Ranking failure modes with pythagorean fuzzy rough VIKOR approach

Due to its superior physical qualities versus plain concrete, "fibre reinforced concrete (FRC)" has seen a rise in popularity in recent years. Fire resistance is an inherent challenge when employing FRC in civil structures and other infrastructure that is crucial strategically. The fire resistance of FRC is influenced by different fibre kinds, fibre dosages, and cementitious matrix designs, per the results of the currently available tests. This article offers a thorough analysis of current studies on FRC's firefighting. We cover the creep, shear failure, compressive, elastic modulus, percentage of elongation, hardness, and mass loss heat mechanical and physical properties of steel fibre reinforced concrete, polypropylene "fibre reinforced concrete", and hybrid fibre reinforced concrete in detail. Also, a comparison and summary of the current dynamic simulation for FRC residual attributes are provided. It establishes a rating list of compromises depending on how closely they adhere to the ideal answer. Comprehensive VIKOR, fuzzy VIKOR, modified VIKOR, and intervals VIKOR approaches, in addition to the VIKOR method, have all been established according to the kind of choice problem and the needs of the decision maker. Based on two example instances, this study analyses the ranking achievement of the initial VIKOR approach and its five modifications. The fuzzy VIKOR approach should be chosen when the periodic VIKOR method doesn't quite perform well and the knowledge in the decision situation is ambiguous. Nonetheless, the classic VIKOR is the ideal way to solve any choice problem without needlessly complicating the related mathematical procedures. The alternatives are Normal concrete, Steel fibres concrete, Macro-polypropylene fibres concrete and Micro-polypropylene fibres concrete. The evaluation parameters are Compressive strength (MPa), Tensile strength (MPa) and Tensile strength in relation to compressive strength (%). Normal concrete is got first rank, Steel fibres concrete is got third rank, Macro-polypropylene fibres concrete is got fourth rank and Micro-polypropylene fibres concrete is second rank

Since 2013, the Spanish fleet of EF-18 fighters has not received major updates nor any other jet has been proposed for their replacement. Thereby, a decision problem of interest to the Spanish Air Force is addressed in this study. In this regard, a collection of six potential alternatives (Eurofighter Typhoon, F-35 Lightning II, F/A-18 Super Hornet, Dassault Rafale, F-15 EX, and Saab 39 Gripen) and 14 criteria (3 of which being qualitative) are evaluated based on expert consensus by a combination of fuzzy logic and multi-criteria decision making (MCDM) approaches (AHP-VIKOR/AHP-TOPSIS). The weights of the criteria and the valuation of the qualitative ones (via questionnaires) were made possible by the participation of two independent groups of experts, all of them fighter pilots. Once the extraction of knowledge was carried out via a fuzzy version of a modified AHP, it was found that the two most important criteria (“air superiority over neighbor countries” and “tactical capability”) were qualitative. A comparison between the rankings of the alternatives provided by fuzzy VIKOR and fuzzy TOPSIS approaches highlights that the fuzzy VIKOR ranking appears highly influenced by the most important criteria. On the contrary, fuzzy TOPSIS presents a more compensatory behavior than fuzzy VIKOR. Given the differences observed when comparing such rankings of alternatives, a triple sensitivity analysis was conducted to ensure robustness. Our results highlight the Dassault Rafale as the most promising option for replacement. This finding suggests its potential as a benchmark for future fighter selection and next-generation fighter planning.

This paper addresses the problem of evaluating vehicle failure modes efficiently during the driving process. Generally, the most critical factors for preventing risk in potential failure modes are identified by the experience of experts through the widely used failure mode and effect analysis (FMEA). However, it has previously been difficult to evaluate the vehicle failure mode with crisp values. In this paper, we propose a novel hybrid scheme based on a cost-based FMEA, fuzzy analytic hierarchy process (FAHP), and extended fuzzy multi-objective optimization by ratio analysis plus full multiplicative form (EFMULTIMOORA) to evaluate vehicle failure modes efficiently. Specifically, vehicle failure modes are first screened out by cost-based FMEA according to maintenance information, and then the weights of the three criteria of maintenance time (T), maintenance cost (C), and maintenance benefit (B) are calculated using FAHP and the rankings of failure modes are determined by EFMULTIMOORA. Different from existing schemes, the EFMULTIMOORA in our proposed hybrid scheme calculates the ranking of vehicle failure modes based on three new risk factors (T, C, and B) through fuzzy linguistic terms for order preference. Furthermore, the applicability of the proposed hybrid scheme is presented by conducting a case study involving vehicle failure modes of one common vehicle type (Hyundai), and a sensitivity analysis and comparisons are conducted to validate the effectiveness of the obtained results. In summary, our numerical analyses indicate that the proposed method can effectively help enterprises and researchers in the risk evaluation and the identification of critical vehicle failure modes.

