Multi-criteria Decision Research Articles

Suitable sites for fog water collection in Cape Town, South Africa

The worldwide shortage of fresh water is a critical issue, with two-thirds of the world’s freshwater being inaccessible, as it is locked up in frozen glaciers. Additionally, arid areas such as South Africa are experiencing increased water scarcity, with regions such as Cape Town having already announced “day zero”, the day when the city’s dams will run out of water. Fog water collection provides a sustainable and alternative source of fresh water. Nevertheless, existing methods for identifying suitable locations rely on manual and trial-based approaches. This study focuses on finding suitable locations for fog harvesting in Cape Town via geographic information system (GIS)-based multicriteria decision analysis. To accomplish this, relevant factors for fog harvesting were identified in the literature and then transformed into spatial data layers. Next, weights were assigned to criteria layers via the analytical hierarchy process method, ultimately creating a final suitability map through a weighted overlay of these criteria layers. The findings of this study indicated that regions near coastlines, with low temperatures and strong winds, elevations above 1000 m or below 200 m, and steep slopes facing the ocean are the most favourable locations for harvesting fog water. This approach could be replicated in other regions, but caution is necessary when determining criteria and thresholds because of the localised nature of fog.

GIS-based multi-criteria decision-making for identifying potential artificial groundwater recharge zones in the Tikurwuha watershed of Ethiopia

GIS-based multi-criteria decision-making for identifying potential artificial groundwater recharge zones in the Tikurwuha watershed of Ethiopia

Assessment of options for the location of railway infrastructure facilities

This article discusses a methodology for assessing optimal cargo facility placement, focusing specifically on evaluating regional attractiveness in Uzbekistan through an integrated attractiveness index. By adapting existing methods to fit Uzbekistan's unique context, the proposed approach becomes more universally applicable to other regions as well. The study introduces a new model that accounts for uncertainty and incorporates multicriteria decision-making, enabling stakeholders to select the most suitable alternatives. A hierarchical goal tree, which clearly identifies weighted factors and equivalent criteria, ensures transparency and validity throughout the decision-making process. This method provides numerous benefits for various stakeholders, including administrators and investors, by clearly outlining potential gains and losses associated with placing logistics centers. The article demonstrates practical application by analyzing placement decisions for logistics facilities within Uzbekistan's railway network, applying a comprehensive, multicriteria evaluation approach. The hierarchical model simplifies the selection process by simultaneously considering multiple parameters and selection criteria, ensuring a thorough assessment. Strategic placement of logistics facilities improves regional competitiveness, stimulates economic growth, and creates new employment opportunities. The methodology presented in this study offers valuable insights for future research and practical implementation, demonstrating its relevance in a modern economy and rapidly evolving logistics sector. Ultimately, the proposed model holds significant potential to enhance logistics infrastructure and support Uzbekistan’s broader economic development goals.

An equity-based financial framework for a sustainable healthcare system in Arab countries.

Disparities persist in access, quality and outcomes across different socioeconomic strata. Addressing these disparities requires a comprehensive understanding of the underlying factors contributing to healthcare inequities. Even though healthcare equity has been discussed in the literature, no comprehensive frameworks have been developed considering the given country's distinctive demographic cultural and socioeconomic variables. This study proposed an equity-based financial framework to enhance the sustainability of the healthcare system in Saudi Arabia, the United Arab Emirates (UAE) and Qatar. Moreover, this research aimed to examine the key factors influencing equitable access to healthcare services. A cross-sectional study design was employed, utilizing national health accounts, demographic surveys and health outcomes data from 3 Arab countries: Saudi Arabia, UAE and Qatar. The study included participants from 15 medical organizations, 500 policymakers and 10,000 patients. A stratified random sampling technique was employed to ensure a diverse and representative sample. The economic equity measurements included the principal component analysis (PCA) and Theil index. Financial sustainability was evaluated using techniques such as the cost-effectiveness analysis (CEA) and systems dynamics modeling techniques. This study identified a positive convergence in healthcare systems among Qatar, Saudi Arabia and the UAE. The Theil index value of 0.35 suggested a balanced distribution of healthcare resources across the 3 countries. Policy A had an incremental cost-effectiveness ratio (ICER) of $15,000 per quality-adjusted life year (QALY), making it more cost-effective compared to Policy B with an ICER of $20,000 per QALY and Policy C with an ICER of $25,000 per QALY. The Delphi technique achieved a consensus level of 90%, while Policy C emerged as the most preferred option in the multi-criteria decision analysis (MCDA), scoring a total of 85 points. Moreover, the PCA accounted for 60% of the variations related to healthcare equity in the specified countries. The findings of this study provide valuable insights for policymakers, offering a new roadmap for economic evaluation studies aimed at enhancing healthcare equity and sustainability in Arab countries.

