Fuzzy Evidential Reasoning Research Articles

A fuzzy evidential reasoning-based approach for assessing the navigation obstruction risk of shipwrecks in the Yangtze river

ABSTRACT This paper aims to evaluate the navigation obstruction risk represented by shipwrecks in shallow waters based on a fuzzy evidential reasoning approach. The purpose of this approach is to establish a hierarchical decision-making framework that considers ship conditions, traffic environment, distance from sensitive areas, and natural environment; to derive the belief rule base using the IF-THEN rule with a belief structure; and to obtain the crisp values of navigation obstruction risk of shipwrecks via evidential reasoning. This approach is applied to the navigation obstruction risk assessment of 10 historical shipwrecks in the Yangtze River, and the evaluation results are consistent with historical data. Consequently, this approach can be used for assisting in shipwreck salvage decision-making in navigable waters.

A multi-method examination of barriers to traceability in Industry 5.0-enabled digital food supply chains

PurposeRecent worldwide developments have altered how businesses operate. As a result, when making business decisions, the emphasis for many industries has shifted towards digital adoption to ensure sustainability, and the food supply chain is no exception. However, a substantial gap exists in assessing the barriers to a digitised food supply chain enabled by Industry 5.0 technologies. This study strives to bridge the gap by identifying and assessing the barriers to improved traceability.Design/methodology/approachFor this study, a mixed method approach was used encompassing both qualitative and quantitative techniques, including an online survey, exploratory factor analysis (EFA), and the fuzzy evidential reasoning approach (FERA). The literature survey and expert opinion first yielded a list of 18 barriers, which were subsequently examined using EFA. As a result, four barriers were removed. The remaining 14 barriers were then assessed using FERA from the perspectives of the Technology, Organisation and Environment (TOE) framework. Further, a sensitivity analysis was performed to test the model’s reliability.FindingsThe present study resulted in the prioritisation of barriers from the TOE perspective. According to the findings, the top three barriers that impede the traceability of Industry 5.0-enabled digital food supply chains are Limited Digital and Physical Infrastructure, Inadequate Capital Investment, and the Intricate Supply Chain Framework.Research limitations/implicationsThe findings from this research will prove valuable for decision-makers, practitioners and policymakers in developing methods for improving traceability within the digital food supply chain. Concerned stakeholders may use the findings to identify and take immediate action for better decision-making.Originality/valueThis study’s originality lies in its position as one of the first to identify and examine the challenges to better traceability in an Industry 5.0-enabled digital food supply chain. It also adds value by broadening the TOE framework’s scope in the Industry 5.0-enabled digital food supply chain context.

Efficient Cybersecurity Assessment Using SVM and Fuzzy Evidential Reasoning for Resilient Infrastructure

Efficient Cybersecurity Assessment Using SVM and Fuzzy Evidential Reasoning for Resilient Infrastructure

Using the fuzzy evidential reasoning approach to assess and forecast the water conflict risk in transboundary Rivers: A case study of the Mekong river basin

Improving the reliability of conflict evaluation and risk prediction methods used in transboundary water-sharing problems is an important research gap that needs to be addressed. This paper constructed a novel and comprehensive water conflict risk assessment framework that takes into account the multi-dimensional attributes of water resources. The reliability of each evaluation index is represented based on the triangular fuzzy number which can address the ambiguity and uncertainty. The fuzzy evidence reasoning model (FERM) was constructed to comprehensively calculate the risk level of water conflict in the transboundary river basin. The GM (1,1) model was used to predict the indicators for dynamic forecasting of the water conflict risk, and the water sharing problem in the Mekong River Basin (MRB) was selected as the case study to validate the method proposed in this paper. The results showed: (1) Except for Myanmar, the risk of water conflicts in other countries has decreased and will continue to decrease in the future. (2) The water quantity conflict risk in Laos and Thailand is increasing. (3) There is little change in the risk of water ecological conflict in riparian countries. (4) The risk of socio-economic conflict in Cambodia rises from high grade to very high grade, while the risk of economic and social conflict in other countries will decline. (5) From 1992 to 2027, water conflict in riparian countries have been on the decline. The comparison of the TWAP’s (Transboundary Waters Assessment Programme) assessment and prediction of water conflict risk shows that the research results of the two are close to each other. Therefore, the proposed model could help to assess and forecast the water conflict risk in transboundary rivers. This assists riparian countries to take effective preventive measures to avoid water conflicts.

