Multi-criteria Decision-making Tool Research Articles

Material Selection Framework for Lift-Based Wave Energy Converters Using the Fuzzy Technique for Order Preference by Similarity to Ideal Solution

Material selection is a crucial aspect in the design of reliable, efficient and long-lasting wave energy converters (WECs). However, to date, the development of tailored methodologies applied to the material selection of WECs remains vastly unexplored. In this paper, a material selection framework for the case of lift-based WECs is developed. The application of the methodology is demonstrated with the hydrofoils of the device. Offshore steel, high-strength offshore steel, aluminium alloys, and carbon- and glass-fibre-reinforced composites are considered and evaluated subject to relevant criteria for wave energy converters, namely structural reliability, hydrodynamic efficiency, offshore maintainability, total manufacturing cost and environmental impact. Candidate materials are assessed via fuzzy TOPSIS for three scenarios of the life cycle of the WEC: conceptual, commercial and future projection stages. Results show that the choice of optimal materials could change from present to future and that multi-criteria decision-making tools aided by a fuzzy approach are useful design tools for novel WECs when field data are scarce. Hence, methodologies such as the ones presented in this work can help in reducing the probability of mechanical failures of emerging WEC technology.

Sustainable stationary hydrogen storage application selection with interval-valued intuitionistic fuzzy AHP

The transition to a hydrogen-based economy necessitates a comprehensive evaluation of different hydrogen storage options, considering their sustainability performance. This study innovatively applies the Interval-Valued Intuitionistic Fuzzy Analytic Hierarchy Process (IVIF-AHP) to evaluate and compare four hydrogen storage options: Compressed Hydrogen Gas (CHG), Cryogenic Liquid Hydrogen (CLH), Metal Hydride (MH), and Underground Hydrogen (UH). The evaluation criteria are derived from four dimensions of sustainability: economic, environmental, social, and technical performance, each further decomposed into sub-criteria. The study's novelty lies in using a novel intuitionistic fuzzy AHP, offering a more nuanced and robust understanding of the trade-offs between the various options and effectively capturing the vagueness and subjectivity inherent in human decision-making. Through this methodology, CHG emerged as the most promising option with a preference score of 0.487, closely followed by UH with a score of 0.453. The lowest preference score was accorded to MH, with a score of 0.301. These quantitative insights underscore the relative sustainability performance of each technology under the defined criteria. The findings contribute to the growing body of literature on sustainable hydrogen storage, providing policymakers and practitioners with a multicriteria decision-making tool that captures the complexity of sustainability considerations. This study underlines the critical role of holistic, multicriteria evaluations in advancing sustainable hydrogen storage. It encourages further exploration and validation of its approach in different contexts and with updated technological advancements.

Optimal energy mix in relation to multi-criteria decision-making (MCDM), review and further research directions

The need for a transition to a society that will meet its energy needs from local resources and with minimal negative environmental impact is no longer presented as an option but as a necessity. Energy resources are limited, and it is necessary to ensure that they are properly used and managed in a sustainable manner. Optimal redistribution of energy supply from different energy generation and distribution options to end users within the local community is called the energy mix. What sets itself the task is to find this way of sustainable use of all available energy and energy types from local communities, different regions to the entire country. This is not a simple task, because it includes many variables that must all be considered. Therefore, it is necessary to classify and define universal criteria (sustainability), which describe ways of supplying energy and energy in a particular locality. The criteria mainly describe energy needs, availability of energy resources, existing technologies, economic and environmental indicators, qualitative and quantitative values of different energy supply options. This paper aims to review the state of play in optimal energy mix research in relation to the local community. Also, this paper provides an overview and importance of the application of MCDM methods in this area. As a way out, in this paper we propose conclusions, directions and research opportunities in the field of seeking the optimal energy mix supply for local community, region or state, connected to the importance MCDM tools.