Identification of Multiple Extraction Methods to be Used in Extraction of Macro and Micronutrients of Neutral and Alkaline Soils by a Multi-Criteria Decision-Making Technique (TOPSIS)

Abstract This study aimed to determine the plant-available forms of macronutrients (P, K, Ca, Mg, S) and micronutrients (Fe, Cu, Zn, Mn, B) in neutral and alkaline soils using multiple nutrient extraction methods. It also sought to evaluate the correlations between these methods and routine soil analysis techniques through the TOPSIS multi-criteria decision-making approach. The study focused on identifying the most suitable multi-nutrient extraction method for the soils of the Thrace Region (Turkiye) and aimed to provide a methodological framework for similar research. A total of 130 neutral and alkaline soil samples from the Thrace Region were analyzed using Mehlich-3, ammonium bicarbonate-DTPA (AB-DTPA), modified Morgan (M. Morgan), acid ammonium acetate-EDTA (AAAc-EDTA), and water (W) extraction solutions, alongside routine soil analysis methods. Nutrient concentrations were determined using an ICP-OES device. Correlation coefficients between routine and multi-nutrient extraction methods were calculated, and the TOPSIS technique was applied to rank the extraction methods based on these coefficients. The TOPSIS analysis ranked the multi-nutrient extraction methods for neutral and alkaline soils of the Thrace Region as follows: 1) AB-DTPA, 2) Mehlich-3, 3) AAAc-EDTA, 4) M. Morgan, and 5) Water. The AB-DTPA and Mehlich-3 methods were identified as the most effective multi-nutrient extraction methods for neutral and alkaline soils in the Thrace Region. These methods are recommended for practical use, with their limitations in correlation relationships and analytical precision noted. Further studies are required to address these limitations and enhance the reliability of these methods in diverse soil analysis applications.

Developing a decision support system for sustainable urban planning using machine learning-based scenario modeling

Urbanization is rapidly transforming cities, posing intricate issues for sustainable urban development. Conventional urban planning techniques frequently encounter difficulties in incorporating several variables, including environmental, social, and economic issues. This research presents an innovative decision support system (DSS) aimed at tackling these difficulties through the application of machine learning and fuzzy decision-making methodologies. The approach utilizes random forest recursive feature elimination (RF-RFE) to determine the most significant criterion from a collection of 15 parameters, such as environmental impact, energy efficiency, social equity, and economic viability. The logarithmic percentage change-driven objective weighting (LOPCOW) approach is employed to determine the weights of these criteria according to their importance. The evaluation based on relative utility and nonlinear standardization (ERUNS) method is employed to rank different urban development methods, utilizing q-rung fuzzy sets (q-ROFS) to address uncertainty and imprecision. The analysis indicates that Green Urbanization is the most advantageous option among the assessed alternatives, demonstrating its compatibility with sustainable development objectives. The proposed DSS integrates machine learning-based feature selection with fuzzy multi-criteria decision-making, providing a comprehensive framework for navigating the intricacies of urban planning and facilitating data-driven, sustainable urban development decisions.

