Fuzzy Linguistic Variables Research Articles

Performance measurement of construction suppliers under localization, agility, and digitalization criteria: Fuzzy Ordinal Priority Approach.

The suppliers’ performance plays a vital role, with a domino effect, in project success, organizational competitiveness, protecting supply chain and construction industry from disruptions and PESTEL risks (political, economic, social, technological, environmental, and legal). Therefore, measuring the performance of the construction suppliers has become the primary focus of project-oriented organizations and the core of business decision-making, especially during global megatrends. The question that may arise here is, “How can the performance of the construction suppliers be determined under uncertainties considering the post-COVID-19 era?” Organizations need eligible suppliers for the rapid recovery of the supply chain and construction sector at this critical stage. Given the importance of the issue, this study aims to propose a novel approach for measuring the performance of construction suppliers using the fuzzy ordinal priority approach (OPA-F). OPA-F is a recent development in multiple criteria decision-making (MCDM) that can determine the criteria weights for performance measurement using fuzzy linguistic variables. We do not always have access to a complete data set in real-world situations and business environments. Nevertheless, OPA-F can handle this dilemma, even with incomplete input data. This research intends to consider three main aspects of the construction suppliers, known as (L-A-D) capabilities, including localization, agility, and digitalization. In this regard, we bring up a case study from the construction industry to demonstrate the application of the proposed framework. The findings show that the most critical criterion is “digitalization” for the case study. This criterion covers “supply chain automation” and “virtualization and dematerialization” of services/products. The proposed approach is practical and straightforward, particularly for academicians and decision-makers; it can also incorporate uncertainties.

Effectiveness of Traffic Management at Kajang SILK Highway

The traffic management centre is a place of operational monitoring and supervision activities at all highways under the Malaysian Highway Authority. The purpose of this centre is to help gather and disseminate information related to traffic to the users. The information presented by them is accurate and constantly updated from time to time. However, are highway users in Malaysia aware of the existence of this traffic management centre? How can traffic management centre help them to determine their travel times and fast routes especially during the festive seasons? Therefore, researchers have outlined some of the objectives to assist in obtaining information about this study. The main objective of this study is to measure the level of consumer awareness of the highway traffic management centre. In addition, the second objective of this study is to examine the factors causing traffic congestion. Besides, researchers also want to investigate the drivers’ attitudes during the congestion which will lead to an accident and congestion and thus propose measures to raise the level of awareness and measures that can be taken to reduce traffic congestion. The technique used to carry out this study is a fuzzy conjoint model. The questionnaire distributed will be administered by the researcher himself. The level of consumer awareness will be measured through the likert scale which uses fuzzy linguistic variables such as one to five scale (never, sometimes, uncertain, ever, always). This study is conducted on users who use the Kajang SILK highway through the Toll Plaza Sg. Ramal. At the end of the study, the researcher hoped that this study would help the authorities, especially the Malaysian Highway Authority and other management in improving the traffic management centre.

Integration of fuzzy logic in the SEIRD compartmental model for the analysis of intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics

Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we have proposed a fuzzy rule-based Susceptible-Exposed-Infectious-Recovered-Death ([Formula: see text]) compartmental model considering a new dynamic transmission possibility variable as a function of time and three different fuzzy linguistic intervention variables to delineate the intervention and transmission heterogeneity on SARS-CoV-2 viral infection. We have analyzed the datasets of active cases and total death cases of China and Bangladesh. Using our model, we have predicted active cases and total death cases for China and Bangladesh. We further presented the correspondence of different intervention measures in relaxing the transmission possibility. The proposed model delineates the correspondence between the intervention measures as fuzzy subsets and the predicted active cases and total death cases. The prediction made by our system fitted the collected dataset very well while considering different fuzzy intervention measures. The integration of fuzzy logic in the classical compartmental model also produces more realistic results as it generates a dynamic transmission possibility variable. The proposed model could be used to control the transmission of SARS-CoV-2 as it deals with the intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics.