APPLICATION OF ANALYTIC HIERARCHY PROCESS INDECISION MAKING OF PROCESSED BANANA PRODUCTS FOR COMMUNITY ENTERPRISES

Bananas are one of the most produced and consumed fruit crops in the world. However, producers, especially farmers and small businesses, are currently facing many challenges in the market, particularly competitiveness. This article aims to analyze factors that influence the decision-making process of processing banana products for community enterprises. The selection of the best-processed banana products according to the different criteria uses the Analytic Hierarchy Process (AHP) as a multi-criteria decision-making support tool. A case study of community enterprises that process banana products in Prachinburi Province, Thailand is presented. The criteria and priorities are considered based on the opinions of the community enterprises. There are five main factors: main raw materials, readiness for production, profitability and marketing channels, storage conditions, and environmental and societal impacts. The alternatives were divided into two cases: unripe and ripe bananas. The results show that the main factor with the highest priority in processing banana products is the readiness for production. For overall results, the banana chip is the best choice to produce if the supply is unripe bananas. If the supply is ripe bananas, the dried banana is the best choice to produce. These results provide a guideline for decision-making in banana product processing, which helps determine the best option for complex problems.

Optimal Selection of Cotton Fabrics: A WASPAS Method Approach

Always used for cotton-rich materials. WASPAS Cotton Fabric Selection in this paper, “the WASPAS method, a relatively fresh and computationally powerful MCDM (Multi-Criteria Decision Making) tool”, is suggested to rank ten candidate cotton fabrics according to four cloth properties such as: “cover, thickness, area density, and permeability”. The suggested fabrics are evaluated and chosen in order to achieve the best thermal comfort characteristics. “Sample Number 3 ranks first (best option) with the highest evaluation score of 0.95281, while Sample Number 6 ranks tenth with the lowest evaluation score of 0.50685.” The proposed method's ranking results demonstrate a substantial agreement in ranking with previous approaches, as evidenced by the extremely high standard coefficients of correlation. With rank coefficients of correlation higher than 0.90, the ranking methods provided by the four hypothetical weight sets have the highest degree of agreement. Furthermore, even if the original making choices team is changed, that will be no rank reversal. As a result, sensitivity analyses based on altering criterion weights and the impact of dynamic matrices improve the proposed approach's stability and robustness in terms of ranking success.

Ascertainment of Hydropower Potential Sites Using Location Search Algorithm in Hunza River Basin, Pakistan

The recent energy shortfall in Pakistan has prompted the need for the development of hydropower projects to cope with the energy and monetary crisis. Hydropower in the northern areas is available yet has not been explored too much. Focusing on the sustainable development goal (SDG) “Ensure access to affordable, reliable, sustainable and modern energy”, thirteen proposed sites were selected from upstream to downstream of the Hunza River for analysis. The head on all the proposed sites was determined by taking the elevation difference between the proposed turbine and the intake at all sites. The discharge on all proposed ungauged sites was determined using ArcGIS for watershed delineation and the area ratio method along with Soil Conservation Service–Curve Number (SCS-CN) by using gauged data of Hunza River provided by Water and Power Development Authority (WAPDA) Pakistan at Daniyor bridge Gilgit, Shimshal and the Passo tributaries of Hunza River. The Location Search Algorithm (LSA) approach used a multi-criteria decision-making tool (MDM) to make a decision matrix considering the location and constraint criteria and then normalizing the decision matrix using benefit and cost criteria, the relative weights were assigned to all criteria using a rank sum weighted method and the sites were ranked on the basis of the final score. The results revealed that Hunza River has a significant hydropower potential and based on the final score in the LSA approach, proposed site 13, site 4 and site 9 were concluded as the most promising sites among proposed alternatives. The proposed methodology could be an encouraging step for decision makers for future hydropower development in Pakistan.

