Fuzzy Multi-criteria Decision Research Articles

An innovative fuzzy multi-criteria decision making model for analyzing anthropogenic influences on urban river water quality

An innovative fuzzy multi-criteria decision making model for analyzing anthropogenic influences on urban river water quality

Optimizing Supplier Selection using Fuzzy MCDM

This study explores the supplier selection problem within technology manufacturing by employing the Fuzzy Analytic Hierarchy Process (FAHP). Data was collected from a technology manufacturing company, and expert evaluations were sought from professionals with over 10 years of experience in the industry. The criteria considered for supplier selection included profitability of the supplier (C1), relationship closeness (C2), technological capability (C3), conformance quality (C4), and conflict (C5). The FAHP results indicated that relationship closeness (C2) ranked highest among the criteria, highlighting its significance in the supplier selection process, while conflict (C5) received the lowest ranking. These findings emphasize the importance of nurturing strong relationships with suppliers and vendors to enhance overall supply chain performance. The proposed FAHP model effectively addresses the complexities and uncertainties inherent in supplier evaluation, offering a robust decision-making tool tailored to the specific needs of technology manufacturing companies

Applying Fuzzy Multi-Criteria Decision Making for the Selection of Competitive Factors in Robotics Based on System Development Life Cycle

Applying Fuzzy Multi-Criteria Decision Making for the Selection of Competitive Factors in Robotics Based on System Development Life Cycle

A novel multi-criteria decision making method to evaluate green innovation ecosystem resilience

Green innovation ecosystem resilience evaluation is crucial for ensuring sustainable development in dynamic environments. By comprehensively assessing resilience, it helps identify risks and facilitate effective responses, thus promoting sustainable development. To effectively evaluate the resilience of green innovation ecosystems, this paper proposes a model based on the interval-valued intuitionistic fuzzy multi-criteria decision-making (MCDM) method, designed to support decision-makers (DMs) in resilience evaluation. In this model, we improve the Criteria Importance Through Intercriteria Correlation (CRITIC) method by introducing Spearman's correlation coefficient to more accurately determine the objective weights of various indicators based on their interrelationships. Furthermore, we propose an Interval-Valued Intuitionistic Fuzzy Hybrid Average Aggregation (IVIFHAA) operator, which considers both the subjective and objective weight order for aggregating expert evaluation results. Additionally, a scoring function is introduced to generate ranked evaluation results. We also establish a resilience evaluation criteria system covering interconnectivity, sustainability, and diversity within green innovation ecosystems to assist DMs in resilience evaluation. Finally, to validate this method, we apply it to assess the resilience of regional green innovation ecosystems and compare it with other methods. The results demonstrate that the improved operator effectively reduces information loss and improves evaluation efficiency, while the improved CRITIC method handles outliers during the evaluation process.

Exploring a fuzzy relationships among criteria for the development of community-based tourism in Malaysia: a multi-stakeholder perspective

