Human reliability analysis (HRA) is a systematic activity to identify, analyze, and reduce human failure events in complex engineering systems. As a widely-used approach in HRA, the success likelihood index method (SLIM) mainly depends on experts’ judgments for the rating and weighting of performance shaping factors (PSFs). Additionally, the estimation precision of human error probabilities (HEPs) is greatly affected by experts’ opinions and PSF weights. In this paper, an improved SLIM by integrating the minimum cost consensus method and the game theory-based combination weighting method is developed to acquire the HEPs of operational tasks under a 2-tuple linguistic environment. To facilitate consensus reaching, the missing information in task state assessment matrices of experts is filled based on their knowledge-match levels and similarity degrees. Then, a minimum cost consensus method is used to resolve the conflicts among domain experts considering individual harmony. Besides, a game theory-based combination weighting method is introduced to determine the relative weights of PSFs by integrating their subjective and objective importance. Finally, the feasibility and advantages of the proposed SLIM are validated via an empirical case concerning bunkering operation on board.Note to Practitioners—This research is motivated by a human error assessment issue during the shipboard bunkering operation. It develops a new SLIM based on the minimum cost consensus method and the game theory-based combination weighting method. The purpose of this work is to reduce high-risk human errors in the bunkering operation and enhance maritime transportation safety. Using the proposed SLIM, practitioners can handle unknown information and discrepancies in experts’ opinions and effectively quantify human errors. The application results prove the superiority of the new model. Furthermore, the proposed SLIM can be modified and applied to the socio-technical system in other industries.

Fuzzy Petri nets (FPNs) are widely used in various fields for knowledge representation and reasoning. Nevertheless, the original FPN model has many limitations when applied in the practical situations. In response, this article aims to develop a new FPN model, named as probabilistic linguistic Petri nets (PLPNs), for representing and acquiring knowledge based on a dynamic consensus reaching process. First, to avoid losing the initial information, the probabilistic linguistic term sets (PLTSs) are adopted to represent the professional knowledge of domain experts. Subsequently, a knowledge acquisition approach is proposed to acquire the knowledge parameters of PLPNs. Then, a dynamic consensus reaching method is introduced during the knowledge acquisition process to increase the group consensus level among experts. Finally, a real-world risk assessment example concerning the subway fire system is presented to validate the usefulness and effectiveness of the proposed PLPNs. The results show that the new PLPNs are efficient and practical to represent and acquire expert knowledge with conflict and inconsistent opinions.

Failure mode and effect analysis (FMEA) is a widely used reliability management technology to evaluate the risk of potential failures in a system, product, or service. Nevertheless, the normal risk priority number (RPN) method has been extensively criticized for many deficiencies in practical applications. To overcome the drawbacks of traditional FMEA, plenty of methods have been suggested in previous studies. But majority of them evaluated the risk factors of each failure mode directly and cannot take group and individual risk attitudes into account. In this article, we put forward a new FMEA approach integrating probabilistic linguistic preference relations (PLPRs) and gained and lost dominance score (GLDS) method. The PLPRs are adopted to describe the risk evaluations of experts by pairwise comparison of failure modes. An extended GLDS method is introduced to derive the risk ranking of failure modes considering both group and individual risk attitudes. Moreover, a two-step optimization model is proposed to determine the weights of risk factors when their weighing information is unknown. Finally, a load-haul-dumper machine risk analysis case is presented to demonstrate the proposed FMEA. It is shown that the approach being proposed in this study provides a practical and effective way for risk evaluation in FMEA.

