Fuzzy Decision-making Framework Research Articles

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.

Location selection for a lumber drying facility via a hybrid Pythagorean fuzzy decision-making approach

The strategic selection of facility locations plays a critical role in optimizing operational efficiency, reducing costs, and enhancing customer satisfaction, thereby contributing significantly to the success and competitiveness of businesses. In this study, an interval-valued Pythagorean fuzzy decision-making framework is proposed to select the best location for the lumber drying industry. A four-level hierarchical model is devised with four main criteria, 16 subcriteria, and five alternatives. The opinions of different experts are gathered to obtain input data. The weights of the criteria are calculated using the interval-valued Pythagorean fuzzy analytic hierarchy process (AHP) method. The interval-valued Pythagorean fuzzy weighted aggregated sum product assessment (WASPAS) method is employed to evaluate the alternative locations. A sensitivity analysis is conducted to support the validity of the model results. The study concludes by revealing the optimal location for the lumber drying industry in Turkey. This study presents its novelty by formulating the lumber drying facility location selection problem as a complex fuzzy multicriteria decision-making problem and integrating the Pythagorean fuzzy AHP and WASPAS methods to solve the problem.

Collaboration of Vague Theory and Mathematical Techniques for Optimizing Healthcare by Recommending Optimal Blockchain Supplier

The Internet of Medical Things (IoMTs) has the potential to revolutionize healthcare delivery by connecting medical devices and applications to healthcare IT systems via the internet. This connectivity enables the collection, transmission, and analysis of patient data, facilitating remote healthcare delivery and enhancing patient outcomes. However, the security and privacy of the data transmitted and stored within IoMT systems remain critical concerns. Blockchain technology offers a promising solution to address these challenges in IoMTs applications. Blockchain provides a decentralized and immutable ledger where transactions, in this case, patient data, are securely recorded and cannot be altered retroactively. This ensures the integrity and confidentiality of sensitive medical information while eliminating the need for a central authority to manage data exchange. This study proposes a comprehensive fuzzy decision-making framework for selecting the best blockchain provider in the IoMT field. The framework comprises two main steps: Determining the relative importance weights of the criteria utilizing the Stepwise Weight Assessment Ratio Analysis (SWARA) method. Determining the most suitable supplier for blockchain services providers employing the Ranking Alternatives with the Fuzzy ROOT Assessment Method (RAM) technique. A comparison between Fuzzy Multi-Criteria Decision-Making (MCDM) methods is employed to ensure the robustness and validity of the proposed framework.

A q-rung orthopair fuzzy decision-making framework considering experts trust relationships and psychological behavior: An application to green supplier selection

The selection of an optimal supplier is a critical and open challenge in supply chain management. While experts' assessments significantly influence the supplier selection process, their subjective interactions can introduce unpredictable uncertainty. Existing methods have limitations in effectively representing and handling this uncertainty. The paper aims to address these challenges by proposing a novel approach that leverages q-rung orthopair fuzzy sets (q-ROFSs). The novelty of the proposed approach lies in its ability to accurately capture experts' preferences through the use of q-ROFSs, which offer membership and non-membership degrees, providing a broader expression space compared to conventional fuzzy sets. Additionally, it incorporates social network analysis (SNA) to effectively consider the trust relationships among experts. The proposed approach is divided into three stages. The first stage, presents a novel method to determine experts' weights by combining SNA, the Bayesian formula, and the maximum entropy principle. This approach allows for a more precise representation of varying levels of expertise and influence among experts, addressing the uncertainty arising from subjective interactions. The second stage introduces a hybrid weight determination method to determine criteria weights within the context of q-ROFSs. Entropy plays a crucial role in capturing fuzziness and uncertainty in q-ROFSs, while the projection measure simultaneously provides information about the distance and angle between alternatives. By employing both objective weights estimated using entropy and normalized projection measure and subjective weights derived using an aggregation operator and a score function, the presented approach achieves a comprehensive assessment of criteria importance. To incorporate both subjective and objective weights effectively, game theory is applied which allows us to align decision-making with both quantitative and qualitative aspects, making the method more versatile and applicable. The third stage redefines the traditional Combined Compromise Solution (CoCoSo) method using Bonferroni mean operators which captures interrelationships among arguments to be aggregated. Furthermore, in recognition of the importance of an expert risk preferences and psychological behaviors, we apply regret theory, ensuring that the chosen solutions align more effectively with their underlying preferences and aspirations. The applicability and effectiveness of the proposed approach are demonstrated through a numerical example of green supplier selection. The comparative analysis illustrates the practicality and real-world relevance while the sensitivity analysis, confirms the stability and robustness of the proposed approach.