A novel approach to risk analysis of automooring operations on autonomous vessels

To compensate for the lack of historical incident data available for the risk assessment of unmanned ships, this study adopted failure mode and effects analysis (FMEA) to analyse potential failures, andbased on experts’ experience, ranked them in terms of the likelihood of failure occurrence, severity of consequences and difficulty of detection. First, fuzzy linguistic variables were introduced to indicate experts’ preferences when addressing the inherent uncertainty of unmanned cargo ships' failure modes. Second, in the expert survey, the indicator of hesitance was introduced to reflect experts' degree of familiarity with the evaluated failure modes. Third, to overcome the inability of FMEA to sort failure modes of the same risk value, this study adopted a technique for order of preference by similarity to ideal solution (TOPSIS) to further rank the identified failure modes. Finally, 19 failure modes for the automooring system were selected to demonstrate how the assessment was conducted. The empirical analysis results indicated that the developed model can effectively screen out failure modes with high-level risks. This study also provides risk control options (RCOs) and offers a new and practical approach to the risk assessment of unmanned cargo ships.

An integrated fuzzy AHP approach for optimisation of theory of failures of multi-directional composite laminates

Owing to their improved engineering characteristics, composite materials have been widely used in various engineering fields over the past several decades. But unlike metals, composite materials' failure is progressive and may be initiated in the form of breaking of fibres, micro-cracks in the matrix, etc. but their propagation and final failure modes may be considerably different. The anisotropic behaviour of composite laminates makes it extremely difficult to formulate a failure theory since it requires performing bi-axial tests and plotting the failure envelopes. The failure strength of multi-direction composite materials has been the subject of decades of research. Since the last few decades, different failure theories have been used and subsequently refined to provide predictions. An attempt has also been made to provide designers with a ranking of the overall efficacy of each failure hypothesis. Fuzzy linguistic variables are frequently used to supplement the traditional analytic hierarchy process in capturing the decision maker's subjectiveness and fuzziness. The fuzzy AHP method is used in this paper to identify the best theory of failure for composite materials as a decision-making problem. Because of their effectiveness and simplicity, triangular fuzzy numbers are used to demonstrate the strength of each component in the hierarchy structure.

Synthesizing fuzzy tree automata

Fuzzy tree automata are mathematical devices for modeling and analyzing vaguely defined tree structures. The behavior of a fuzzy tree automaton generates a fuzzy tree language by mapping a set of regular trees on a ranked alphabet to fuzzy membership values. It calculates the membership grade of trees using a set of rules that process their structural characteristics. This paper deals with constructing fuzzy tree automata models that their behavior satisfies a set of given logical propositions (called properties) on the structure of trees. Our goal is uncertainty modeling by synthesizing fuzzy tree automata whose behavior is described by fuzzy linguistic variables. In this regard, we first provide several patterns and heuristic tricks and techniques for constructing fuzzy tree automata that satisfy simple properties. Then, we develop a method for modeling complex propositional formulas based on the conversion of a logical formula into a computation tree, as well as a step-by-step combination of models.

Practical implementation of the model for assessing the readiness of united territorial communities to implement project activities

The article justifies the practical implementation of the model of assessing the readiness of united territorial communities to implement project activities. The factors influencing the implementation of project management and project activities are highlighted. It is emphasized that the methodological support for determining the readiness of united territorial communities for project activities involves building an appropriate assessment model using the fuzzy set method. It is determined that the input parameters of the fuzzy inference system are 4 fuzzy linguistic variables: financial capacity to implement project activities; staff capacity to implement project activities; organizational support of project activities; technological support of project activities. The output parameter is the linguistic variable "level of readiness of the territorial communities for project activities". It is substantiated that the level of readiness of amalgamated territorial communities for project activities reflects the extent to which: the territorial community is financially capable of implementing project activities; members of the territorial community are ready to implement project activities; institutional and organizational support is complete, which provides a set of powers and resources that the territorial community has to implement project actions and activities; technological support is appropriate, according to which there is a material and technical base, openness of information and communication, and openness of the information system. It is proved that the proposed methodological support for determining the readiness of amalgamated territorial communities for project activities allows not only to determine the level of community readiness in each area (financial capacity to implement project activities; staff capacity to implement project activities; organizational support for project activities; technological support for project activities), but also to give a generalized assessment using the method of fuzzy sets. The peculiarity of the obtained model is its ability to identify the level of preparedness of the community for the implementation of project activities, to identify weaknesses and significant deviations in the work, as well as to develop further actions to implement project changes.