Enhancing resilience in CSCs: a SCOR-integrated fermatean fuzzy AHP approach

PurposeThe purpose of this study is to propose a novel framework that utilizes interval-valued fermatean fuzzy analytic hierarchy process (IVFF-AHP) to assess the resilience of circular supply chains (CSC)s. By categorizing resilience-related factors into different levels and determining their weights using IVFF-AHP, the framework aims to provide a comprehensive evaluation of the resilience of CSCs.Design/methodology/approachThe proposed framework integrates the concepts of CSCs, fuzzy multi criteria decision-making (MCDM) methods and the supply chain operations reference (SCOR) model. The SCOR model serves as a basis for identifying the factors relevant to resilience assessment. The framework then employs IVFF-AHP to determine the relative weights of these factors at each level, accommodating the inherent uncertainties and linguistic evaluations associated with assessing resilience.FindingsThe findings of this study highlight the efficacy of the IVFF-AHP-based framework for assessing the resilience of CSCs. By considering multiple factors at different levels and applying fuzzy AHP, the framework provides decision-makers with a comprehensive understanding of the resilience of CSCs. The use of IVFF-AHP enables the effective handling of vague and imprecise information, enhancing the decision-making process.Practical implicationsBy employing IVFF-AHP, decision-makers can better understand the factors influencing the resilience of CSCs and allocate resources accordingly. This enhanced understanding can aid in developing strategies to mitigate risks, enhance adaptability and improve the overall resilience of CSCs.Originality/valueThis study contributes to existing literature by introducing a novel framework that combines IVFF-AHP, CSCs and the SCOR model for assessing resilience. The application of fuzzy MCDM methods to evaluate the resilience of CSCs represents a significant novelty. The integration of the SCOR model further enhances the practical value of the framework, as it provides a standardized reference for identifying resilience-related factors. This research offers a unique and valuable perspective for researchers and practitioners in the field.

A Novel Geospatial‐based Coupled Multi‐Criteria Decision‐Making Techniques (E‐PROMETHEE‐II) for Identifying Potential Land Areas for Surface Irrigation in Koch Bihar District, West Bengal, India

ABSTRACTSurface irrigation is vital for maintaining agricultural productivity and preventing regional food insecurity. The current study aims to identify potential surface irrigation sites in the Koch Bihar district via an innovative geospatial‐based E‐PROMETHEE II multi‐criteria decision‐making (MCDM) technique. Fourteen factors, including distance from water bodies, river density, rainfall, temperature, evapotranspiration, the normalized difference vegetation index (NDWI), the soil moisture index, elevation, slope, aspect, soil properties, groundwater depth and land use/land cover, were selected for analysis, with a multicollinearity test ensuring their validity. The E‐PROMETHEE II method was used to create a suitability map for surface irrigation, benchmarked against the analytical hierarchical process (AHP) method. The results highlighted highly suitable zones for irrigation, notably in the Cooch Behar I, Dinhata I, Tufanganj I, Mathabhanga II, and parts of the Tufanganj II blocks. Validation via receiver operating characteristic (ROC) and area under the curve (AUC) analyses demonstrated AUC values exceeding 0.70 for all the models, underscoring their reliability. E‐PROMETHEE II (AUC = 0.889) emerged as the top‐performing method, trailed by the AHP (AUC = 0.728). These findings will facilitate the implementation of new surface irrigation systems in the study area.

Harnessing High-Altitude Advantages: Sustainable Data Center Site Selection in Ski Resort Regions for Optimized Energy Efficiency

Data centers are a part of the digital economy, but they are great contributors to energy use and carbon emissions. The site selection for data centers can, therefore, be an important tool in the optimization of energy efficiency and sustainability, especially in those areas where climatic and infrastructural factors can keep environmental impacts as low as possible. This study will evaluate seven possible sites for a data center in Turkey, focusing on high-altitude ski resorts with natural cooling advantages. In the paper, the research uses q-Rung Orthopair Fuzzy Numbers (q-ROFs) in combination with methods like Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), taking environmental, technical, and socio-economic criteria into consideration, such as energy efficiency, disaster risk, infrastructure quality, and accessibility. To strengthen result reliability, a sensitivity analysis by varying the q parameter and a comprehensive study by performing different multi-criteria decision-making (MCDM) methods were employed. Analysis revealed that Erciyes and Uludağ consistently ranked among the top alternatives across all methods and q values, indicating their robustness. The results confirm the effectiveness of the proposed methodology in supporting sustainable and high-altitude-aware site selection decisions. This research presents a holistic and Sustainable Site Selection framework for data centers; therefore, it provides actionable insights into stakeholders who balance operational efficiency with environmental responsibility. Future studies should subsequently delve into dynamic modeling and global applications to enhance the versatility of the framework.

