drying Methods via Multi‐Criteria Decision Making Techniques

ABSTRACTFood and food safety have been among the most important issues for people throughout history. Societies have always tried to be self‐sufficient in food and have avoided becoming dependent on foreign sources. However, the fact that most foods are seasonal and the increasing population's food consumption have revealed the need to preserve foodstuffs for a long time. The old and well‐known method used today for extending shelf life is the drying process. The drying process is preferred over other preservation methods for reasons such as being more economical, easier to transport, having a longer shelf life, more concentrated nutritional value, and containing fewer additives. This ensures that dried foods are of higher quality in terms of physical, chemical, microbial properties, and nutritional values compared to other packaged foods. While the drying process was traditionally done over a long period, technological advancements have led to the production of higher quality and more valuable commercial products in a shorter time. In this study, traditional and technological methods used in drying apricots were compared according to the parameters determined by experts in the field. Since multiple parameters are effective in the comparison, Multi‐Criteria Decision Making (MCDM) techniques were used. The optimum apricot drying method was determined by combining the results obtained from different MCDM techniques with the Borda rule.

Multi-criteria evaluation of slurry fuels based on coal and organic waste

The applicability of slurry fuels for power generation by direct combustion was assessed using multi-criteria decision making (MCDM). Slurry fuels based on coal slime and water with 5% additive of biomass waste (rice husk, sawdust and sewage sludge) were studied. was experimentally established that the use of biomass waste in the composition of slurry fuel improves the characteristics of sedimentation stability by 2.5–4.2 times, atomization parameters by 1.5–2.0 times and environmental indicators of the combustion process by 1.5–2.4 times. A multi-criteria assessment of the efficiency of using slurry fuels in comparison with widely used solid fossil fuels in thermal power engineering was carried out based on the obtained experimental results. The indicators of the highest relative efficiency for slurry fuels with additives of sewage sludge and rice husk in the temperature range of 700–900 °C are 10–12% higher than for the composition without additives.

Multi-criteria decision making beyond consistency: An alternative to AHP for real-world industrial problems

The Analytic Hierarchy Process (AHP) is a widely used method for multi-criteria decision-making that hinges on the assumption of consistency in pairwise comparisons. However, in real-world scenarios, decision-makers often struggle to provide fully consistent judgments. This article introduces a decision-making framework that operates independently of consistency. Utilizing the Skew-Symmetric Bilinear representation of preferences allows decision-makers to more accurately evaluate alternatives and criteria, making this framework more applicable in practical settings. The proposed method is validated through practical examples and an in-depth case study in the textile industry, effectively resolving a complex decision-making problem related to acquiring a data analytics tool for supplier selection. The results underscore the robustness and adaptability of this consistency-independent technique as a flexible alternative to traditional AHP methods.

POLICY ON THE IMPLEMENTATION OF SMART MOBILITY IN THE SOUTH TANGERANG CITY, INDONESIA BASED ON PUBLIC TRANSPORTATION USING THE PROMETHEE METHOD

The increase in population density and mobility in certain urban areas has underscored the need for a smart mobility concept. As a component of the broader smart city framework, smart mobility aims to enhance transportation services, making them more accessible, safe, comfortable, efficient, and affordable for the public. This research assesses the readiness for implementing public transportation-based smart mobility in South Tangerang and develops a policy strategy model for its transportation planning and development. The study employed a mixed-method approach, incorporating multivariate analysis and Multi-Criteria Decision Analysis. The findings reveal that South Tangerang is currently unprepared to implement smart mobility, as indicated by low scores on its assessment indicators. Recommended policies include improving the availability, security, and comfort of public transportation, reorganizing transit routes, providing real-time information access, adjusting schedules, and promoting bicycle use by adding bicycle lanes. This research highlights the current state of transportation systems in developing countries and emphasizes the importance of implementing policies that benefit a wide segment of the population.

Assessing the Holistic Value of Danhong Injection for Chronic Stable Angina: A Multi-Criteria Decision Analysis in China

IntroductionThis study aimed to assess the holistic value of Danhong injection (DHI) for treating chronic stable angina (CSA) and to identify the therapeutic characteristics of DHI. MethodThe holistic value of DHI was assessed using a well-established evaluation index system including 34 criteria for the post-marketing assessment of the value of Chinese patent medicine (CPM) based on the Multi-criteria decision analysis (MCDA) model. Each criterion was rated by seven experts based on an evidence matrix comprising a literature review on DHI for treating CSA. The overall value of DHI for the treatment of CSA was evaluated by aggregating these performance scores and predetermined weights. Additionally, the contributions of each domain and criterion to the overall value estimate were calculated. Uncertainty was explored using an online retest and intrarater correlation coefficient. ResultsWe found that the comprehensive value estimate of DHI for treating CSA was 80.18 points. The domain of “safety and risk management” was the strongest contributor to the final estimated value (30.37), followed by “effectiveness” (23.75). The criteria “size of population affected by disease” (3.79), “risk control of toxic medicinal materials” (4.38), “comparative non-medical costs” (1.14), “clarity of the mechanism of drug action” (0.6), “sustainability of medicinal materials resources” (1.91) and “patient compliance” (4.59) were found to be the most significant contributors to the overall value estimate across the 6 evaluation domains. The initial scores for DHI over the two rounds showed good consistency across panelists (ICC=0.886 (0.858-0.909)). ConclusionCompared with standard treatment alone, DHI was perceived as being at a “good level” intervention.

A multi-criteria decision analysis framework for evaluating deep learning models in healthcare research

Selecting the appropriate deep learning (DL) model for healthcare research poses a significant challenge due to the diversity of evaluation criteria and the complex nature of health-related tasks, where a single metric like accuracy is often insufficient. Motivated by the need for a structured, multi-criteria approach, this study proposes a Multi-Criteria Decision Analysis (MCDA) framework using the Analytic Hierarchy Process (AHP). Our primary contribution is the development of a comprehensive decision-making framework that integrates multiple evaluation criteria, such as accuracy, sensitivity, specificity, and computational complexity, alongside empirical data from existing literature to systematically compare DL models. The framework was validated through a use case involving the selection of the best DL model for diagnosing COVID-19 using X-ray images, where we compared eight popular models, including ResNet34, SqueezeNet, and AlexNet, and it was also evaluated through comparative scenarios using traditional methods, including weighted sum, weighted average, and accuracy-based evaluation. Quantitative results show that SqueezeNet achieved the highest score in the AHP framework (88.64), while ResNet34 performed best in traditional methods such as weighted sum (588.49) and accuracy ranking (98.33%). A sensitivity analysis further demonstrated the impact of varying criteria weights, showing how changes in the importance of accuracy and precision, influenced model ranking. These findings highlight the flexibility and robustness of the AHP framework in addressing the complexities of model selection in healthcare research. The implications of this work suggest that a structured, data-driven evaluation approach can provide more nuanced and reliable insights compared to traditional methods like single-metric evaluations, ultimately supporting more informed decision-making in healthcare applications.