FUZZY FACTORS CONSIDERATION FOR THE OPTIMIZATION OF THE WASTE COLLECTION ROUTE IN THE TERRITORIAL COMMUNITY

The paper considers the process of the creation of the information technology module for the optimization of the waste collection process in the communities. The given module determines the expediency of inclusion of the waste containerbin to the route of the dust cart, depending on the degree of loading of the container and impact of the external factors on its availability, determined by the intensity of the traffic on the road to the container and state of the road, connected with weather conditions. Objective of the research consists in automatic determination of those waste containers which do not need urgent waste removal, that is an important component of theoptimization problem for the formation of the most reasonable routes for waste transportation. Scientific task of the researchincludes: determination of the factors, influencing the relevance  of the waste removal problem; substantiation of the expediency of using fuzzy logic for this purpose; formation of fuzzy linguistic variables and construction of fuzzy knowledge base; study the efficiency of the process of fuzzy logic conclusion, regarding the determination of the relevance of the containers removal on the base of the suggested influencing  factors; conclusion concerning the rationality of the suggested approach. The process of the relevance  calculation of the inclusion the waste containers into the route of the waste collection, taking into account the degree of their filling and availability by means of fuzzy logic tools is described. Analysis of the external factors, influencing the relevance of the waste collection from the specific container is performed. Fuzzy graph model is suggested, the model presents possible routes of the dust carts, linguistic variables are given, membership functions of their terms are formed, fragment of the knowledge base, supplied by the production rules, processes of the fuzzification and defuzzification are graphically illustrated using Mamdani fuzzy inference algorithm. Calculation of the actuality index of the waste collection is given on the specific example. Testing has been performed on the base of the data of trash enclosures in the city of Rivne. As a result of elimination ща non-actual containersfrom the route the length of the routes was cut by on average 10.43 %. Further improvement of the routes formation is possible, taking into account the degree of importance of the waste collection, for instance, such as from hospitals, schools, recreation spaces and other civil objects.

A Marine GIS-based Alert System to Prevent Vessels Collision with Offshore Platforms

How to effectively use an intelligent aid-to-navigation (AtoN) to prevent ships from accidentally colliding with offshore marine platforms (OMPs) is an important issue for maritime environment. This study proposes a novel collision alert system with three components to process Automatic Identification System (AIS) data in the cloud and broadcast AtoNs to ships in order to avoid collision with OMPs. The first component receives as input fuzzy linguistic variables (vessel size, relative speed, and approach distance) from the AIS data and calculates the radius of the guarding ring. The second component serves a Marine GIS (MGIS), where the system simulates the alert based on a fuzzy guarding ring at one of three levels: green (continuous light), amber (slow flash and alarm), and red (flash and fast alarm). The third component is a warning indicator device, signaling the risk of collision between moving ships and offshore platforms. The MGIS component can also be used on an OMP to alert approaching ships and to warn offshore platform workers and vessel traffic service (VTS) on-duty operators. The ability of the proposed system to protect OMPs from vessel collisions has been verified. Other potential applications include offshore wind turbines, oil rigs, or other working platforms.

Saudi Airlines Cabin Crew Performance Appraisal Using Artificial Intelligence - The Adaptive Neuro-Fuzzy Inference System

This paper targets the application of artificial intelligence in improving and developing performance through the reduction of human error and the development of fair judgment in the performance of different activities. It focuses on Adaptive Neural Fuzzy Inference System (ANFIS) for the Saudi Airlines cabin crew. Among the findings of the research was that the training error is that the difference between the training data output value and the output of the fuzzy inference system and that the fuzzy linguistic variables are not clear when applied in the performance evaluation system. The study applied a case study to analyze an adaptive neural inference system and rate it to the impact it could create on the cabin crew. The study concluded that in conducting the performance appraisal of the employees some factors such as work goals, and information on the effectiveness of goals must be considered. Also, the lack of a formal reasoning mechanism supporting the inference and evaluations is a critical challenge in performance appraisal; hence, the application of artificial intelligence in the evaluation of the performance of employees in an organization could significantly impact the employees in working towards a common goal.