PurposeThe purpose of the study was to identify and analyze the fuzzy relationships among criteria for the development of community-based tourism (CBT) in Malaysia through a multi-stakeholder lens. By exploring these relationships, the study aimed to provide valuable insights for stakeholders and policymakers to enhance the sustainability and effectiveness of CBT initiatives in Malaysia. The focus was on understanding the interplay between environmental, economic and socio-cultural criteria to guide decision-making and planning processes for sustainable tourism development.Design/methodology/approachThe study utilized a quantitative approach, specifically employing a fuzzy multi-criteria decision-making (MCDM) model known as decision-making trial and evaluation laboratory (DEMATEL). This methodology allowed for the integration of diverse perspectives from multiple stakeholders involved in CBT in Malaysia. By using DEMATEL, the researchers were able to analyze the complex relationships among sustainable criteria, identify causal factors and assess the interdependencies among environmental, economic and socio-cultural dimensions. The approach aimed to provide a comprehensive understanding of the criteria influencing the sustainability of CBT development in Malaysia.FindingsThe findings of the study revealed that environmental and economic factors emerged as pivotal causal criteria influencing socio-cultural outcomes in the development of CBT in Malaysia. The research highlighted the importance of multi-stakeholder collaboration in building sustainability in CBT, emphasizing the need for regular meetings, shared decision-making processes and clear communication channels among all involved parties. These insights empower stakeholders to make informed decisions and prioritize criteria for sustainable CBT development, ultimately contributing to the well-being of communities, preservation of natural and cultural heritage, and long-term environmental sustainability.Research limitations/implicationsThe study's limitations include the focus on CBT in Malaysia, which may limit the generalizability of the findings to other contexts. Additionally, the research relied on a quantitative approach, potentially overlooking qualitative nuances in stakeholder perspectives. Future studies could address these limitations by exploring diverse geographical locations and incorporating qualitative methods to capture a more comprehensive understanding of sustainable criteria in CBT development. These implications suggest opportunities for further research to validate and extend the findings in different contexts and with a broader range of stakeholder perspectives.Practical implicationsThe practical implications of the study include providing guidance for tourism policymakers and key stakeholders in developing effective policy interventions for CBT in Malaysia. By understanding the complex relationships among sustainable criteria, stakeholders can make informed decisions to foster environmental, economic and socio-cultural growth in CBT initiatives. The findings offer insights for creating economic opportunities, promoting fair trade practices and enhancing linkages between CBT and local businesses. These practical implications aim to support sustainable decision-making, planning processes and implementation strategies to ensure the long-term success and positive impact of CBT in Malaysia.Social implicationsThe social implications of the study underscore the importance of multi-stakeholder collaboration in CBT development in Malaysia. By engaging various stakeholders, including host communities, government agencies, non-governmental organizations (NGOs) and tourists, the research promotes social cohesion, cultural exchange and community empowerment. Enhancing social interactions and mutual understanding among stakeholders can lead to inclusive decision-making processes, equitable distribution of benefits and the preservation of local heritage and traditions. These social implications emphasize the potential for CBT to contribute to social well-being, cultural preservation and sustainable development, fostering positive social impacts and strengthening community resilience.Originality/valueThe originality and value of the study lie in its exploration of the complex relationships among sustainable criteria for CBT development in Malaysia from a multi-stakeholder perspective. By utilizing the DEMATEL method and quantitative analysis, the research offers a novel approach to understanding the causal factors influencing environmental, economic and socio-cultural outcomes in CBT initiatives. The study's findings provide valuable insights for stakeholders and policymakers to make informed decisions, prioritize criteria and develop effective strategies for sustainable CBT development. This original contribution enhances the understanding of sustainable criteria relationships and empowers stakeholders to drive positive change in the tourism sector.

Integrated Approach Using Intuitionistic Fuzzy Multicriteria Decision-Making to Support Classifier Selection for Technology Adoption in Patients with Parkinson Disease: Algorithm Development and Validation.

Parkinson disease (PD) is reported to be among the most prevalent neurodegenerative diseases globally, presenting ongoing challenges and increasing burden on health care systems. In an effort to support patients with PD, their carers, and the wider health care sector to manage this incurable condition, the focus has begun to shift away from traditional treatments. One of the most contemporary treatments includes prescribing assistive technologies (ATs), which are viewed as a way to promote independent living and deliver remote care. However, the uptake of these ATs is varied, with some users not ready or willing to accept all forms of AT and others only willing to adopt low-technology solutions. Consequently, to manage both the demands on resources and the efficiency with which ATs are deployed, new approaches are needed to automatically assess or predict a user's likelihood to accept and adopt a particular AT before it is prescribed. Classification algorithms can be used to automatically consider the range of factors impacting AT adoption likelihood, thereby potentially supporting more effective AT allocation. From a computational perspective, different classification algorithms and selection criteria offer various opportunities and challenges to address this need. This paper presents a novel hybrid multicriteria decision-making approach to support classifier selection in technology adoption processes involving patients with PD. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) was implemented to calculate the relative priorities of criteria and subcriteria considering experts' knowledge and uncertainty. Second, the intuitionistic fuzzy decision-making trial and evaluation laboratory (IF-DEMATEL) was applied to evaluate the cause-effect relationships among criteria/subcriteria. Finally, the combined compromise solution (CoCoSo) was used to rank the candidate classifiers based on their capability to model the technology adoption. We conducted a study involving a mobile smartphone solution to validate the proposed methodology. Structure (F5) was identified as the factor with the highest relative priority (overall weight=0.214), while adaptability (F4) (D-R=1.234) was found to be the most influencing aspect when selecting classifiers for technology adoption in patients with PD. In this case, the most appropriate algorithm for supporting technology adoption in patients with PD was the A3 - J48 decision tree (M3=2.5592). The results obtained by comparing the CoCoSo method in the proposed approach with 2 alternative methods (simple additive weighting and technique for order of preference by similarity to ideal solution) support the accuracy and applicability of the proposed methodology. It was observed that the final scores of the algorithms in each method were highly correlated (Pearson correlation coefficient >0.8). The IF-AHP-IF-DEMATEL-CoCoSo approach helped to identify classification algorithms that do not just discriminate between good and bad adopters of assistive technologies within the Parkinson population but also consider technology-specific features like design, quality, and compatibility that make these classifiers easily implementable by clinicians in the health care system.