Parametric analysis of erosion wear of sponge iron slag-filled ramie–epoxy composites using Taguchi and preference selection index methods

The present work reports on the development of a novel set of ramie–epoxy composites filled with sponge iron (SI) slag (0–30 wt.%) and on their response to solid particle erosion wear under different test conditions. SI slag is a by-product generated during the processing of SI to steel in arc or induction furnace. While these composites are fabricated using the conventional hand layup technique, the erosion tests are carried out using an air jet erosion tester following ASTM G76 test standard as per Taguchi's L27 orthogonal array. Mechanical characterization of ramie–epoxy composites suggests that the incorporation of SI slag improves their tensile strength by about 52%, flexural strength by 10% and micro-hardness by about 30%. A parametric appraisal of the erosion response carried out using Taguchi method reveals that the filler content, impact velocity and impingement angle are the most significant control factors influencing the erosion rate of these composites. The individual effects of different factors in wider ranges (impact velocity 25–125 m/s, filler content 0–30 wt.% and impingement angle 30–90°) are ascertained by conducting steady-state erosion experiments in controlled conditions. Preference selection index method, which is an effective multi-criteria decision-making tool is used to select the best candidate among all composites based on different physical, mechanical and wear test results. It is found that the composite with 30 wt.% SI slag content displays the optimal combination of properties. The worn morphologies of the eroded surfaces of the composites are studied and analyzed using electron microscopy to identify possible mechanisms causing the wear loss.

Interactive Internet Framework Proposal of WASPAS Method: A Computational Contribution for Decision-Making Analysis

Concerning the development of computational tools and solutions as a decision-making aid, this paper presents the results of the waspasWEB project, which strives to provide decision-makers with a readily accessible mechanism to employ the weighted aggregated sum product assessment (WASPAS) method. The social contribution of the project encompasses the development of a user-friendly and publicly accessible internet tool, as well as a package launched on the Comprehensive R Archive Network (CRAN) to serve the community of users of the R language. The use of operational research methodologies is crucial to justify decisions, and this effort seeks to advance the adoption of such methodologies, offering managers, researchers, and the general public an intuitive and easily accessible multi-criteria decision-making tool. In this way, we present the technical specifications, usability, and interactivity of the user with the computational platform, being validated its viability through a hypothetical case study. At the end of the research, it exposes the limitations and feasibility of the proposed computational model along with future research.

A grey ordinal priority approach for healthcare waste disposal location selection

PurposeIncreasing public consciousness and demand for sustainable environment make selection of a safe location for effective disposal of healthcare waste (HCW) a challenging issue. This problem becomes more complicated due to involvement of multiple decision makers having varying knowledge and interest, conflicting quantitative and qualitative evaluation criteria, and presence of several alternative locations.Design/methodology/approachTo efficiently resolve the problem, the past researchers have already coupled different multi-criteria decision-making tools with uncertainty models and criteria weight measurement techniques, which are time-consuming and highly computationally complex. Based on involvement of a group of experts expressing their opinions with respect to relative importance of criteria and performance of alternative locations against each criterion, this paper proposes application of ordinal priority approach (OPA) integrated with grey numbers to solve an HCW disposal location selection problem.FindingsThe grey OPA can simultaneously estimate weights of the experts, criteria and locations relieving the decision makers from complicated computational steps. The potentiality of grey OPA in solving an HCW disposal location selection problem is demonstrated here using an illustrative example consisting of three experts, six criteria and four alternative locations.Originality/valueThe derived results show that it can be employed to deal with real-time HCW disposal location selection problems in uncertain environment providing acceptable and robust decisions. It relieves the experts from pair-wise comparisons of criteria, normalization of data, identification of ideal and anti-ideal solutions, aggregation of information and so on, while arriving at the most consistent decision with minimum computational effort.