Fuzzy Petri nets (FPNs) are a useful instrument for modelling expert systems to conduct knowledge representation and reasoning. Many studies have been carried out for improving the performance of FPNs in terms of their accurate representation of knowledge and power of approximate reasoning. Nevertheless, the current representation methods with FPNs are unable to handle the uncertain linguistic knowledge given by domain experts and the reliability of their judgments. In addition, the existing reasoning algorithms have no way to capture the interrelationship of the propositions with the same output transition. Therefore, we present a new type of FPNs, called 2-dimensional uncertain linguistic Petri nets (2DULPNs). The 2-dimensional uncertain linguistic variables (2DULVs) and Choquet integral are combined for knowledge representation and reasoning for the first time. The truth degrees of propositions, thresholds and certainty values of linguistic production rules are denoted as 2DULVs. Some new aggregated operators based on Choquet integral are proposed and used in the approximate reasoning to capture the interactions among antecedent propositions. Finally, an equipment fault diagnosis example is provided to illustrate the correctness and effectiveness of the proposed 2DULPN model.

Abstract As a vital modelling technique, fuzzy Petri nets (FPNs) have been widely used in various areas for knowledge representation and reasoning. However, the conventional FPNs have many deficiencies in representing inaccurate knowledge, acquiring knowledge parameters and conducting approximate reasoning when used in the real world. In this article, a new version of FPNs, called R‐numbers Petri nets (RPNs), is proposed to overcome the shortcomings and enhance the effectiveness of FPNs. Based on R‐numbers, expert knowledge is depicted in the form of weighted R‐numbers production rules. The interrelationships among input places (or transitions) are modelled by the R‐numbers Maclaurin symmetric mean operator in the knowledge reasoning process. In addition, the conflict opinions of experts are handled with the proposed RPN model in order to obtain more precise knowledge parameters. Finally, the effectiveness and practicality of the proposed RPNs are illustrated by a realistic example concerning reliability analysis of an electric vehicle motor.

Occupational health and safety (OHS) is a multidisciplinary activity aimed at recognizing, evaluating and controlling hazards arising in or from the workplace that could impair the health and well-being of workers. In dealing with an OHS risk analysis problem, it is a crucial step and also a big challenge to assess the risk of occupational hazards. Normally, domain experts tend to use qualitative words to express the risk of occupational hazards due to the fuzziness of human cognition, and multiple risk criteria are often involved in the OHS risk analysis. In this study, we develop a new OHS risk assessment framework by integrating the TODIM (an acronym in Portuguese for interactive and multicriteria decision-making) and preference ranking organization method for enrichment evaluations (PROMETHEE) methods to assess and rank the risk of occupational hazards under linguistic spherical fuzzy environment. The linguistic spherical fuzzy sets are utilized to deal with the vague and uncertain risk assessments of occupational hazards provided by experts. An integrated TODIM-PROMETHEE algorithm is introduced to determine the risk priority of the identified occupational hazards. Moreover, we extend the indifference threshold-based attribute ratio analysis method to derive the relative weights of risk criteria based on linguistic spherical fuzzy information. Finally, the OHS risk analysis of medical staff in a hospital is presented to illustrate the effectiveness and availability of our proposed model. Results show that the TODIM-PROMETHEE framework being developed in this study provides a useful, effective, and practical way for the risk assessment of occupational hazards in OHS.

As a proactive reliability analysis tool, failure mode and effect analysis (FMEA) was widely utilized in various industries to guarantee safety and reliability. Recently, many researchers in this field have emphasized the limitations of this technique, such as in failure mode evaluation, risk factor weighting, and failure mode prioritization. The objective of this article is to develop a new FMEA model combining linguistic Z-numbers and an extended projection method to enhance the inherent characteristics of FMEA. Specifically, the linguistic Z-numbers are used to express experts' risk assessment information and the reliability of the assessment result. The normal projection method is extended to determine the risk priority of the failure modes considered in FMEA. Moreover, the relative weights of risk factors are derived objectively based on the idea of technique for order preference by similarity to ideal solution (TOPSIS) method. A practical risk evaluation case of aircraft landing system is given for verifying the applicability and effectiveness of the proposed FMEA. The results show that the proposed linguistic Z-number projection model is practical and flexible, which can not only depict experts' complex and uncertain risk evaluation information accurately, but also obtain more accurate risk prioritization of failure modes.