A method to assess design complexity of modular automatic assembly system in design phase

PurposeTo find the system with minimum investment and best quality performance that is capable of producing all of the product variants, assessing the complexity of designing assembly system at the early concept stage is an essential step, which helps and instructs a designer to create a product- and system-oriented assembly solution with the least complexity. The purpose of this paper is to propose a quantifying measurement of complexity in the design of a modular automated assembly system.Design/methodology/approachThe configurable assembly system is becoming a trend, which enables companies to quickly respond to changes caused by different product variants but without a large investment. One of the enabling factors is the availability of modular solutions of assembly modules that can be configured according to different technical requirements. This paper develops a methodology using fuzzy evaluation to calculate the design complexity in the design phase for a modular automatic assembly system. Fuzzy linguistic variables are used to measure the interaction among the influence factors, to deal with the uncertainty of the judgement. The proposed method investigates three matrices to present how the function-based assembly modules, design complexity factors, part attributes and product components, which are regarded as the main influence factors, complicate the construction of a modular assembly system. The design complexity is derived and quantified based on these assessments.FindingsThe proposed approach presents a formal quantification to evaluate the design complexity with regard to a modular assembly system from beginning, which can be identified and used as criteria to indicate the quality of performance and investment cost in advance. A mathematical model based on the fuzzy logic is established to provide both theoretical and practical guidance for the paper. To validate the predictive model, the statistic relationships between the assessed system design complexity, real assembly defect rate and investment cost are estimated based on regression analysis. The application of the presented methodology is demonstrated with regard to a traditional rear drive unit in the automotive industry.Originality/valueThis paper presents a developed method, which addresses the measures of complexity found in the design of a modular assembly system. It would help to run the design process with better resource allocation and cost estimation in a quantitative approach.

Fuzzy FMEA for Biology-Guided Radiotherapy (BgRT)

<h3>Purpose/Objective(s)</h3> Biology-guided Radiotherapy (BgRT) is a new external beam radiotherapy delivery modality combining positron emission tomography-computed tomography (PET-CT) with a linear accelerator. Our research group has previously introduced the BgRT specific prospective process map for developing an initial clinical workflow and has performed the risk analysis for the BgRT process that will bring in a new treatment paradigm. However, due to a lack of experience and historical data, assigning an accurate value of each risk factor in traditional FMEA leads to difficulties for evaluators. Also, there are certain sub-processes where the differences in weights of risk factors needs to be considered. Therefore, in this study, we introduced the fuzzy FMEA to perform a comprehensive analysis of failure modes (FMs) of BgRT and overcome weaknesses of conventional FMEA. <h3>Materials/Methods</h3> Five sub-processes unique to BgRT out of a total of 15 sub-processes were selected and relevant 84 FMs were identified. The evaluators used the fuzzy linguistic variables (very low, low, medium, high, and very high) to evaluate the importance of risk factors for assigning weights as well as the evaluation of each risk factor (occurrence, severity, and detectability) of each failure mode. Then, the fuzzy measure was used to calculate the weights of risk factors and the fuzzy integral method was used to fuse every value of risk factor of FMs to obtain the final fuzzy risk priority number value. <h3>Results</h3> Top 3 FMs are the machine down/emergency during beam delivery, failure to meet activity before PET pre-scan, and failure/delay of tracer delivery from a local vendor. The mitigation plans are discussed by team members including complete guidelines for the emergencies on the treatment machine, maintenance program of PET tracers by dedicated staffs, and staff training program for the BgRT procedure. Strong cooperation with a local PET tracer vendor and timely patient scheduling to allow multiple PET images at the treatment machine were also included in mitigation plans. <h3>Conclusion</h3> The proposed fuzzy FMEA presents a more reasonable and effective method for assessing potential failures allowing fuzzy linguistic variables terms that are easier to use than numerical assignments to evaluate the FMs, especially with the new prototype machine for BgRT. The results have been shared with team members, and mitigation plans will be implemented.