How digitalization transforms the traditional circular economy to a smart circular economy for achieving SDGs and net zero

Promoting and achieving sustainable development goals (SDGs) and net zero will be a high priority among companies and institutions. Several strategies are being developed to motivate and integrate SDGs and net zero-related approaches in companies, among which the circular economy (CE) is gaining momentum due to its documented impact on the elements of the SDGs and net zero. In fact, recent studies began to examine the relationship between CE, SDGs and net zero through different perspectives and with different areas of application. Although this relationship is primarily for the implementation of the SDGs and net zero targets, very few studies demonstrate concerns about CE-SDG-net zero relationships, specifically with smart CE. Although the traditional CE influences the SDGs and net zero positively, the traditional approach remains insufficient in several areas, including the lack of real-life information, where most CE practices and principles reside. To address this gap, a smart CE has been established by researchers to unleash the potential of achieving SDGs and net zero. To explore the smart CE and to tackle the existing literature gap, this study focuses on identifying the influence of smart CE with a focus on achieving SDGs and net zero with a single textile case study. The study is divided with four phases, as follows. The first phase attempts to select the best and most feasible CE practices that have a major impact on SDGs and net zero. The second phase understands the smart integrated success factors for adopting selected CE practices. The third phase evaluates and analyzes the overall common success factors for selected CE practices, and finally, the fourth phase validates the available results from the previous phases through feedback from various reliable sources. A case study methodology has been used in this study to understand the core of the research, while it should be noted this study is groundbreaking work in the field of research. Two different multi-criteria decision-making tools (MCDMs) have been used, namely the Best Worst Method (BWM) and the grey DEMATEL for selecting the best and most feasible CE practices and for evaluating the commonly collected success factors. Two theories (CSF theory and TOE theory) have been used to strengthen the study's theoretical background and contribution. To assist practitioners, the available results are discussed in a way that understands the relationship between the chosen CE practices and their considered influential success factors with SDGs and net zero through digitalization.

Assessing alternatives of including social robots in urban transport using fuzzy trigonometric operators based decision-making model

Current trends point to a not-too-distant future with qualitatively advanced interactions between humans and social robots. It is critical to consider the possibility of forming meaningful social relationships with robots when defining the future of human-robot interactions, as well as studying how these interactions will evolve to the point where humans are unable to distinguish between humans and robots in urban transportation. In this study, the advantages of using social robots in urban transportation are prioritized by using a multi-criteria decision-making tool, which consists of two consecutive stages, namely: i) a novel fuzzy sine trigonometry based on the logarithmic method of additive weights (fuzzy ST-LMAW) that is proposed to calculate the criteria weights; ii) a nonlinear fuzzy Aczel-Alsina function based the weighted aggregate sum product assessment (fuzzy ALWAS-WASPAS) that is developed to select and rank the alternatives. The proposed model enables flexible nonlinear processing of complex and uncertain information encountered in real applications. A case study is developed to rank three alternatives with twelve sub-criteria grouped into four aspects using the proposed method. The results show that the most advantageous alternative is to replace people with social robots as safety drivers in level four autonomous vehicles due to their possible impact on transportation.

Complex spherical fuzzy Aczel Alsina aggregation operators and their application in assessment of electric cars

The multi-criteria decision-making (MCDM) tool is a robust decision-making technique utilized in several fields like networking, risk management, digital analysis, cybercrime investigation, artificial intelligence, waste management enterprises and many other selection criteria. Complex SFS (CSFS) is a new edition of the spherical fuzzy set (SFS) that offers substantial information about any item in terms of amplitude and phase terms in a wider range of real terms. Complex SFS (CSFS) can be an extension of the spherical fuzzy set (SFS). The Aczel-Alsina aggregation tools are more appropriate aggregation operators (AOs), and they are used to conquer the impact of inconsistent and uncertain data. In this paper, we reveal some new approaches based on Aczel-Alsina aggregation tools under consideration of Complex Spherical Fuzzy (CSF) information. These new approaches include the CSF Aczel-Alsina weighted average (CSFAWA) operator, and the CSF Aczel-Alsina ordered weighted average (CSFOWA) operator. In addition to this, we also introduce a list of novel techniques by making use of the theory of Aczel-Alsina aggregation tools such as CSF Aczel-Alsina weighted geometric (CSFAWG) and CSF Aczel-Alsina ordered weighted geometric (CSFOWG) operators. To demonstrate the resilience and efficacy of the approaches that have been mentioned, we will examine a few exceptional examples and remarkable properties of the methodology that we have devised. In addition, a characterization is provided for an approach to the MCDM issue using the CPF information system. We use the example of electric automobiles as a case study to illustrate the uniformity and dependability of the methodology that we have established. This example was chosen because of the high cost of fuel and the present economic challenges that are being encountered by families in the middle class. An empirical case study is also constructed to determine an electric car that is desirable based on the techniques that we have proposed. To evaluate the correctness and superiority of the established strategies, we compare the outcomes of previously used techniques with the AOs currently being provided.