Power battery third-party reverse logistics provider selection: Fuzzy evidential reasoning

Massive power batteries (PBs) are crucial to new energy vehicle enterprises. Due to Extended Producer Responsibility (EPR), the third-party reverse logistics provider selection(3PRLPs) process has become an important decision to save cost. This paper uses an innovative combination of qualitative analysis and quantitative data integration to address the PB 3PRLPs problem by using Failure Modes and Effects Analysis (FMEA) and fuzzy evidential reasoning (FER). Firstly, the possible failures and potential effects in the PB 3PRLPs are identified by the FMEA to determine criteria and importance grades. Subsequently, AHP is utilized to calculate the criteria weight based on the importance of grades. FER is creatively applied to address the intersection of assessment grades and allocate the belief degree (BD) of the interaction to fuse heterogeneous data. Additionally, sensitivity analysis is done to look into the stability of the sequencing. Compared with other methods, the proposed method not only solves the subjectivity of AHP weighting but also manipulates probabilistic and fuzzy uncertainties for multi-criteria decision-making (MCDM). This method is useful in quantitatively analyzing the 3PRLPs problem and in providing auxiliary decision support for enterprises.

A Cognitive Fuzzy Evidential Reasoning Approach for Multiexpert Multicriterion Decision Making

A Cognitive Fuzzy Evidential Reasoning Approach for Multiexpert Multicriterion Decision Making

Enhancing fuzzy evidential reasoning approach using dynamic adjustment mechanism and new rule-based transformation for engineering emergency response evaluation

With the frequent occurrence of various emergency events in engineering field, engineering emergency response (EER) evaluation plays an increasingly significant role in handling such situations and provides great challenges to research since the uncertain information and the urgent response time. Aiming at achieving timely and effective emergency response, an enhanced evidential reasoning (ER) approach based on the dynamic adjustment mechanism and new rule-based transformation is proposed. First, the linguistic terms to represent various preference information provided by experts are encoded into the trapezoidal interval type-2 fuzzy sets (TrIT2FSs) with different granularities. Second, for ensuring the validity of the information, based on the definition of the expert decision risk preference coefficients, a dynamic adjustment mechanism is constructed to identify and adjust the preference information. Meanwhile, combined with social network, the experts’ weights can be calculated and revised several times to obtain group information. Then, a new rule-based transformation and related optimization models are proposed to convert the TrIT2FSs into interval belief structures. Furthermore, considering the importance of attributes, the relative weights and interval belief structures are combined. Finally, according to integrated interval belief structures obtained by the analytical ER algorithm, a new ranking approach with the optimism degree and decision tendency degree is constructed to rank the interval expected utility and score utility of each alternative. To further show the effectiveness, superiorities, and stability of the proposed method, a case study on the EER evaluation is preformed and some comparisons and discussions are provided.

인천항 일반부두의 위험평가에 관한 연구

The port risk evaluation applied in this study is a hybrid approach of Fuzzy Evidential Reasoning and Utility Techniques. The Analytic Hierarchy Process was incorporated to evaluate each risk factor’s importance and to yield the weighted risk level of the individual risk factor and the overall risk level. The interpretation of the results of the hybrid approach is straightforward. This feature enables port risk managers to identify the strengths and weaknesses of the ports regarding each risk factor, upper-level risk factors, and overall risk. This offers the necessary insight to determine which areas require special attention for risk mitigation. Consequently, the results obtained by the risk evaluation model are expected to be used by port risk managers as diagnostic instruments to mitigate port risk or to improve risk management practices.

Vulnerability assessment of strait/canals in maritime transportation using fuzzy evidential reasoning approach.

The safety and security of straits and canals have been playing an important role in maritime transportation. The disruption of a strait or canal will lead to increased transportation costs and world trade problems. Therefore, an advanced approach incorporating fuzzy logic and an evidential reasoning (ER) algorithm is developed to conduct the vulnerability assessment of straits or canals in this paper. A hierarchical structure is first developed taking into account both qualitative and quantitative factors. The fuzzy rule-based transformation technique is applied to convert quantitative factors into qualitative ones, which enables the application of a fuzzy ER method to synthesize all the information from the bottom to the top along the developed hierarchical structure. The software of intelligent decision system (IDS) is used to facilitate the process of vulnerability assessment. The developed framework then is validated and demonstrated in a case study for vulnerability prioritization which can be used as a reference to ensure the safety and security of straits and canals for decision-makers.