Fuzzy decision-making framework for explainable golden multi-machine learning models for real-time adversarial attack detection in Vehicular Ad-hoc Networks

This paper addresses various issues in the literature concerning adversarial attack detection in Vehicular Ad-hoc Networks (VANETs). These issues include the failure to consider both normal and adversarial attack perspectives simultaneously in Machine Learning (ML) model development, the lack of diversity preprocessing techniques for VANETs communication datasets, the inadequate selection guidelines for real-time adversarial attack detection models, and the limited emphasis on explainability in adversarial attack detection. In this study, we propose an original fuzzy decision-making framework that incorporates multiple fusion standpoints. Our framework aims to evaluate multi-ML models for real-time adversarial attack detection in VANETs, focusing on three stages. The first stage involves identifying and preprocessing Dedicated Short-Range Communication (DSRC) data using standard and fusion preprocessing approaches. Two communication scenarios, normal and jammed, are considered, resulting in two DSRC datasets. In the second stage, we develop multi-ML models based on the DSRC datasets using standard preprocessing and feature fusion preprocessing for dataset-1 and dataset-2, respectively. The third stage evaluates the multi-ML models using a fuzzy decision-making approach based on the Fuzzy Decision by Opinion Score Method (FDOSM) and an adversarial attack decision fusion matrix. The External Fusion Decision (EFD) settings of the FDOSM address individual ranking variance, provide a unique rank and select the best model. Experimental results demonstrate that the K-Nearest Neighbors Algorithm (kNN) model achieves the highest explain score of 0.2048 in dataset-1 using standard preprocessing, while the Random Forest (RF) model applied to dataset-2 using fusion preprocessing emerges as the most robust and golden model against adversarial attacks, with a score of 0.1819. This finding suggests that the fusion preprocessing approach using Principal Component Analysis (PCA) is more suitable for addressing normal and adversarial attack perspectives. Furthermore, our fuzzy framework undergoes evaluation in terms of systematic rank, sensitivity analysis, explainability analysis, and comparison analysis. Overall, this framework provides valuable insights for researchers and practitioners in VANETs, informing the execution, selection, and interpretation of multi-ML models to tackle adversarial attack detection problems effectively. The new fuzzy framework demonstrates that multi-ML models based on feature fusion preprocessing are more effective.

Development of hybrid feature learner model integrating FDOSM for golden subject identification in motor imagery.

Brain-computer interfaces (BCIs) based on motor imagery (MI) face challenges due to the complex nature of brain activity, nonstationary and high-dimensional properties, and individual variations in motor behaviour. The identification of a consistent "golden subject" in MI-based BCIs remains an open challenge, complicated by multiple evaluation metrics and conflicting trade-offs, presenting complex Multi-Criteria Decision Making (MCDM) problems. This study proposes a hybrid brain signal decoding model called Hybrid Adaboost Feature Learner (HAFL), which combines feature extraction and classification using VGG-19, STFT, and Adaboost classifier. The model is validated using a pre-recorded MI-EEG dataset from the BCI competition at Graz University. The fuzzy decision-making framework is integrated with HAFL to allocate a golden subject for MI-BCI applications through the Golden Subject Decision Matrix (GSDM) and the Fuzzy Decision by Opinion Score Method (FDOSM). The effectiveness of the HAFL model in addressing inter-subject variability in EEG-based MI-BCI is evaluated using an MI-EEG dataset involving nine subjects. Comparing subject performance fairly is challenging due to complexity variations, but the FDOSM method provides valuable insights. Through FDOSM-based External Group Aggregation (EGA), subject S5 achieves the highest score of 2.900, identified as the most promising golden subject for subject-to-subject transfer learning. The proposed methodology is compared against other benchmark studies from various key perspectives and exhibits significant novelty in several aspects. The findings contribute to the development of more robust and effective BCI systems, paving the way for advancements in subject-to-subject transfer learning for BCI-MI applications.