Development of the external control model of online courses quality indicators

An analytical survey of internationally accepted samples of assessment of online courses, is conducted. An approach for construction of the system of quality indicators of electronic courses on the basis of aggregating simple numeric values and fuzzy linguistic variables on the basis of fuzzy logic, is proposed.

A new type-2 fuzzy multi-criteria hybrid method for rail transit operation safety assessment

Rail transit plays a very important role in the social and economic development of big cities. Its operational safety is of great significance for ensuring people’s lives and maintaining social stability. In this paper, the rail transit operation safety management framework is proposed and analyzed by considering the factors of organizational management, personnel management, equipment and facilities and operating environment together. Considering the fuzzy uncertainty in the process of the safety assessment of rail transit operation (SARTO) and the interrelationship and conflict among the criteria, a new hybrid model combining DEMATEL, ANP and VIKOR with type-2 fuzzy linguistic variables is proposed. The model can present the effect-cause relationship among the different main criteria by the type-2 fuzzy DEMATEL, obtain the weights of the sub-criteria by the type-2 fuzzy ANP and the risk assessment value of each metro line by the type-2 fuzzy VIKOR and give further suggestions for rail transit operation safety management. This novel hybrid methodology is used to evaluate operation safety of metro lines in Beijing, and the sensitivity analysis is also applied to analyze the robustness. For the accuracy and consistency with the actual situation check, the proposed type-2 fuzzy VIKOR method is compared with both TOPSIS method and MOORA method in interval type-2 fuzzy sets. The application of Beijing metro lines proves the feasibility and the practicality of the new hybrid method, which provides effective support for decision-makers in practice.

Eliciting symptom‐diagnosis knowledge from online medical Q&amp;A

AbstractWith the objective to automatically detect diseases from symptoms in free‐text data, a methodology to extract symptom‐diagnosis knowledge from online medical textual data in Q&amp;A domain is proposed in this paper: (1) a term frequency‐inverse document frequency and PRECISION method is adopted to retrieve symptom words from unstructured text; (2) a variable precision rough set based genetic algorithm is applied to reduce redundant symptom words, and a rough set based rule is utilized for adding discriminative symptom words assisting to discriminate diseases sharing similar symptoms; (3) by employing fuzzy linguistic variables to express the risk level of disease or severity level of symptoms, a knowledge base with fuzzy belief structure is generated. Using data extracted from a Chinese medical Q&amp;A forum for training and testing, some classical gastrointestinal diseases serve as a case study to evaluate the efficiency of the proposed methodology. Subsequently performance comparisons are made between the proposed methodology and some other classifiers, such as the decision tree algorithms including ID3 and J45, and the Bayesian network classifier. The comparative results demonstrate that the proposed methodology outperforms the decision tree algorithms and the Bayesian network classifier.

An Intelligent Ranking Model for Multi-Criteria Decision Making Using Fuzzy Matrix Method

Background: In the daily life, one may encounter several problems whose solution is based on the consideration of several criteria altogether. To generate a solution for such problems is in fact a complex task. If one generates a solution, then there is no guarantee that the solution is optimal. It is really challenging to take into account all the criteria at the same time and generate a solution that is optimal. Several techniques have been proposed to solve multi-criteria decision making problems. Methods: In this paper, we propose an intelligent solution to multi criteria decision making problem by means of fuzzy aggregation using matrix method. The proposed method finds application in solving such problems where the criteria are defined in qualitative form (fuzzy linguistic variables) rather than in quantitative form (crisp). A novel application of the proposed approach to rank the employees in an organization is presented in the paper. Results: The proposed method is empirically validated by applying it to find the best employee in a institute of repute. Data collection is done in real time to prove the utilization of proposed approach in a efficient manner. Conclusion: The results obtained after application of proposed method to rank the employees are realistic as it takes into consideration the ambiguity involved in human thought process.