Failure mode and effect analysis (FMEA), as a proactive reliability management technique, has been widely employed to reduce the risk of systems and assure the quality of products in various industries. Nevertheless, the traditional FMEA method shows many weaknesses when used in real-life scenarios. This causes the dilemma for practitioners that it is not great as expected. Many alternative risk priority models have been developed to enhance the capacity of FMEA, but the majority of them focus on how to acquire a complete ranking of failure modes. In this article, we propose a novel FMEA approach using hesitant uncertain linguistic Z numbers (HULZNs) and density-based spatial clustering of applications with noise (DBSCAN) algorithm to assess and cluster the risk of failure modes. The HULZNs are adopted to represent the uncertain and hesitant risk evaluation information of FMEA team members. The normal DBSCAN algorithm is improved to cluster the recognized failure modes into different risk classes. Moreover, the weights of FMEA experts are dynamically obtained with a weight adjustment method. Finally, a practical case of a geothermal power plant is given to demonstrate the effectiveness and validity of our introduced FMEA approach.

Fuzzy Petri nets (FPNs) are a graphical-based knowledge representation and reasoning tool widely used in many fields for intelligent decision making. However, the traditional FPNs could not accurately represent domain experts’ uncertain and ambiguous knowledge with the increasing complexity of knowledge base systems. Additionally, the task of assessing the truth degrees of input places is limited to small-scale expert groups in current practices. In response to these problems, we develop a new type of FPN model, called Pythagorean FPNs (PFPNs), for knowledge representation and reasoning in the large group context. Pythagorean fuzzy sets are introduced to capture imprecise and ambiguous knowledge and a large group truth determination method is proposed to get the truth degrees of input places with massive data. Finally, an application example regarding security risk assessment is given to show the effectiveness and advantages of our proposed PFPNs.

Abstract Timely and effective emergency decision making (EDM) is the key to control the spread of disasters and reduce the casualties and property losses caused by emergencies. However, due to limited time and insufficient data, it is difficult for decision makers to provide accurate information about emergency incidents. Moreover, the EDM problems become complicated and unstructured requiring the deployment of advanced mathematical techniques to derive the most acceptable response. In this paper, we propose a new EDM approach by using picture fuzzy sets and axiomatic design technique for determining the optimal rescue plan to reduce the damages of emergencies. The contribution of this paper is to apply the picture fuzzy sets to handle the uncertainty and ambiguity of decision makers' assessments on emergency alternatives, apply the picture fuzzy hybrid averaging operator to aggregate decision makers' opinions into a group evaluation matrix and extend the axiomatic design technique to identify the best emergency solution for EDM. Finally, a real example is provided, and the result is compared with existing methods to demonstrate the feasibility and practicability of our proposed EDM approach.

Undergraduate teaching audit and evaluation (UTAE) is a new type of evaluation pattern, which is extremely important for a university to improve its quality assurance system and enhance teaching quality. Selecting an optimal university for benchmarking through UTAE to promote the quality of teaching can be regarded as a complex multicriteria decision making (MCDM) problem. Furthermore, in the process of UTAE, experts' evaluations over the teaching quality of universities are often imprecise and fuzzy due to the subjective nature of human thinking. In this paper, we propose a new UTAE approach based on q-rung orthopair fuzzy sets and the multiattribute border approximation area comparison (MABAC) method for evaluating and selecting the best university for benchmarking. The introduced method deals with the linguistic assessments given by experts by using q-ROFSs, assigns the weights of audit elements based on the indifference threshold-based attribute ratio analysis method, and acquires the ranking of universities with an extended MABAC method. The feasibility and effectiveness of the proposed q-rung orthopair fuzzy MABAC method is demonstrated through a realistic UTAE example. Results show that the UTAE method being proposed is valid and practical for UTAE.