New Horizons in Bank Mergers: A Quantum Spherical Fuzzy Decision-Making Framework for Analyzing Islamic and Conventional Bank Mergers and Enhancing Resilience

This study explores the implications of merging two fundamentally different types of banks: Islamic and conventional banks. The research aims to provide insight into the unique opportunities and challenges presented by such a merger and to offer strategic guidance for future mergers. A balanced scorecard-based strategic analysis using a Quantum Spherical Fuzzy Decision-Making Approach was used to develop short- and long-term strategic plans for the merged bank. The balanced scorecard included 12 key performance indicators (KPIs) in 4 groups, and the methodology incorporated several questions to guide the analysis. The results of the study offer valuable insights into the potential opportunities and challenges of merging these two types of banks, as well as strategic recommendations for stakeholders at all levels. The study serves as a useful guideline for future mergers between similar or different types of banks. Overall, the findings suggest that a well-planned merger strategy is essential for avoiding challenges and maximizing the benefits of merging Islamic and conventional banks. By integrating the strengths of both types of banks, a merged entity could create a competitive advantage and potentially improve financial performance. However, this requires careful consideration of cultural differences, regulatory challenges, and other factors that could impact on the success of the merger.

Fuzzy Decision-Making Framework for Sensitively Prioritizing Autism Patients with Moderate Emergency Level

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that requires careful assessment and management. The prioritization of ASD patients involves navigating through complexities such as conflicts, trade-offs, and the importance of different criteria. Therefore, this study focuses on prioritizing patients with ASD in the healthcare setting through an evaluation and benchmarking framework. The aim of this study is to develop a framework that utilizes Multi-Criteria Decision Making (MCDM) methods to assist healthcare professionals in prioritizing ASD patients, particularly those with moderate injury levels. The methodology of the framework outlines several phases, including dataset identification, development of a decision matrix, weighting of 19 ASD criteria using the FWZIC method, ranking 432 patients using the VIKOR method, and evaluating the proposed framework using four sensitivity analysis scenarios. Among the 19 ASD criteria, the criterion 'verbal communication' obtained the highest weight. Additionally, criteria such as 'laughing for no reason', 'nodding', 'patient movement at home', and 'pointing with the index finger' obtained similar higher weights, indicating their potential impact on ASD patients. The experimental results highlight the significance of adjusting ASD weights in influencing the final rankings obtained through the VIKOR method. This emphasizes the need for careful consideration when assigning weights to the 19 ASD criteria to ensure accurate prioritization. Moreover, the framework provides valuable insights into improving the care and support provided to individuals with autism in Iraq. The findings contribute to the existing body of knowledge in the field of autism care prioritization and pave the way for future research and interventions aimed at enhancing the quality of care for individuals with autism in Iraq.

An interval type-2 fuzzy ORESTE method for waste-to-energy plant site selection: A case study in China

Serving as a promising solution to manage municipal solid waste (MSW), the waste-to-energy (WTE) system has recently received increasing attention. The first and critical step of constructing a WTE system is selecting a sustainable plant site. To support the site selection of the WTE system, this paper proposes a novel interval type-2 fuzzy decision-making framework. First, a sustainable evaluation index system is established by considering multiple criteria from several dimensions. Second, to deal with the heterogeneous information formats under different criteria, two information transformation mechanisms are developed. Through the proposed information transformation mechanisms, the heterogeneous information formats are unified into interval type-2 fuzzy sets. Then, a new interval type-2 fuzzy distance model is proposed to enhance Besson’s mean ranks in ORESTE method. Based on the proposed distance model, an interval type-2 fuzzy ORESTE method is developed to analyze the conflicts between the alternative sites and select the most desirable one. Finally, an example about the site selection of the WTE system is provided to illustrate the implementation process of our proposal, meanwhile, the comparative analysis and discussions are conducted to verify its superiority and flexibility.