A Distance Measure for Intuitionistic Fuzzy Multicriteria Decision Making in Pattern Recognition and Medical Diagnosis

ABSTRACT In artificial intelligence, decision-making is a widely studied field, and distance measure plays a crucial role in decision science. Several distance measures for Intuitionistic Fuzzy sets (IFSs) have been developed to handle uncertain situations. However, some of these measures lack properties of distance measures or produce unreasonable results. This study introduces a new distance measure between IFSs that satisfies the necessary properties and resolves counter-intuitive cases. The proposed distance measure is verified to be more reasonable through numerical examples, and it is applied to pattern recognition and medical diagnosis problems.

Correlation coefficient of r,s,t-spherical hesitant fuzzy sets and MCDM problems based on clustering algorithm and technique for order preference by similarity to ideal solution method

Correlation coefficient of r,s,t-spherical hesitant fuzzy sets and MCDM problems based on clustering algorithm and technique for order preference by similarity to ideal solution method

A two-stage framework for site selection of deep-sea offshore wind-to-hydrogen projects based on GIS - MULTIMOORA method under Pythagorean triangular fuzzy environment

Constructing deep-sea offshore wind-to-hydrogen projects (DS-OWHP) is crucial to solve the problem of offshore electricity storage and consumption. Addressing the uncertainty of the decision-making environment, and the lack of a comprehensive indicator system, this paper proposes a two-stage decision framework based on Geographic Information System (GIS). In the first stage, GIS is used to analyze the suitability of study area and identify alternative sites. In the second stage, the fuzzy MCDM method is used to further optimize alternative sites. Firstly, a comprehensive evaluation indicator system is established, and the Pythagorean Triangular Fuzzy Number (PTFN) is used to describe evaluation information. Then, a combination of stepwise weight assessment ratio analysis (SWARA), entropy, and game theory is used to determine weights, and the Multi-Objective Optimization on the basis of a Ratio Analysis plus the Full Multipli-cative form (MULTIMOORA) method is used to rank alternative sites. Finally, the feasibility of framework is proved through a case study in Guangdong Province. The results show that average wind speed has the highest weight of 0.149, and alternative site A2 which is close to zhuahai is the optimal choice. Through sensitivity analysis and comparative analysis, the reliability and stability of proposed model is verified.

Optimizing the performance parameters of vacuum evaporation technology for management of anaerobic digestate in a waste water treatment plant using fuzzy MCDM method

Evaporation technology in waste water treatment plants is used to treat excess liquid digestate, eventually reaping the essential nutrients from the process. In the present study, the input conditions have been optimized for implementing and achieving optimal performance of the digestate evaporation technology used in waste water treatment plants by employing fuzzy multi-criteria decision making method. Three input parameters, namely the Temperature, the Pressure, and the Mass Flow rate, each at three levels are considered. Experiments are conducted as per the Taguchi’s L9 standard orthogonal array. The performance of the evaporator is examined based on a few response characteristics, such as payback period, thermal energy consumption, power consumption and cost per unit of electricity. Temperature's impact on multi performance stands out significantly, contributing the most (70.52 %), followed by mass flow rate (18.95 %), and pressure (5.37 %). Result of the study reveals that within the investigated range of input parameters, temperature at 80 ℃, pressure at 0.3 bar and mass flow rate at 20 kg/h is the optimal combination. The results of the present study will help in enhancing the efficiency and effectiveness of the evaporation process, leading to improved digestate treatment that supports circular economy initiatives.