Biomechanical effects of material selection and micro-groove design for dental implants

Abstract In this study, biomechanical evaluation was performed to select appropriate dental implant material, and the implant-bone model was verified by finite element analysis (FEA). Ti–6Al–4V, ZK60, WE43, HA, and Ta materials were compared using three different multi-criteria decision-making (MCDM) methods according to lightness, functional durability, relative movement, penetration, and cost criteria. The stress, deformation, safety factor, sliding distance, and penetration distributions obtained under four loading conditions were scored according to these evaluation criteria and Ti–6Al–4V was determined as the most successful material. By adding 0.1 mm wide and deep micro-grooves to the dental implant model, an 8.3 % improvement in sliding distance and a 53 % improvement in penetration were achieved.

Correction: Wang, C.-N.; Cheng, H.-C. A Fuzzy Multicriteria Decision-Making (MCDM) Model for Sustainable Supplier Evaluation and Selection Based on Triple Bottom Line Approaches in the Garment Industry. Processes 2019, 7, 400

Removal of an Author [...]

Hybrid multi-criteria decision making approaches for transtibial prostheses socket material selection

The selection of appropriate material for manufacturing of prosthetic socket is a critical decision that impacts comfort, durability, functionality, and cost effectiveness. This study employs a comprehensive Multi Criteria Decision Making (MCDM) approach to evaluate ten potential materials for prosthetic sockets based on six evaluation criteria. High density polyethylene (HDPE), polypropylene (PP), nylon 6 (Polyamide 6), nylon 12 (Polyamide 12), polyethylene terephthalate (PET), unsaturated polyester resin, epoxy resin, vinyl ester resin, carbon fiber reinforced polymer (CFRP), and polylactic acid (PLA) are assessed based on density, specific compressive strength, yield strength, young’s modulus, thermal conductivity, and relative cost of material. To address the complexity and multi dimensionality of the material selection process, four different MCDM techniques, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Multi Objective Optimization by Ratio Analysis (MOORA), Evaluation Based on Distance from Average Solution (EDAS), and Grey Relational Analysis (GRA) are employed in combination with an entropy weighting method to account for the varying importance of the criteria. By comparing these methods, we identify carbon fiber reinforced polymer (CFRP) and vinyl ester resin as most promising materials that balances the mechanical properties, thermal behaviour, and economic considerations. CFRP being most expensive material among peers, its remarkable mechanical properties make it outperform other materials. The findings serve as a comprehensive guide for researchers and practitioners in selecting materials to meet specific requirements for prosthetic socket manufacturing, while also offering a robust framework for future research and application in the field of prosthetics.

An integrated multi criteria decision making method using dual hesitant fuzzy sets with application for unmanned aerial vehicle selection

Unmanned aerial vehicles (UAVs) have gained widespread attention in recent years due to their expanding applications across various industrial sectors. Selecting the most suitable UAV for a given task is a critical decision-making challenge, which is typically modeled as a multi-criteria decision-making (MCDM) problem. However, expert assessments in such selection processes often involve considerable uncertainty and hesitation. To address this, this paper proposes a novel integrated MCDM framework that combines dual hesitant fuzzy sets (DHFSs), the best-worst method (BWM), and the MULTIMOORA method to evaluate and rank UAV alternatives. In the proposed method, DHFSs are employed to capture both membership and non-membership degrees of expert assessments under uncertainty, while expert weights are objectively determined based on the entropy of their assessments. Criteria weights are then calculated using an extended dual hesitant fuzzy BWM. Subsequently, the MULTIMOORA method is extended into the dual hesitant fuzzy environment, where UAV alternatives are evaluated from three perspectives: the ratio system, the extended reference point approach, and the full multiplicative form, and the evaluation results are aggregated to generate a comprehensive and reliable final ranking. To demonstrate the practicality and effectiveness of the proposed method, a case study on UAV selection for power line inspection is presented. The results show that the proposed approach effectively handles uncertainty, produces stable and consistent rankings, and offers reliable decision support under uncertain and fuzzy conditions. The proposed method provides a flexible and systematic decision-making tool that can assist decision-makers in solving UAV selection problems in complex, real-world scenarios.