Decision-making framework for identifying regions vulnerable to transmission of COVID-19 pandemic

At the beginning of 2020, the World Health Organization (WHO) identified an unusual coronavirus and declared the associated COVID-19 disease as a global pandemic. We proposed a novel hybrid fuzzy decision-making framework to identify and analyze these transmission factors and conduct proactive decision-making in this context. We identified thirty factors from the extant literature and classified them into six major clusters (climate, hygiene and safety, responsiveness to decision-making, social and demographic, economic, and psychological) with the help of domain experts. We chose the most relevant twenty-five factors using the Fuzzy Delphi Method (FDM) screening from the initial thirty. We computed the weights of those clusters and their constituting factors and ranked them based on their criticality, applying the Fuzzy Analytic Hierarchy Process (FAHP). We found that the top five factors were global travel, delay in travel restriction, close contact, social cohesiveness, and asymptomatic. To evaluate our framework, we chose ten different geographically located cities and analyzed their exposure to COVID-19 pandemic by ranking them based on their vulnerability of transmission using Fuzzy Technique for Order of Preference by Similarity To Ideal Solution (FTOPSIS). Our study contributes to the disciplines of decision analytics and healthcare risk management during a pandemic through these novel findings. Policymakers and healthcare officials will benefit from our study by formulating and improving existing preventive measures to mitigate future global pandemics. Finally, we performed a sequence of sensitivity analyses to check for the robustness and generalizability of our proposed hybrid decision-making framework.

A Fuzzy Decision-Making Framework for Route Selection in Multimodal Transportation Networks

This study presents a novel decision-making framework for route selection in multimodal transportation networks under fuzzy environments. The proposed framework integrates the fuzzy group decision-making technique, the fuzzy deliberate process, and fuzzy multiple criteria decision-making approaches. Hence, the fuzzy Delphi method, fuzzy risk assessment, the fuzzy analytic hierarchy process, and the fuzzy technique for order of preference by similarity to ideal solution are first combined to select the most appropriate multimodal route. An empirical study demonstrates that the proposed framework is an outstanding and systematic decision support tool to aid decision makers in transportation decision-making processes of freight route selections.

Evaluation of green lean production in textile industry: a hybrid fuzzy decision-making framework.

Textile industry is an old and effective industry in Iran. However, due to its age and high energy consumption, this industry has low profitability and entrepreneurship. One of the most important problems of the weaving industry is the issue of waste regarding manpower, materials, machinery, and especially energy consumption. Another problem is environmental pollution. In this paper, using a multiple decision-making model for ranking and selecting criteria and sub-criteria, which is presented using the step-wise weight assessment ratio analysis (SWARA) and also by examining several industrial plants on weaving, the final ranking was performed using the fuzzy COPRAS method. According to the final result and using the opinions of experts and reviewing the studied cases, the environmental criterion was more important than other criteria, and also according to the existing sub-criteria, the amount of CO2 production and pH in the process of completion and washing and the types of pollution in the effluent and sewage were more important than other sub-criteria. Also, among the alternatives, company 5 is evaluated as the best alternative.

Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients

IntroductionThe vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues. ObjectivesThis research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods. MethodsThe methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the ‘recipients list’ and ‘COVID-19 distribution criteria’. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM. Results(1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values. ConclusionThe findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.

A Two Phase Integrated Fuzzy Decision-Making Framework for Green Supplier Selection in the Coffee Bean Supply Chain

In Vietnam, as more and more organizations are moving toward globalization, green supplier selection (GSS) has emerged as a strategic approach in supply chain management that requires supplier practices in lessening the environmental risks to society. Based on both conventional and environmental criteria, this paper aims to evaluate a set of suppliers by establishing a multi-criteria decision-making (MCDM)-based framework using an integrated fuzzy analytical hierarchy process (FAHP) with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Initially, five GSS criteria of the environmental aspect (wastewater treatment, solid waste generation, energy consumption, air pollution, corporate social responsibility) and conventional criteria (quality, cost, delivery, and technology) are identified from the literature and consulting field experts to employ the MCDM approach. The trustworthiness of the proposed integrated framework is presented by discussing a case study in the coffee bean supply chain in Vietnam. The FAHP is used to generate criteria weights in which fuzzy set theory is applied to translate the linguistic evaluation statements of experts, and VIKOR is used to rank the alternatives against the selected criteria. From FAHP findings, the most important criteria are quantity discount, solid waste generation, order fulfillment rate, logistics cost, and purchasing cost. A consistency test is performed to ensure the uniformity of the expert’s input. The best suppliers are determined through the final ranking of the VIKOR model for the case study. The work presented provides insight to decision-makers of supplier selection that helps determine significant GSS criteria and aids in the minimization of environmental risks to society arising from the supply chain on corporate sustainability standards.