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.

Enhancing Student Performance through Fuzzy MCDM: Evaluating Family Concern, Study Environment, and other Key Factors

Abstract: In this study, the Fuzzy Analytic Hierarchy Process (FAHP) was utilized to evaluate factors affecting student wellbeing and performance. The criteria assessed included C1 - Family Concern, C2 - Environment of Study, C3 - Resources, C4 - Time Management, and C5 - Lecturer Concern. A survey was administered to students at United College of Arts and Science in Coimbatore, Tamil Nadu, India, to gather numerical data. Pairwise comparison matrices were created based on the survey responses to assess the relative importance of each criterion. The FAHP analysis revealed that Family Concern (C1) has the greatest impact on student outcomes, highlighting the crucial role of family support in student success and well-being. The findings suggest that educators and parents should prioritize addressing family-related issues and consider implementing additional supportive measures, such as yoga and sports activities, to further improve student performance and overall wellbeing

Combining Fuzzy MCDM and Machine Learning for Predictive Analytics in Diabetes Management

This study utilizes the Fuzzy Analytic Hierarchy Process (FAHP) to evaluate the factors influencing the risk of diabetes-related complications. Data was collected from diabetes specialists in Coimbatore, each with over 15 years of experience, to construct a pairwise comparison matrix based on expert opinions. The analysis identified six key criteria: Age (C1), Cardiopulmonary function (C2), Cardiovascular disease (C3), Family history of sudden death (C4), Smoking (C5), and Blood glucose (C6). The results reveal that Cardiopulmonary function (C2) is the most critical factor, with a weight of 0.1193, followed by Age (C1) and Family history of sudden death (C4). While long-term health indicators are more influential than immediate lifestyle factors, the contributions of Smoking (C5) and Alcoholism (C6) are still relevant. These findings emphasize the need for a comprehensive approach to diabetes management that balances inherent and lifestyle-related risks.

Integrating Fuzzy MCDM Methods and ARDL Approach for Circular Economy Strategy Analysis in Romania

This study investigates the factors influencing CO2 emissions in Romania from 1990 to 2023 using the Autoregressive Distributed Lag (ARDL) model. Before the ARDL model, we identified a set of six policies that were ranked using Fuzzy Electre, Topsis, DEMATEL, and Vikor. The multi-criteria decision-making (MCDM) methods have highlighted the importance of a circular policy on CO2 emission reduction, which should be a central focus for policymakers. The results of the ARDL model indicate that, in the long term, renewable energy production reduces CO2 emissions, showing a negative relationship. Conversely, an increase in patent applications and urbanization contributes to higher CO2 emissions, reflecting a positive impact. In total, five key factors were analyzed: CO2 emissions per capita, patent applications, gross domestic product, share of energy production from renewables, and urbanization. Notably, GDP does not significantly explain CO2 emissions in the long run, suggesting that economic growth alone is not a direct driver of CO2 emission levels in Romania. This decoupling might result from improvements in energy efficiency, shifts towards less carbon-intensive industries, and the increased adoption of renewable energy sources. Romania has implemented effective environmental regulations and policies that mitigate the impact of economic growth on CO2 emissions.

A fuzzy multi-criteria decision-making for optimizing supply chain aggregate production planning based on cost reduction and risk mitigation