Evaluation of interrelationship between supply chain transparency and relative efficiency for logistics service providers in Thailand

ABSTRACT Logistics Service Providers are key players in logistics operations to support a seamless flow between stakeholders in the supply chain in this globalization age. One key strategy in supply chain management to improve supply chain efficiency is through the integration of supply chain transparency among key activities and operations in the supply chain. In this study, we investigate the complex, interrelationship between supply chain transparency and supply chain efficiency in supply chains using an integrated Multi-Criteria Decision Analysis approach. Seven key performance indicators were initially identified and analyzed using the Analytic Hierarchy Process approach based on the illustrated case study of logistics service provider in Thailand. Then, based on the selected key performance indicators, the efficiency optimization technique called the Data Envelopment Analysis is further applied to evaluate the performance of each logistics service provider sequentially. Finally, the transparency level of each logistics service provider is integrated to assess the effects on supply chain efficiency through the scenario analysis. Analyzed results suggest a practical analysis to comprehend how transparency and efficiency can be quantified in the supply chain and how their interrelationship plays a role in managerial implications relevant to supply chain managers and policyholders.

Silviculture Promotes Sustainability in Nothofagus antarctica Secondary Forests of Northern Patagonia, Argentina: A Multicriteria Analysis

Despite the growing recognition of sustainability in forest management, comprehensive multi-criteria evaluations of silvicultural practices remain scarce, particularly in Patagonia. In this study, we applied a multi-criteria decision analysis to evaluate the sustainability of different strip-cutting intensities in secondary Nothofagus antarctica forests in Northern Patagonia, Argentina. The performance of four management alternatives was assessed: no cutting, low cutting intensity, medium cutting intensity, and high cutting intensity. These alternatives were evaluated across 11 indicators of nature’s contributions to people. Indicator values were estimated from previous research across three contrasting sites, complemented by expert surveys to estimate weights and target values for each indicator. The results indicate that the key indicators included those associated with firewood harvesting, fire and invasions prevention, and timber species plantation performance. Medium cutting intensity consistently emerged as the most sustainable option across all sites, models, and scenarios. In contrast, no cutting performed poorly across most sites, models, and scenarios. These findings underscore the importance of integrating diverse ecological and socioeconomic indicators into forest management planning. The promotion of medium cutting intensity has the potential to enhance sustainability in N. antarctica forests, thereby contributing to the development of resilient and multifunctional landscapes in Northern Patagonia.

Nudge-Based Intervention for Cognitive Enhancement of Elderly in Long-Term Care Facilities During Fire Evacuation According to Urgent-Level Circumstances

The cognitive ability of the elderly significantly influences evacuation performance in urgent situations. Despite its importance, many fire evacuation studies overlook the impact of cognitive ability on elderly evacuation performance. To address this gap, this study employs multicriteria decision-making to identify nudging factors that enhance the cognitive abilities of the elderly during fire evacuations in long-term care facilities. Based on a literature review, key nudging factors include guidance lights, guide lines, handrails, and guidance equipment, with sub-criteria such as location, color, size, and intervals. Experts from academic and practical fields analyzed the nudging factors, followed by a hybrid analytic hierarchy process (AHP–TOPSIS) analysis. The findings emphasize the necessity of providing auditory information through guidance equipment (e.g., voice evacuation system) in high-level scenarios (practice experts AHP: 0.31) and visual information through the continuous installation of guide lines in strategic locations (academic experts AHP: 0.35) to facilitate efficient evacuation. As a result, this study confirms both the differing and concordant opinions among expert groups while recognizing the absolute necessity of elderly evacuation research and considering the unique challenges that prevent actual evacuation experiments with elderly individuals. By synthesizing these perspectives, the study derives the weights and ranks of nudging factors based on urgent-level circumstances, thereby conducting a quantitative assessment of factors that enhance cognitive ability during elderly evacuation. The findings of this study can serve as a basis for future evacuation policy formulation for elderly-related facilities and, as a derivative effect, contribute to ensuring the life safety of elderly individuals within the local community.