A multi-stage fuzzy decision-making framework to evaluate the appropriate wastewater treatment system: a case study.

Selection of appropriate treatment processes for wastewater treatment (WWT) plants at the design stage involves a careful examination of different economic, environmental, and social parameters. Designers and decision-makers seek a compromise among such conflicting elements, which can be facilitated by decision support tools that are adapted for the ambiguity of individual opinions and decision parameters. This study aims to improve the qualification and efficiency of decision-making in WWT processes. A multi-stage framework is proposed to help select investments, technology, appropriate technology-specific system, and companies that apply such systems. The framework combines the Analytic Hierarchy Process (AHP), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), cash flow analysis, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) within fuzzy logic. The main contribution is the description and formation of an integrated framework to guide businesses and researchers for the evaluation of several WWT decision processes. To the best of the authors' knowledge, no study in the literature fuses multiple stages of this WWT process with the proposed approaches.

An extended fuzzy decision-making framework using hesitant fuzzy sets for the drug selection to treat the mild symptoms of Coronavirus Disease 2019 (COVID-19)

The whole world is presently under threat from Coronavirus Disease 2019 (COVID-19), a new disease spread by a virus of the corona family, called a novel coronavirus. To date, the cases due to this disease are increasing exponentially, but there is no vaccine of COVID-19 available commercially. However, several antiviral therapies are used to treat the mild symptoms of COVID-19 disease. Still, it is quite complicated and uncertain decision to choose the best antiviral therapy to treat the mild symptom of COVID-19. Hesitant Fuzzy Sets (HFSs) are proven effective and valuable structures to express uncertain information in real-world issues. Therefore, here we used the hesitant fuzzy decision-making (DM) method. This study has chosen five methods or medicines to treat the mild symptom of COVID-19. These alternatives have been ranked by seven criteria for choosing an optimal method. The purpose of this study is to develop an innovative Additive Ratio Assessment (ARAS) approach to elucidate the DM problems. Next, a divergence measure based procedure is developed to assess the relative importance of the criteria rationally. To do this, a novel divergence measure is introduced for HFSs. A case study of drug selection for COVID-19 disease is considered to demonstrate the practicability and efficacy of the developed idea in real-life applications. Afterward, the outcome shows that Remdesivir is the best medicine for patients with mild symptoms of the COVID-19. Sensitivity analysis is presented to ensure the permanence of the introduced framework. Moreover, a comprehensive comparison with existing models is discussed to show the advantages of the developed framework. Finally, the results prove that the introduced ARAS approach is more effective and reliable than the existing models.

A hyper-hybrid fuzzy decision-making framework for the sustainable-resilient supplier selection problem: a case study of Malaysian Palm oil industry.

One of the major challenges of the supply chain managers is to select the best suppliers among all possible ones for their business. Although the research on the supplier selection with regards to green, sustainability or resiliency criteria has been contributed by many papers, simultaneous consideration of these criteria in a fuzzy environment is rarely studied. Hence, this study proposes a fuzzy decision framework to investigate the sustainable-resilient supplier selection problem for a real case study of palm oil industry in Malaysia. Firstly, the resilient-based sustainable criteria are localized for the suppliers’ performance evaluation in palm oil industry of Malaysia. Accordingly, 30 criteria in three different aspects (i.e. general, sustainable and resilient) are determined by statistical tests. Moreover, a hyper-hybrid model with the use of FDEMATEL (fuzzy decision-making trial and evaluation laboratory), FBWM (fuzzy best worst method), FANP (fuzzy analytical network process) and FIS (fuzzy inference system), simultaneously is developed to employ their merits in an efficient way. In this framework, regarding the outset, the relationships among the criteria/sub-criteria are obtained by FDEMATEL method. Then, initial weights of the criteria/sub-criteria are measured by FBWM method. Next, the final weights of criteria/sub-criteria considering the interrelationships are calculated by FANP. Finally, the performance of the suppliers is evaluated by FIS method. To show the applicability of this hybrid decision-making framework, an industrial case of palm oil in Malaysia is presented. The findings indicate the high performance of the proposed framework in this concept and identify the most important criteria including the cost in general aspects, resource consumption as the most crucial sustainable criterion and agility as the most important resilient criterion.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-021-12491-y.