Application of a fuzzy multi-criteria decision framework for safety-critical maritime infrastructure evaluation

This present paper is an investigation of a framework for Safety-Critical Maritime Infrastructure (SCMI) evaluation. The framework contains three Multi-Criteria Decision-Making (MCDM) tools, namely: fuzzy Step-wise Weight Assessment Ratio Analysis (SWARA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Weighted Aggregates Sum Product Assessment (WASPAS). It also contains five safety practice criteria: people's safety, property safety and monitoring capabilities, response to regular and irregular threats in a robust yet flexible manner, and breaches in physical security. The framework has four safety culture criteria: learning from experience and inter-element collaboration, lack of facility maintenance, and anticipating risk events and opportunities. Through the framework, an evaluation of the safety practices and safety culture of six Nigerian seaports is done. Then, data obtained from the ports in regard to their safety practices and culture were analysed in line with the framework identified and adopted. The results revealed that the safety of people's life is the most important safety practice and contributed about 47.90% to evaluate the SCMI. Results equally showed that the most significant safety culture is learning from experience, and accounted for approximately 53.20% in assessing the SCMI. Similarly, the TOPSIS method ranked Warri (A5) and Tin Can Island A1 as the best and worst safety practices performance, respectively. Results of analyses on the TOPSIS showed that Apapa port (A2) and Onne port (A6) had the best and worst performances correspondingly. WASPAS was also analysed. The results indicated that A6 performed the best safety practice performance, while A2 had the worst safety practice performance. Analysis of the WASPAS method was equally done. It showed that A1 had the worst safety culture performance, while A5 had the best safety culture performance. Therefore, the proposed framework could serve as a veritable tool for analysing SCMI by using safety practice and safety culture criteria.

Selection of Ideal MSW Incineration and Utilization Technology Routes Using MCDA for Different Waste Utilization Scenarios and Variable Conditions

Liability to prevent the consequences of an unhealthy situation due to accumulating toxic and hazardous emissions caused by open dumping of municipal solid waste with increasing urbanization has necessitated a renewed thinking on waste disposal. Grate-fired incineration systems were adopted by urban management in the past and present, but with criticism due to the formation of airborne emissions. Improved combustion methods like fluidized beds are now propagated because of current requirements like efficient energy recovery potential, stricter emission norms, adaptability with urban growth, adaptability to co-firing with other waste like biomass, edible oil wastes or industrial effluent, and integration with conventional energy generation. Such a comprehensive and futuristic approach is more sustainable for the community. A multi-criteria decision-making tool is used to identify the best technology option between grate combustion and fluidized bed combustion for disposing and energy recovery from waste. A total of 10 different collection and disposal options involving two combustion methods, namely, grate combustion and fluidized bed method, are considered. Utilization of the energy is done for three end uses, namely, power generation, water distillation, and district cooling. Two different regions in an arid climate zone are considered for this study under two types of scenarios, namely with recycling and without recycling. The different options are prioritized based on their overall ranking using five major performance factors.