In previous studies, fuzzy Petri nets (FPNs) have been used for knowledge representation and reasoning in various areas. However, the traditional FPNs have limited abilities in representing uncertain knowledge and conducting approximate reasoning when applied in practical situations. In addition, the knowledge parameters in existing FPNs are usually given by a small number of experts. To address these issues, a grey reasoning Petri net (GRPN) model is proposed in this article for knowledge representation and reasoning under large group environment. In this model, grey production rules in an expert system are modeled by the GRPNs, where grey numbers are used to represent the truth degrees of places, certainty values, and the thresholds on output arcs of transitions. The grey weighted Bonferroni mean operator is adopted as a substitute of the classical min and max operators in the developed grey reasoning algorithm to capture the interrelationships of input places and the interrelationships between transitions. Furthermore, a large group decision-making method is introduced for obtaining the knowledge parameters of GRPNs based on the grey correlation analysis. Finally, the usefulness and effectiveness of the proposed GRPN model is demonstrated by a real-world risk evaluation example.

Once an emergency event occurs, a reasonable and effective emergency decision should be made within a short time. The emergency decision-making (EDM) problems which are based on linguistic information have become a research focus in recent years. In this paper, to ensure more effective EDM in real-life circumstances, a novel combined approach is presented by extending the zero-sum game (ZSG) using best-worst method (BWM) and Pythagorean fuzzy uncertain linguistic variables (PFULVs). First, the weights of criteria are calculated by the BWM. Then, the payoff evaluations of decision-makers are expressed by the PFULVs, and the group payoff evaluation matrix is constructed by using the Pythagorean fuzzy uncertain linguistic prioritized weighted averaging operator. After that, the ZSG method is introduced to rank alternative emergency solutions. The new approach helps to express the vagueness of assessment data provided by decision-makers more precisely in emergency situations. Finally, a real EDM example is presented and a comparison analysis is performed to validate the feasibility and practicability of the proposed BWM-PFULV-ZSG method.

Robots have received considerable attention in many manufacturing companies due to their great capabilities and characteristics. Selecting an appropriate robot for a specific application can be regarded as a challenging multicriteria decision-making problem. Furthermore, decision makers are inclined to represent their opinions by using linguistic terms owing to their ambiguous thinking. In this regard, we put forward a novel robot selection model by integrating quality function development (QFD) theory and qualitative flexible multiple criteria method (QUALIFLEX) under interval-valued Pythagorean uncertain linguistic context. For the developed model, the evaluations given by decision makers are presented as interval-valued Pythagorean uncertain linguistic sets for dealing with the uncertainty and vagueness of decision makers’ information. An extended QFD method is used for determining criteria weights from the perspective of customers. A modified QUALIFLEX technique based on closeness degree is utilized to generate the ranking order of alternative robots and determine the most suitable one. Finally, an empirical example of an auto manufacturing company is applied to clarify the effectiveness and accuracy of the proposed robot selection approach.

Failure mode and effect analysis (FMEA) is a prospective reliability analysis technique used in a wide range of industries for enhancing the safety and reliability of systems, products, processes, and services. However, the conventional FMEA method has been criticized for inherent drawbacks that limit effectiveness and applications. In this paper, a novel integrated FMEA model based on cloud model theory and hierarchical technique for order of preference by similarity to ideal solution (TOPSIS) method is developed to assess and rank the risk of failure modes. First, individual linguistic assessments of failure modes are converted into normal clouds. Then, FMEA team members' weights are calculated based on the subjective weighting information. Finally, the risk priority of failure modes is determined by using the cloud hierarchical TOPSIS. The newly proposed FMEA method combines the advantages of the cloud model in coping with fuzziness and randomness of linguistic assessments and the merits of hierarchical TOPSIS in solving complex decision making problems. Two empirical examples to illustrate the feasibility and effectiveness of the proposed FMEA are presented together with a comparison to existing methods.