BackgroundGlobal healthcare systems face enormous challenges due to the ageing population, demanding novel measures to assure long-term efficacy and viability. The expanding senior population, which requires specialised and efficient healthcare solutions, emphasises the importance of improving healthcare sustainability. Recognising the importance of personalised healthcare recommendations in improving patient outcomes as well as facility sustainability, this study tackles the crucial need for targeted treatments to help the elderly navigate the complicated healthcare landscape.ObjectivesThrough the integration of automation with the Fuzzy VIKOR approach as well as Electronic Health Record (EHR) data, this work seeks to create an automated decision-making mechanism that improves personalised healthcare suggestions for the elderly. By using automated data-driven observations, Fuzzy VIKOR to handle decision-making uncertainty as well as the clinical depth of EHR data, the primary objective is to increase the efficacy and accuracy of treatment choices. In order to guarantee that treatment recommendations are not only medically beneficial but also in line with each patient's needs and preferences, this research aims to close the gap between automated intelligence as well as patient-centered care.MethodThe Fuzzy VIKOR approach is used with Electronic Health Record (EHR) data to establish a strong framework for personalised healthcare recommendations. AI techniques are employed to enhance data processing, while Fuzzy VIKOR is used to control uncertainty in decision-making, whereas EHR data gives comprehensive clinical insights. The combination of these aspects enables the creation of a system that compensates for uncertainties in medical knowledge and patient preferences, culminating in a ranked array of treatment alternatives customised to the difficulties of healthcare decision-making for the aged.ResultsThe study shows how the proposed methodology improves therapy selection for senior populations. By combining AI-powered analysis, Fuzzy VIKOR, and EHR data, the study provides a refined and personalised approach to healthcare recommendations, providing ranked treatment alternatives based on individual characteristics and preferences. The findings demonstrate the potential of this strategy to handle healthcare complexity and contribute to the developing era of precision medicine.ConclusionFinally this study makes an important contribution to the continuing discussion about the sustainability of healthcare for the elderly. The combination of AI-driven methodologies, the Fuzzy VIKOR technique and EHR data offers a promising approach to improving therapy selection in the setting of precision medicine. By accepting personalised healthcare recommendations, this study anticipates a future in which elderly people's unique characteristics and preferences are central to decision-making processes, maintaining not only better patient outcomes but also the long-term viability and sustainability of healthcare services for the elderly.

Supplier selection is not an easy process as it typically involves multiple criteria and it requires human judgment. Therefore, there is a need to have a better system for supplier selection due to uncertainty and vagueness that exist in dealing with the multiple criteria decision making (MCDM) problem. This study is aimed to identify the important criteria for the selection of the best pharmaceutical raw materials supplier for XYZ Pharmaceutical Manufacturing company by using Fuzzy Analytical Hierarchy Process (AHP) and Fuzzy VIKOR approaches. Furthermore, this paper also presents the comparison results between the two methods in determining the best supplier. Fuzzy theory was used since it provides a right tool to encounter the uncertainties and complexity of decision making environment. This study investigated eight alternative suppliers based on seven criteria evaluated by two decision makers from the company. This study has successfully identified the top three most important criteria for the selection of supplier, namely, Regulatory Compliance, Price, and Product Variety. This study highlighted that Regulatory Compliance is a new important criterion for the pharmaceutical company to consider in future supplier selection. Based on the alternative results of Fuzzy AHP, it was found that the top three most important supplier were A5, A3 and A1. While based on the alternative results of Fuzzy VIKOR, it showed that the top three most important supplier were A5, A3 and A7. The comparison results between the two methods have shown that the Fuzzy VIKOR method is more suited to the problem of supplier selection.