In an increasingly complex and uncertain business environment, decision makers require robust strategies for supply chain aggregate production planning that account for the interdependencies and coordination across multiple echelons of the supply chain network. Traditional approaches often inadequately address the uncertainties and risks inherent in supply chain operations, leading to suboptimal performance and increased costs. This study introduces a business model incorporating open innovation dynamics to achieve cost-effectiveness and resilient supply chain performance in the face of uncertainties. A Multi-Objective Fuzzy Linear Programming model was proposed by integrating the Chance-Constraint Programming and Zimmermann’s approach designed to assist decision makers in optimizing the production plan, material flows and resource allocation across the entire supply chain network. The model focuses on both cost and risk minimization based on unsymmetrical triangular fuzzy numbers. Specifically, it targets the downside risk of uncertainty, aiming to reduce the likelihood of negative outcomes or financial losses due to fluctuations, unpredictability, and unforeseen circumstances, which can drive-up supply chain operation costs. The efficacy of the model is demonstrated through a case study. It highlights various imprecise factors such as costs, customer demands, and machine operating hours. The findings underscore the model's capability to provide decision makers with a comprehensive supply chain aggregate production plan that not only optimizes the supply chain network and enhances operational efficiency and reliability but also significantly reduces costs and mitigates unsymmetrical skewness of risks associated with operational uncertainties.

Application of machine learning and fuzzy AHP for identification of suitable groundwater potential zones using field based hydrogeophysical and soil hydraulic factors in a complex hydrogeological terrain

The eastern section of West Bengal grapples with limited surface water availability in its hard rock terrain, compounded by a semi-arid climate, variable rainfall, and a plateau topography, prompting communities to adapt groundwater water-use practices, leading to unsustainable extraction and misuse. Thus, the novel objective of the present research was to produce groundwater potential maps by comparing machine learning techniques with a Fuzzy MCDM model using specific field-based conditioning factors. In the first step, 285 wells were identified, of which 70 percent were used for training and 30 percent for the validation of the models. Secondly, field-based conditioning factors including, longitudinal conductance (SC), longitudinal resistance (ρl), transverse resistance (TR), coefficient of electrical anisotropy (λ), resistivity of formation (ρm), fracture porosity (φf), reflection coefficients (r), hydraulic conductivity (K), transmissivity(Tr), bulk density, porosity, permeability, soil moisture content and water holding capacity were used to analyze the association between these conditioning factors and groundwater occurrences. In the following steps, the XGBoost, Random Forest, and Naïve Bayes models were executed using the training dataset, and factor weights were calculated using Fuzzy Analytical Hierarchy Process of Extent analysis method. To validate and compare the performance of four models, ROC curves, AUCs, MCAs, and correlation plots were used. In general, all four models were successful in evaluating the potential of groundwater occurrences. The predictive capability of the XGBoost techniques with the highest AUC values (0.79) and the highest correlation value (0.78) is superior to those of other machine learning and MCDM models. Geophysical survey revealed that transmissivity and hydraulic conductivity of the aquifer of the river basin range from 1.55 to 440.11 m/day and 10.15–2253 m2/day, indicating a moderate to good hydrodynamic potential. Planners and engineers can use such groundwater potential maps to manage water resources effectively.

Interdependencies in industry 4.0 maturity: Fuzzy MCDA analysis for open innovation in developing countries

The emergence of Industry 4.0 (I4.0) is reshaping industries worldwide, driven by rapid technological progress and the need for open innovation. This study focuses on understanding the interdependencies of driving factors of I4.0 maturity in developing countries using Fuzzy Multi-Criteria Decision Analysis (MCDA) methods. By analyzing Indonesia, Pakistan, and Venezuela, the research aims to foster open innovation and address the unique challenges these nations face in adopting I4.0 technologies. I4.0 maturity models are essential for evaluating current maturity levels and identifying areas for improvement. However, the complexity and interdependence of various factors—ranging from data science and technology to policy, governance, and open innovation dynamics, such as social open innovation and the role of SMEs—complicate this process. This study employs Fuzzy TOPSIS and Fuzzy DEMATEL to identify the critical factors influencing I4.0 maturity and analyze their interdependencies and prioritization. The results indicate that 'Data and Information' and 'Willingness to Change' are crucial across all countries, while strategic differences between large enterprises and SMEs highlight the need for tailored approaches. This research highlights the importance of continuous IT investment, digital leadership, collaborative ecosystems, and agile strategies in fostering open innovation and driving I4.0 adoption. This research contributes to the theoretical and practical understanding of I4.0 maturity, offering valuable insights for practitioners and academics to explore the dynamic interactions of I4.0 factors and their impact on operational efficiency.