Electric vehicles (EVs) are recognized as one of the most promising technologies worldwide to address the fossil fuel energy resource crisis and environmental pollution. As the initial work of EV charging station (EVCS) construction, site selection plays a vital role in its whole life cycle, which, however, is a complicated multiple criteria decision making (MCDM) problem involving many conflicting criteria. Therefore, this work aims to propose a novel integrated MCDM approach by a grey decision making trial and evaluation laboratory (DEMATEL) and uncertain linguistic multi-objective optimization by ratio analysis plus full multiplicative form (UL-MULTIMOORA) for determining the most suitable EVCS site in terms of multiple interrelated criteria. Specifically, the grey DEMATEL method is used to determine criteria weights and the UL-MULTIMOORA model is employed to evaluate and select the optimal site. Finally, an empirical example in Shanghai, China, is presented to demonstrate the applicability and effectiveness of the proposed approach. The results show that the proposed approach is a useful, practical, and effective way to find the optimal location of EVCSs.

This study develops an improved failure mode and effects analysis (FMEA) method using two-dimensional uncertain linguistic variables (2DULVs) and alternative queuing method (AQM). The 2DULVs are employed to represent the evaluations provided by FMEA team members on the weights of risk factors and the risk of failure modes in the form of pairwise comparisons. The two-dimensional uncertain linguistic best worst method (2DUL-BWM) is used to derive the weights of risk factors. The two-dimensional uncertain linguistic AQM (2DUL-AQM) is proposed for determining the risk ranking of the identified failure modes. Finally, the maintenance of a water treatment plant is presented as an example to demonstrate the applicability and effectiveness of the proposed FMEA method. By comparing its performance with those of other existing methods, the proposed FMEA is shown to be more advantageous in ranking the risk of failure modes.

In the highly competitive environment, capturing and disseminating of tacit knowledge are significant to an organization’s success with the development of knowledge-based systems. However, in practical knowledge acquisition process, domain experts tend to express their judgments using multigranularity linguistic term sets, and there usually exists uncertain and incomplete information since expert knowledge is experience-based and tacit. In addition, although the technical capabilities of expert systems based on fuzzy Petri nets (FPNs) are expanding, they still fall short of meeting the increasingly complex knowledge demands. Therefore, this paper develops a theoretical model based on linguistic interval 2-tuples and interval-valued intuitionistic FPNs (IVIFPNs) for acquiring and representing tacit knowledge to increase and sustain the competitive advantages of knowledge intensive organizations. An empirical case study in medical practice is provided to demonstrate the application and feasibility of the proposed model, and the results show that it can well capture experts’ tacit knowledge and reuse the acquired knowledge productively.

Fuzzy Petri nets (FPNs) are a vital modeling technique for the construction of knowledge-based systems, which have been commonly used in many fields, such as fault diagnosis, risk assessment, workflow management, and disassembly process planning. However, the conventional FPNs have been blamed for the following reasons: 1) the representation parameters in FPNs cannot precisely model experts' experience since it is difficult to manage the fuzziness and randomness of knowledge assessments simultaneously, and 2) the weight coefficients in the existing approximate reasoning algorithms are hardly enough to reflect the associated weights of reordered places. In response, we propose a new type of FPNs, called cloud reasoning Petri nets (CRPNs) based on the concept of interval clouds and the hybrid averaging operator. The cloud production rules in a knowledge-based system are modeled by CRPNs, where the truth degrees of places, the certainty factors of rules, and the thresholds of transitions are represented by interval clouds. Moreover, a matrix operation-based reasoning algorithm is proposed to improve the efficiency of calculating final truth degrees, in which both local and ordered weight coefficients are taken into consideration. Finally, a practical example concerning a power system is provided to demonstrate the usefulness and advantages of the proposed CRPN model.

Failure mode and effect analysis (FMEA) is a well-known engineering technique to recognize and reduce possible failures for quality and reliability improvement in products and services. It is a group-oriented method usually conducted by a multidisciplinary and cross-functional expert panel. In this paper, we explore two key issues inherent to the FMEA practice: the representation of diversified risk assessments of FMEA team members and the determination of priority ranking of failure modes. Specifically, a framework integrating cloud model, a new cognitive model for coping with fuzziness and randomness, and preference ranking organization method for enrichment evaluation (PROMETHEE) method, a powerful and flexible outranking decision making method, is developed for managing the group behaviors in FMEA. Moreover, FMEA team members' weights are objectively derived taking advantage of the risk assessment information. Finally, we illustrate the new risk priority model with a healthcare risk analysis case, and further validate its effectiveness via sensitivity and comparison discussions.