Fuzzy VIKOR approach for assessing the vulnerability of the water supply to climate change and variability in South Korea

Accident rates in academic laboratories are increasing rapidly day to day. The incorporation of complex activities with the inclusion of modern technologies and human behavior during experiments often makes it difficult to manage health and safety risks. Failure modes and effect analysis (FMEA) is widely used risk assessment technique that consider (03) risk parameters which include severity (S), occurrence (O), and detection (D), and calculate a risk priority number (R.P.N), which facilitates in failure modes ranking. However, there are some shortcomings associated with this method. This study aims to propose a framework for prioritizing the health and safety risks (failure modes) in an experiment using a widely used Multi-criteria decision-making method, i.e., Fuzzy VIKOR supported by criteria objective weights determined using the Shannon entropy concept. The fuzzy VIKOR method is employed to rank the failure modes based on the maxi group utility and the minimum individual regret. In this study, an experiment related to preparing rock core samples is taken as an example for implementing the proposed technique. Core sample preparation is one of the most common and dangerous experiments in the petroleum industry. The performance of the experiment allows rock sample preparation of desired shape and size to be tested for its hydrocarbon producing potential. The final results indicate that applying the proposed method in a fuzzy environment can efficiently prioritize the experiment’s health and safety risks, which is further considered for the implementation of control measure prioritization. The proposed approach can be applied to other experiments with the same or different domains.

E-commerce has a significant growth rate in recent years. As a result, the demand of designing E-commerce websites is also increasing at the high pace. Hence, choosing E-commerce design platforms is crucial important. However, selecting an appropriate platform is definitely a complicated task. Fuzzy based approaches are suitable methods for handling these vague decision situations. In this paper, we apply fuzzy VIKOR approach to evaluate and choose the suitable E-commerce design platforms. Fuzzy VIKOR method can solve uncertainties in this issue by using fuzzy linguistic variables to rank platforms. Fuzzy VIKOR method is a famous multi-criteria decision making technique which computes the difference between ideal and anti-ideal alternatives. A numerical simulation is conducted to demonstrate the process the proposed method. The results of this study can assist companies in selecting the suitable E-commerce website design platforms.

Big Data and predictive analysis is considered a major revolution in the history of data sciences. It has been instrumental in transforming the discipline of supply chain management. Recently a rising trend is observed in terms of research output in the area of Big Data and its application in supply chain management, particularly under top SCM journals which clearly indicates the importance of the subject. The current paper argues for the use of fuzzy VIKOR method to solve the Big Data analyst selection problem. Fuzzy VIKOR is a unique multi criteria decision making technique and here it uses linguistic variables with triangular fuzzy numbers to estimate weights of the criteria and ranking of each Big Data analyst. The findings suggest that technical knowledge, intellectual curiosity and business acumen are the strongest influential criteria and must be present in the candidate for the Big Data and analytics job in purchasing function. The findings may assist procurement heads in understanding the criticality related to Big Data and predictive analysis and further careful selection of candidates in the operations team.

The product concept evaluation plays a very important role in product development. It can be considered as multi-criteria decision making (MCDM) problem. In a MCDM problem, a decision maker has to choose the best alternative that satisfies the evaluation criteria among a set of candidate solutions. This paper introduces a compromise MCDM evaluation model which combines fuzzy DEMATEL and VIKOR methods. The criteria weights are determined based on separate cause-effect assessment of a group of experts using the fuzzy DEMATEL approach. The resulting criteria weights are applied to the decision analysis where the fuzzy VIKOR approach is adopted which determines a compromise solution by using utility weight.

Agile manufacturing (AM) system focuses on the customised production with shorter lead time in a cost effective manner. Conceptual design is a vital phase of product development and AM obliges towards it. Selecting appropriate conceptual design often involves consideration of multiple factors, multi criteria decision making (MCDM) techniques facilitates in resolving this problem. In this study, hybrid fuzzy analytical hierarchical process (AHP) and fuzzy VIKOR approach was used for selecting the best conceptual design of instrument panel (IP). The article focuses on development of various concept designs for IP considering the recent manufacturing and technological aspects. Appropriate agile criteria were identified through several brainstorming sessions conducted among the stakeholders of the organisation. Fuzzy AHP model was used to compute the weights of the proposed conceptual designs and fuzzy VIKOR was used to select the best suited conceptual design. The case study was also practically validated in a manufacturing scenario.