Sustainability prioritization of sewage sludge to energy scenarios with hybrid-data consideration: a fuzzy decision-making framework based on full consistency method and fusion ranking model.

This work proposed a novel mathematical framework for the sustainability assessment of sewage sludge to energy (SStE) scenarios, by resorting to fuzzy multi-criteria decision-making (MCMD) methods. In which, an evaluation system including twelve criteria from four dimensions was introduced, while the fuzzy triangular number (TFN) was used to address the hybrid-data issue in the decision-making. More importantly, four fuzzy MCDM methods were used to make the following methodological contributions: (1) the fuzzy full consistency method (FUCOM) was extended into uncertain conditions to determine the weights easily and reliably, which preserves the consistency in ambiguous, subjective judgments; (2) a novel TFN-based fusion ranking model was developed by aggregating three fuzzy MCDM approaches, which not only takes the hybrid data as input information for decision-making (by combining the TFN) but also promotes the confidence in final prioritization (by reconciling different sequences). Four illustrative SStE scenarios were studied to test the feasibility of the model. Besides, the effectiveness and advantages of the model were verified by results comparison and discussion.

An Invasive Weed Optimization-Based Fuzzy Decision-making Framework for Bridge Intervention Prioritization in Element and Network Levels

Recently, the number of deteriorating bridges has drastically increased. Furthermore, tight maintenance budgets are cut down, imposing escalating adverse implications on the safety of bridges. This state of affairs entails the development of decision support systems for the effective management of bridges within the allocated budget. As such, this study introduces an invasive weed optimization-based fuzzy decision-making framework designated for bridge intervention prioritization in both element and network levels. The proposed decision-making platform encompasses three main tiers. The first tier is an optimized fuzzy analytical network process model that aims at computing the weighting vector of the bridge defects, namely corrosion, delamination, cracking, spalling and scaling. In this model, a genetic algorithm optimization model is formulated to improve the consistencies of judgment matrices through circumventing the imprecisions encountered by the classical judgment assignment. The second tier encompasses establishing an integrated bridge deck condition assessment model capitalizing on ground-penetrating radar and inspection reports. In it, the severities of the bridge defects are demonstrated in the form of fuzzy membership functions to address the inherent uncertainties of inspection. Subsequently, a variable-length invasive weed optimization model is structured to automatically calibrate the fuzzy membership functions. The third model is designed for structuring a bridge maintenance decision-making strategy stepping on the integrated condition index. The capabilities of the proposed framework were validated through several levels of comparisons. For instance, it significantly outperformed some of the current condition assessment models. Additionally, it inferred that the thresholds separating the four categories of the integrated bridge deck condition index are 75.651, 67.769 and 60.318.

Fuzzy decision-making framework for treatment selection based on the combined QUALIFLEX–TODIM method

ABSTRACTTreatment selection is a multi-criteria decision-making problem of significant concern in the medical field. In this study, a fuzzy decision-making framework is established for treatment selection. The framework mitigates information loss by introducing single-valued trapezoidal neutrosophic numbers to denote evaluation information. Treatment selection has multiple criteria that remarkably exceed the alternatives. In consideration of this characteristic, the framework utilises the idea of the qualitative flexible multiple criteria method. Furthermore, it considers the risk-averse behaviour of a decision maker by employing a concordance index based on TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method. A sensitivity analysis is performed to illustrate the robustness of the framework. Finally, a comparative analysis is conducted to compare the framework with several extant methods. Results indicate the advantages of the framework and its better performance compared with the extant methods.