Fuzzy Petri net (FPN), as one type of high-level Petri nets, has attracted a lot of attention over the recent decade due to its adequacy for knowledge representation and logic reasoning. However, in the FPN literature, the truth degrees of input places are usually given directly or supposed by researchers. No or little research has been performed on the determination of initial marking vector for a specific FPN. In this correspondence paper, we introduce a group decisionmaking model using hesitant 2-tuple linguistic term sets to obtain the initial truth values of FPNs based on domain experts' knowledge and gathered data. As is illustrated by the numerical example, the proposed framework can well capture domain experts' diversity judgements and derive initial truth degrees for an FPN under different types of uncertainties.

Fuzzy Petri nets (FPNs) are an important modeling tool for knowledge representation and reasoning, which have been extensively used in a lot of fields. However, the conventional FPN models have been criticized as having many shortcomings in the literature. Many different models have been suggested to enhance the performance of FPNs, but deficiencies still exist in these models. First, various types of uncertain knowledge information provided by domain experts are very hard to be modeled by the existing FPN models. Second, the traditional FPNs determine the results of knowledge reasoning using the min, max, and product operators, which may not work well in many practical applications. In this paper, we propose a new type of FPN model based on intuitionistic fuzzy sets and ordered weighted averaging operators to deal with the problems and improve the effectiveness of the conventional FPNs. Moreover, a max-algebra-based reasoning algorithm is developed in order to implement the intuitionistic fuzzy reasoning formally and automatically. Finally, a case study concerning fault diagnosis of aircraft generator is presented to demonstrate the proposed intuitionistic FPN model. Numerical experiments show that the new FPN model is feasible and quite effective for knowledge representation and reasoning of intuitionistic fuzzy expert systems.

Nowadays, failure mode and effect analysis (FMEA) is a widely used reliability analysis technique for products, processes, and services because of its relative simplicity. The conventional risk priority number method, however, has been criticized as having many limitations, such as in the assessment of failure modes, the weighting of risk factors, and the prioritization of failure modes. In this paper, we aim to develop a new risk priority model for FMEA by integrating hesitant 2-tuple linguistic term sets and an extended QUALIFLEX approach. The concept of hesitant 2-tuple linguistic term sets is first presented to express various uncertainties in the assessment information of FMEA team members. Borrowing the idea of grey relational analysis, a multiple objective optimization model is constructed to determine the relative weights of risk factors with incomplete weight information. The extended QUALIFLEX approach with an inclusion comparison method is then suggested to determine the risk ranking of failure modes identified in FMEA. Finally, the practicality and effectiveness of the proposed FMEA are demonstrated through a case study, and the results show that the new risk priority approach is useful and flexible for handling complicated FMEA problems and can yield a reasonable and credible priority ranking of failure modes.

This paper proposes a linguistic reasoning Petri net (LRPN) model and develops an ordered weighted linguistic reasoning (OWLR) algorithm for knowledge representation and reasoning. Linguistic production rules in the knowledge base of a decision support system are modeled by LRPNs, where the truth degrees of the propositions in the linguistic production rules and the certainty factors of the rules are represented by linguistic 2-tuples. Moreover, both local and global weights of knowledge rules are taken into account in the linguistic reasoning process. The developed OWLR algorithm can allow the rule-based expert systems modeled with LRPNs to execute knowledge reasoning in a more flexible and intelligent manner. Finally, a case study regarding production rescheduling is presented to show the effectiveness and benefits of the proposed LRPN model and the linguistic reasoning approach.