Multicriteria Decision-Making Tools for the Selection of Biomasses as Supplementary Cementitious Materials

Using biomass ash to partially replace cement reduces the cement industry’s environmental impact and prevents these agro-industrial wastes from ending up in landfills, eroding soils, or being openly burned. This research aims to select three biomasses to produce supplementary cementitious materials (SCM) through the analytic hierarchy process, considering expert judgments from different domains. Complementary to up-to-date research, we evaluated biomasses taking into account biomass production, ash obtained from combustion, and logistics processes for supplying concrete plants with SCM. We also dealt with an industrial context instead of a laboratory one and validated our approach on a real case study using Colombian data. The results indicate experts count the technical viability of biomass (concrete properties) as the most crucial criteria, followed by the availability and transport characteristics of the waste (production criteria) and the combustion process as the least important criteria. In the baseline scenario (all experts’ judgments having the same weights), we found that cane bagasse is the best alternative, thanks to its large and highly concentrated production, even if it is not the biomass with the best pozzolanic properties. We also analyzed other scenarios in which we changed the weights of the experts’ judgments and the importance of the criteria. We found that cane bagasse, rice husk, and palm rachis remain the three biomasses selected as SCM, showing the robustness of the proposed multicriteria decision-making (MCDM) methodology. The results provide a methodological reference to appraise biomasses for SCM nationally, using a MCDM framework in a group decision-making context.

Optimal site selection for a solar power plant in Iran via the Analytic Hierarchy Process (AHP)

As a vast country with average solar radiation of 4.5 kW-hours per square meter, Iran enjoys a very high potentiality for initiating and utilizing solar systems and, in particular, electricity generation from solar power plants. Optimal site selection for solar power plants requires making multi-criteria decisions. Analytic Hierarchy Process (AHP) is one of the most comprehensive multi-criteria decision-making tools. The present study was conducted to select a regionally optimal site regarding climatic conditions using AHP and the Expert Choice software. Certain criteria and sub-criteria such as the amount of radiation, the number of sunny days, the average temperature, the amount of humidity, the amount of rainfall, the level of visibility, and the cloudiness factor were regarded. To select an optimal site using AHP and Expert Choice software, after specifying the hierarchic levels, including the target, criteria, sub-criteria, and alternatives (intended sites), pairwise comparison was made between the sets to weigh them up. The provincial centers of Iran were considered in the study and Zahedan has been selected as the best location for constructing a solar power plant. Moreover, a sensitivity analysis on the major criteria was performed and the impacts of parameter weight on the alternatives were assessed.

Evaluation of the treatment options for COVID-19 patients using generalized hesitant fuzzy- multi criteria decision making techniques

The breakout of the pandemic COVID-19 has affected numerous countries and territories worldwide. As COVID-19 specific medicines yet to be invented, at present the treatment is case specific, hence identification and evaluation of different prevalent treatment options based on various criteria and attributes are very important not only from the point of view of present pandemic but also for futuristic pandemic preparedness. The present study focuses on identifying, evaluation and ranking of treatment options using Multi Criteria Decision Making (MCDM). In this regard, the existing literature, doctors and scientist were interviewed to know the current treatment options in vogue and the scale of their importance with respect to the criteria. The criteria taken are side effect, regime cost, treatment duration, plasma stability, plasma turnover, time of suppression, ease of application, drug-drug interaction, compliance, fever, pneumonia, intensive care, organ failure, macrophage activation syndrome, hemophagocytic syndrome, pregnancy, kidney problem, age. This study extended Hesitant Fuzzy Set (HFS) to Generalized Hesitant Fuzzy Sets (GHFS). Generalized Hesitant Pentagonal Fuzzy Number (GHPFN) is developed. The properties of GHPFN are demonstrated. Two types of GHPFN has been described. The GHPFN (2nd type) along with MCDM tool Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been applied to rank the treatment options. The result of the study ranked ‘Hydroxychloroquine’ as the first alternative followed by, ‘Plasma Exchange’, ‘Tocilizumab’, ‘Remdesivir’ and ‘Favipravir’. To check the robustness and steadiness of the proposed methodology, comparative analysis and sensitivity analysis has been conducted.