Case-Based Reasoning for Hidden Property Analysis of Judgment Debtors

Many judgment debtors try to evade, confront, and delay law enforcement using concealing and transferring their property to resist law enforcement in China. The act of hiding property seriously affects people’s legitimate rights and interests and China’s legal authority. Therefore, it is essential to find an effective method of analyzing whether a judgment debtor hides property. Aiming at the hidden property analysis problem, we propose a case-based reasoning method for the judgment debtor’s hidden property analysis. In the hidden property analysis process, we present the attributes of the enforcement case by crisp symbols, crisp numbers, interval numbers, and fuzzy linguistic variables and develop a hybrid similarity measure between the historical enforcement case and the target enforcement case. The results show that the recommendations obtained with the information and knowledge of similar historical cases are consistent with judicial practice, which can reduce the work pressure of law enforcement officers and improve the efficiency of handling enforcement cases.

Time dependent network resource optimization in cyber–physical systems using game theory

The social and economic stability of a country is dependent on critical infrastructures (CIs) whose services range from financial to healthcare and power to transportation and communications. Most of these CIs are cyber–physical systems (CPSs), which integrate the network’s computational and communication capabilities to facilitate the monitoring and controlling of physical processes. Such systems are vulnerable to damage due to natural disasters, physical incidents, or cyber-attacks impacting the CPS organizations managing complex industrial control systems and data acquisition systems. When these CPSs are exposed to systemic cyber risks and cascaded network failures, network administrators need to recover from the compromise under limited resources. This is formulated as an attacker-defender game model to emulate the decision-making process in choosing an appropriate attack/defence mechanism in response to cybersecurity incidents using game theory. To further improve the assumptions made in the pure game-theoretic model, we relax the constraints on the rationality of the players, monetary payoff, and completeness of information by incorporating learning in games using reinforcement learning technique and compute the expected payoff using linguistic fuzzy variables.

Supplier selection mechanism in electric coal procurement under sustainability.

The electric energy mainly comes from thermal power generation fueled by electric coal, and the selection of electric coal supplier is of great practical significance for the development of electric power enterprises. In this paper, the mechanism of supplier selection in electric coal procurement is designed from a perspective of sustainability. Concretely, eleven evaluation indexes from three aspects of economy, environment, and society are selected to construct a new evaluation index system for selecting sustainable electric coal suppliers. Based on this index system, a new method of supplier selection in electric coal procurement based on 2-tuple correlation coefficient analysis is proposed. In this method, considering that the evaluation index values have the features of multi-source heterogeneous data, i.e., precise real number, interval number, and fuzzy linguistic variables coexist, all index values are converted into 2-tuples. Then, a 2-tuple deviation maximization method is proposed to determine the weight of each evaluation index, and a new method of 2-tuple correlation coefficient analysis is proposed to select sustainable electric coal suppliers. Moreover, the effectiveness and feasibility of the model are highlighted by an application example of selecting the electric coal suppliers. Combined with the development status of electric power industry and the research results of this paper, several countermeasures and suggestions are provided on how to formulate the optimal procurement strategy of electric coal for electric power enterprises and how to improve the competitiveness for electric coal suppliers.

Development of a multi-period multi-attribute group decision-making method using type- 2 fuzzy set of linguistic variables

Recently, fuzzy linguistic variables have gained a great deal of attention from researchers in many decision-making problems. In this problems, type-2 fuzzy sets have been used to better cover linguistic data uncertainty. However, many of research works in this regard have been performed in type-2 fuzzy domain and in static mode. Since the decision making problems in the real world usually fluctuate over time, so it need to use decision making models in multi period of time. In the present research, a Multi-Period (Dynamic) Multi-Attribute Group Decision-Making (MPMADM) method is presented based on type-2 fuzzy sets where decision-making attributes are first expressed in linguistic terms and then incorporated, as interval type-2 fuzzy numbers, into problem solving where a new integrating operator called Multi-Period Trapezoidal Interval Type-2 Fuzzy Number Weighted Arithmetic averaging (MPTIT2FNWA) is defined on type-2 interval fuzzy numbers to integrate decision-making information in multiple periods of time. Once finished with explaining the proposed method, a numerical example is given to evaluate the proposed method in terms of effectiveness and applicability, with the results compared to those of other methods.