This paper studies a strategic level decision problem for disaster relief facility network planning in metropolises. This problem is concerned with some long-term decisions on locating the emergency shelters, locating the supply and medical centers, and maintaining fleets of ambulances and transportation vehicles. These established facilities and deployed vehicles constitute a humanitarian logistic network for rapid responses and reliefs. An integer programming model is proposed to minimize the total cost of establishing such a disaster response network. A Lagrangian relaxation method is developed for solving the problem in large scales. To illustrate the proposed method's usage in practice, a demo example about its application in Shanghai is given. Some numerical experiments are also performed to validate the effectiveness and the efficiency of the proposed model and method.

Consider the various types of uncertain preference information provided by the decision makers and the importance of determining the associated weights for the aggregation operator, the multiple attribute group decision making (MAGDM) methods based on some dependent interval 2-tuple linguistic aggregation operators are proposed in this paper. Firstly some operational laws and possibility degree of interval 2-tuple linguistic variables are introduced. Then, we develop a dependent interval 2-tuple weighted averaging (DITWA) operator and a dependent interval 2-tuple weighted geometric (DITWG) operator, in which the associated weights only depend on the aggregated interval 2-tuple arguments and can relieve the influence of unfair arguments on the aggregated results by assigning low weights to them. Based on the DITWA and the DITWG operators, some approaches for multiple attribute group decision making with interval 2-tuple linguistic information are proposed. Finally, an illustrative example is given to demonstrate the practicality and effectiveness of the proposed approaches.

The two most important issues of expert systems are the acquisition of domain experts' professional knowledge and the representation and reasoning of the knowledge rules that have been identified. First, during expert knowledge acquisition processes, the domain expert panel often demonstrates different experience and knowledge from one another and produces different types of knowledge information such as complete and incomplete, precise and imprecise, and known and unknown because of its cross-functional and multidisciplinary nature. Second, as a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. The parameters in current FPN models could not accurately represent the increasingly complex knowledge-based systems, and the rules in most existing knowledge inference frameworks could not be dynamically adjustable according to propositions' variation as human cognition and thinking. In this paper, we present a knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems mentioned above. As is illustrated by the numerical example, the proposed approach can well capture experts' diversity experience, enhance the knowledge representation power, and reason the rule-based knowledge more intelligently.

The main objective of this paper is to propose a new risk priority model for prioritizing failures in failure mode and effects analysis (FMEA) on the basis of fuzzy evidential reasoning (FER) and belief rule-based (BRB) methodology. The technique is particularly intended to resolve some of the shortcomings in fuzzy FMEA (i.e., fuzzy rule-based) approaches. In the proposed approach, risk factors like occurrence (O), severity (S), and detection (D), along with their relative importance weights, are described using fuzzy belief structures. The FER approach is used to capture and aggregate the diversified, uncertain assessment information given by the FMEA team members; the BRB methodology is used to model the uncertainty, and nonlinear relationships between risk factors and corresponding risk level; and the inference of the rule-based system is implemented using the weighted average-maximum composition algorithm. The Dempster rule of combination is then used to aggregate all relevant rules for assessing and prioritizing the failure modes that have been identified in FMEA. A case study concerning an ocean going fishing vessel in a marine industry is provided and conducted using the proposed model to illustrate its potential applications and benefits.

Although a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer from some deficiencies. First, the parameters in current FPN models, such as weight, threshold, and certainty factor do not accurately represent increasingly complex knowledge-based expert systems and do not capture the dynamic nature of fuzzy knowledge. Second, the fuzzy rules of most existing knowledge inference frameworks are static and cannot be adjusted dynamically according to variations of antecedent propositions. To address these problems, we present a new type of FPN model, dynamic adaptive fuzzy Petri nets, for knowledge representation and reasoning. We also propose a max-algebra based parallel reasoning algorithm so that the reasoning process can be implemented automatically. As illustrated by a numerical example, the proposed model can well represent the experts' diverse experience and can implement the knowledge reasoning dynamically.