Neutrosophic LOPCOW-ARAS model for prioritizing industry 4.0-based material handling technologies in smart and sustainable warehouse management systems

Industry 4.0 technologies embedded in the warehouse management system (WMS) are needed to improve the automation of material handling activities such as receiving, storing, picking, sorting, packaging, and delivering. This research aims to introduce a neutrosophic multi-criteria group decision-making tool that is intelligible in supporting the transition and upgrading of WMS with Industry 4.0-based solutions. This advanced two-stage model is based on the integration of the logarithmic percentage change-driven objective weighting (LOPCOW) method and the additive ratio assessment (ARAS) method under the type-2 neutrosophic number (T2NN) environment. In the first stage, T2NN-LOPCOW generates an objective importance vector of decision-making criteria. In the second stage, T2NN-ARAS based on the generalized weighted Heronian mean operator provides an advantageous order of Industry 4.0-based material handling technologies. T2NN-LOPCOW-ARAS brings the following novelties: ((i) to straightforwardly represent and explore interconnection levels between weights of criteria, ((ii) to provide wide-scoping insight into the stability of initial priority order, as well as a broad spectrum of flexible solutions, ((iii) to control the normalization procedure and minimize distortions due to the double-normalization backbone. The real-life case study of a logistics company from the Serbian grocery retail sector illustrates the practical applicability of T2NN-LOPCOW-ARAS. A practical evaluation framework is defined to comprehensively assess automated guided vehicles (AGVs), collaborative robotics, and drones. The sensitivity analyses show the high robustness of the proposed framework. The comparative investigation shows that T2NN-LOPCOW-ARAS is superior to the extant methods. The research findings show that AGVs are the most favorable Industry 4.0-based material handling solution.

Prediagnosis of Disease Based on Symptoms by Generalized Dual Hesitant Hexagonal Fuzzy Multi-Criteria Decision-Making Techniques

Multi-criteria decision-making (MCDM) is now frequently utilized to solve difficulties in everyday life. It is challenging to rank possibilities from a set of options since this process depends on so many conflicting criteria. The current study focuses on recognizing symptoms of illness and then using an MCDM diagnosis to determine the potential disease. The following symptoms are considered in this study: fever, body aches, fatigue, chills, shortness of breath (SOB), nausea, vomiting, and diarrhea. This study shows how the generalised dual hesitant hexagonal fuzzy number (GDHHχFN) is used to diagnose disease. We also introduce a new de-fuzzification method for GDHHχFN. To diagnose a given condition, GDHHχFN coupled with MCDM tools, such as the fuzzy criteria importance through inter-criteria correlation (FCRITIC) method, is used for finding the weight of criteria. Furthermore, the fuzzy weighted aggregated sum product assessment (FWASPAS) method and a fuzzy combined compromise solution (FCoCoSo) are used to rank the alternatives. The alternative diseases are chosen to be malaria, influenza, typhoid, dengue, monkeypox, ebola, and pneumonia. A sensitivity analysis is carried out on three patients affected by different diseases to assess the validity and reliability of our methodologies.

Towards "Good" Native Land Governance

Sarawak is the largest state in Malaysia, where two-thirds of the population are Indigenous. This study aims to evaluate, through the lens of good governance principles, the current practice of the Sarawak State’s formal land governance of lands associated with Native Customary Rights (hereafter known as Native land governance). Being quantitative in nature, this study conceptualises an evaluation framework for good governance principles as applied to Native land governance. Next, this study empirically tests out the framework by adopting a multi-criteria decision-making tool known as The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). TOPSIS analysis enables the integration of perceptions between State/private groups and Indigenous groups. The output of the TOPSIS analysis is summarised in a strength, weakness, opportunity, and threat (SWOT) format according to the TOPSIS closeness value. Unfortunately, results show that the weaknesses outnumber the strengths in Sarawak’s Native land governance. Among these issues, Indigenous respondents highlight major issues with the Sarawak land registry’s efficiency in delivering outcomes that are equitable for Indigenous land rights. This study ends with recommendations on how the state of Sarawak can move towards compliance with good governance principles in relation to lands associated with Native Customary Rights.