A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction.

Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there's a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs. The success of AI, particularly LLMs, in the healthcare domain, depends on their efficacy, safety, and ethical compliance. Therefore, it is essential to have a robust evaluation framework for their integration into medical contexts. This study proposes using the Fuzzy-Weighted Zero-InConsistency (FWZIC) method extended to p, q-quasirung orthopair fuzzy set (p, q-QROFS) for weighing evaluation criteria. This extension enables the handling of uncertainties inherent in medical decision-making processes. The approach accommodates the imprecise and multifaceted nature of real-world medical data and criteria by incorporating fuzzy logic principles. The MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) method is employed for the assessment of medical LLMs utilized in the case study of this research. The results of this research revealed that "Medical Relation Extraction" criteria with its sub-levels had more importance with (0.504) than "Clinical Concept Extraction" with (0.495). For the LLMs evaluated, out of 6 alternatives, ( ) "GatorTron S 10B" had the 1st rank as compared to ( ) "GatorTron 90B" had the 6th rank. The implications of this study extend beyond academic discourse, directly impacting healthcare practices and patient outcomes. The proposed framework can help healthcare professionals make more informed decisions regarding the adoption and utilization of LLMs in medical settings.

Prioritization of renewable energy for offshore ship charging stations based on intuitionistic fuzzy GLDS method: A case of China

Offshore ship charging station (OSCS) projects can help to address the current demand for electric ships for ocean voyages to a large extent. The proper selection of the energy source for power generation is a key part of the OSCS project. To select the optimal renewable energy for OSCS with many difficulties such as the ambiguity of the decision-making environment, the differences in group assessment information, and the conflict and compensation between criteria, this paper proposed a fuzzy multi-criteria decision-making (MCDM) framework. First, a comprehensive criteria system was constructed. Second, the intuitionistic fuzzy set (IFS) was introduced to express experts’ fuzzy cognition. Third, based on the quality of evaluation, a novel expert weighting method was proposed, and the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator was used to aggregate the individual evaluations. Fourth, the criteria importance through intercriteria correlation (CRITIC) method, and the stepwise weight assessment ratio analysis II (SWARA II) method were introduced to determine the criteria weights. Fifth, considering criteria compensation, and individual and group ranks, the gained and lost dominance score (GLDS) method were used for ranking. Finally, to verify the applicability and reliability of the framework, a case study was conducted in Pingtan Island, Fujian Province. The results show that wind energy was the best alternative, followed by solar, wave and nuclear energy.

Elevating Pakistan’s flood preparedness: a fuzzy multi-criteria decision making approach

In South Asia, Pakistan has a long and deadly history of floods that cause losses to various infrastructures, lives, and industries. This study aims to identify the most appropriate flood risk mitigation strategies that the government of Pakistan should adopt. The assessment of flood risk mitigation strategies in this study is based on certain criteria, which are analyzed using the fuzzy full consistency method. Moreover, flood risk mitigation strategies are evaluated by using the fuzzy weighted aggregated sum product assessment (WASPAS) method, considering previously prioritized criteria. According to the results, lack of governance, lack of funding and resources, and lack of flood control infrastructure are the most significant flood intensifying factors and act as major criteria for assessing flood risk mitigation strategies in Pakistan. Adopting hard engineering strategies (e.g., dams, reservoirs, river straightening and dredging, embankments, and flood relief channels), maintaining existing infrastructure, and adopting soft engineering strategies (flood plain zoning, comprehensive flood risk assessment, and sophisticated flood modeling) are identified as the top three flood risk mitigation strategies by the fuzzy WASPAS method. The highest weight (0.98) was assigned to the adoption of hard engineering strategies to mitigate flood risks. The study introduces a novel dimension by analyzing the real-time impact of the unprecedented 2022 floods, during which approximately one-third of the nation was submerged. This focus on a recent and highly significant event enhances the study’s relevance and contributes a unique perspective to the existing literature on flood risk management. The study recommends that the government of Pakistan should prioritize hard engineering strategies for effective flood risk mitigation. It also recommends that the government should incorporate these strategies in the national policy framework to reduce flood losses in the future.