Management of sustainable urban green spaces through machine learning-supported MCDM and GIS integration.

This study evaluates green space suitability in İzmir's Konak district using the analytic hierarchy process, machine learning, weighted linear combination, and the technique for order preference by similarity to ideal solution methods, integrated with geographic information systems. The approach enhances reliability in green space identification by ensuring consistent integration of weights determined by different methods. Machine learning enables dynamic adjustments to criterion weights, yielding the best results with the random forest algorithm. The analysis revealed that 75% of green spaces were sub-optimally located, with optimal zones in the western and northern regions. The technique for order preference by similarity to ideal solution methodology prioritized area ZGS_6 as the most suitable area, while ZGS_4 ranked lowest. This method supports efficient resource allocation and improves budgeting processes. Hence, the integration of multi-criteria decision-making and machine learning with geographic information systems enhances the planning of sustainable cities and offers critical insights to decision-makers who prioritize sustainability and livability.

ID-GMLM: Intelligent Decision-Making with Integrated Graph Models and Large Language Models

Multi-criteria decision making (MCDM) and preference learning (PL) are crucial subfields of intelligent decision-making, both aiming to aid decision-makers (DMs) in selecting, classifying, or ranking alternatives. While MCDM and PL can complement each other to some extent, existing approaches combining MCDM and PL often struggle with large data volumes and complex relational information. To address this, we propose a novel approach called ID-GMLM that integrates graph models and large language models (LLMs) for intelligent decision-making. It reformulates decision-making as a high-parallelism ranking function in the graph domain, using graph neural networks (GNNs) to learn and understand complex relationships between alternatives or criteria, and LLMs to parse and quantify the preferences of DMs. ID-GMLM features a multi-task learning framework that optimizes the primary task of predicting alternative rankings while modeling criterion interactions through the auxiliary task. Additionally, ID-GMLM incorporates a parameter tuning network based on criterion weights and an attention network, allowing the model to adaptively adjust to the context of the current task and the evolving preferences of DMs. Experiments on benchmark datasets demonstrate that ID-GMLM achieves significant performance improvements, inheriting the interpretability and intuitive appeal of MCDM while leveraging the computational efficiency and high accuracy of PL.

A Composited Spherical Fuzzy Distance-Based Multiple Criteria Decision-Making Approach for Warehouse Management Operation Technology Evaluation

The development of industry 4.0 requires industries in society to transform themselves in a smarter direction to respond to society’s needs. Therefore, the criteria and decision support models to choose advanced technology for warehouse management are of great significance to enterprises. In this study, the authors integrated fuzzy theory with Multi-Criteria Decision Making (MCDM) model to evaluate and select potential technologies. This study’s primary contribution is the creation of a comprehensive framework for the evaluation of the quality of advanced technologies in warehouse management. The spherical fuzzy Decision-Making Trial and Evaluation Laboratory (SF DEMATEL) – the spherical fuzzy Evaluation based on Distance from Average Solution (SF EDAS) not only offers logical results but also enables decision makers to visualize the effects of criteria different for the final result. Furthermore, other fields can apply valuable insights and techniques through this integrated model.

Prioritization of meteorological factors for rainfall prediction: A hybrid Fuzzy AHP and Fuzzy TOPSIS approach

Abstract Rainfall forecasting is crucial for disaster mitigation, agriculture, and water resource management. However, due to the dynamic nature of weather patterns, predicting rainfall remains a complex challenge. Various meteorological factors influence rainfall, necessitating an effective selection process to identify the most significant parameters. This study introduces a novel approach for parameter selection in rainfall forecasting by integrating two Multi-Criteria Decision-Making (MCDM) techniques: Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). The proposed method prioritizes key meteorological factors that have the greatest impact on occurrence of rainfall and enhancing forecasting precision. The selected parameters contribute to improving rainfall prediction accuracy, reducing errors, and providing a reliable foundation for advanced forecasting models.