The ever increasing demand and depletion of fossil fuels along with environmental concern has prompted search for alternate fuels. The selection of optimum biodiesel with the appropriate blend for the IC engine plays a vital role in the energy sector. This paper describes the application of novel hybrid multi-criteria decision making (MCDM) technique to evaluate the optimum fish oil biodiesel blend. Fuzzy TOPSIS and fuzzy VIKOR are used to evaluate the best blend, where fuzzy is to determine the relative weights of the criteria, TOPSIS and VIKOR has been used for obtaining the final ranking of alternatives. A four stroke single cylinder, constant speed, direct injection with a rated output of 4.4 kW was used for exploratory analysis. The ranking performance of both the methods is also compared with each other and selected B40 is the best blend to operate the engine. The proposed MCDM models are simple, precise and efficient tool for the decision makers to identify the suitable blend among the alternatives. [Received: March 4, 2017; Accepted: March 7, 2018]

Sustainable and judicious exploitation of marine resources has been a highly debated topic in recent times, in particular relation to the growing environmental impacts such as diminishing fish stock, changes in climate leading to uncertain economic profitability in marine capture fisheries. Developing countries like India suffer the most due to the multi-species nature of the fisheries and its biodiversity. Indeed, there is a dire need for an ideal fishing gear to sustainably reap the marine fish without damaging the ecosystem. This study from Palk Bay, India, throws light on the economic aspects of major fishing methods and its compatibility towards sustainable harvests. Results demonstrate that the economic net profit US$ 40452 year–1 was higher for trawler than those of longliner (US$ 39738 year–1) and gillnetter (US$ 19645 year–1) with no significant difference observed between trawler and longliner (P>0.05). But the profit margin (20%) was low for trawler compared to longliner (25%); because; of the total gross revenue, the fuel cost was 10% higher for trawler compared to longliner. Profit of the fishing fleet relies heavily on the input of fossil fuel which also significantly affects the environment. The energy efficiency in terms of fuel use intensity (FUI) was estimated to be 3.5 and 2.7 fold greater for longliner and gillnetter, respectively compared to trawler. The lowest of 0.520 tons of CO2 was emitted per ton of fish caught in a longliner whilst the same, amounted to 0.684 tons of CO2 for gillnetter and 1.823 tons CO2 for trawler. The fuzzy VIKOR index computed using different performance indicators of fishing fleets pointed long liner to be ideal. Result of the scenario analysis advocates that 40% existing trawler replacement aids to save fuel combustion of 121.33 million litre year–1 that could prevent emission of 9.5 million tons of CO2 per annum during fishing which in turn would largely serve to reduce the climate change effect. Ground truthing for the same has been done for the conversion of trawler to longliner. The average catch amounted to 10 to 12 tons per trip (15 days per trip) generating a net profit of US$ 8065 to 11290 for the same.

In this paper, we use a combination of fuzzy analytic hierarchy process and fuzzy VIKOR approaches from the domain of multi-criteria decision making to formulate trust. The VIKOR method was originally developed to solve decision problems with conflicting and non-commensurable criteria. The VIKOR method assumes that compromise is acceptable for conflict resolution and the decision maker wants a solution that is the closest to the ideal. In this method, the alternatives are evaluated according to all the established criteria. By using this method, we can rank the trustee alternatives and determine the compromise solution. Combining fuzzy logic with this approach lets us model the vague, uncertain and subjective nature of trust. The proposed formulation gives a structural way to make decisions to trust an entity, which is required in computational domains. As a result, it is possible to make recommendations in a referral trust scheme and aggregate the trust scores assigned by the recommenders. Through an illustrative example and by performing experimental evaluations, we have shown that the proposed method can make better trust decisions by taking into account the subjective preferences of the trustor about the existing trustworthiness criteria.

The ever increasing demand and depletion of fossil fuels along with environmental concern has prompted search for alternate fuels. The selection of optimum biodiesel with the appropriate blend for the IC engine plays a vital role in the energy sector. This paper describes the application of novel hybrid multi-criteria decision making (MCDM) technique to evaluate the optimum fish oil biodiesel blend. Fuzzy TOPSIS and fuzzy VIKOR are used to evaluate the best blend, where fuzzy is to determine the relative weights of the criteria, TOPSIS and VIKOR has been used for obtaining the final ranking of alternatives. A four stroke single cylinder, constant speed, direct injection with a rated output of 4.4 kW was used for exploratory analysis. The ranking performance of both the methods is also compared with each other and selected B40 is the best blend to operate the engine. The proposed MCDM models are simple, precise and efficient tool for the decision makers to identify the suitable blend among the alternatives. [Received: March 4, 2017; Accepted: March 7, 2018]