A novel failure mode and effect analysis method using a flexible knowledge acquisition framework based on picture fuzzy sets

Failure mode and effect analysis (FMEA) is an effective reliability management tool for identifying potential failures in a system/component that has been widely utilized in various fields by combining fuzzy sets theory. However, the difficulty in assessment expression and acquisition, imprecision in assessment aggregation, and missing relationships among risk factors are prominent challenges in the existing fuzzy FMEA methods. Thus, this work employs the picture fuzzy sets (PFSs) theory to meet these challenges, allowing experts to express assessments more efficiently and accurately. Meanwhile, we propose a novel FMEA method to improve the three essential processes of FMEA, involving the following steps. Firstly, a flexible knowledge acquisition framework (FKAF) is established to simplify the expert evaluation process, allowing experts to express fuzzy information with various forms of non-fuzzy assessments. Then, a strategy-based picture fuzzy conversion (PFC) method is developed to standardize the non-fuzzy values to picture fuzzy numbers (PFNs). Secondly, to improve the assessment aggregation accuracy in uncertain environments, we suggest the picture fuzzy evidential reasoning (PFER) method that extend the existing fuzzy evidential reasoning (FER) methods. Thirdly, to completely describe the parallel and causal relationships among risk factors, four alternative models are established using picture fuzzy Petri nets (PFPNs) and risk priority rankings are determined through inference. Finally, the effectiveness and superiority of the proposed FMEA method are verified through two case studies and one extended experiment, demonstrating that it overcomes the shortcomings of the existing FMEA methods and reduces application costs while ensuring the rationality of rankings.

An intuitionistic fuzzy probabilistic Petri net method for risk assessment on submarine pipeline leakage failure

Under the influence of various factors, the submarine pipelines will be damaged, resulting in oil and gas leakage accidents. It is essential to perform risk assessments to prevent accidents. However, accurate and sufficient historical data usually can not be obtained. To solve this problem, this study proposes a probabilistic Petri net (PPN) method based on intuitionistic fuzzy evidential reasoning (IFER), which can determine the failure probability of basic events through domain expert experience, and quantitatively evaluate the probability of submarine pipeline leakage failure. The main contributes include: according to DNV-RP-F107, a group of intuitionistic fuzzy numbers is defined and an improved probability conversion method is proposed for defuzzification; an IFER approach is proposed, which uses belief structures as expert linguistic terms and transforms them into IFNs to deal with the uncertainty of expert evaluation; a probabilistic Petri net method is proposed to quantitatively calculate the failure probability of top event and identify the critical events. Finally, the proposed intuitionistic fuzzy probabilistic Petri net (IFPPN) method is illustrated by a case study. By comparing with the existing methods, the feasibility of this method is validated, and the evaluation results can provide a reference for the safe operation of the pipeline system.

An early warning method based on fuzzy evidential reasoning considering heterogeneous information

Early warning is an effective way to eliminate or reduce the losses and negative impacts caused by hazards in advance, which has important practical significance in real world situations. It has drawn great attention from government and academy in recent years. Information related to hazards plays an important role in early warning process, there are different types of such information in real world situations. However, existing early warning studies employ just single information type to describe the hazards, it might not describe the hazards properly and comprehensively due to the complex and uncertain of real world situations. Therefore, it seems necessary and reasonable to consider different types of information related to hazards in the early warning process. To fill up the gap, this study proposes a novel early warning method that considers heterogeneous information from both quantitative and qualitative contexts including experts’ hesitancy. On such basis, fuzzy evidential reasoning approach is utilized due to its capacity not only to fuse different types of information, but also to keep as much information as possible during the fusion process. Finally, an illustrate example together with related discussions are presented to demonstrate the validity, feasibility and novelty of the proposed method. The proposed early warning method can provide more safety status information of early warning objectives, which can be used to not only forecast and warn different types of hazards, but also conduct the risk analysis and assessment. It has widely potential application values in real world situations.

Risk Assessment of Constructing Deep Foundation Pits for Metro Stations Based on Fuzzy Evidence Reasoning and Two-tuple Linguistic Analytic Network Process

Accidents occur frequently while constructing deep foundation pits for metro stations, thereby risking substantial economic losses and casualties. To subject the construction of such pits to scientific and rational risk assessment and overcome the limitations of existing risk evaluation models and risk fusion problems, proposed here is a risk-assessment model for such pits based on fuzzy evidential reasoning and the two-tuple linguistic analytic network process (TL-ANP). First, the risk loss indicators are optimized, the weights of different risk events and of each risk loss indicator in the metro-station deep-foundation-pit construction project are calculated using TL-ANP, and trapezoidal fuzzy numbers are used to describe the occurrence probability of each risk event and loss. Second, relying on a table of expert weight indices, the best–worst method based on generalized interval-valued trapezoidal fuzzy numbers is used to determine the weights of experts. Finally, the overall risk grade of the construction project is evaluated by aggregating the risk levels of all risk events through an evidence-reasoning algorithm. The analysis results for a deep foundation pit for a station on Line 5 of Nanning Metro show that the model provides a quantitative basis for determining expert weights and risk loss weights reasonably and improving the reliability of the evaluation system. Also, not only does applying the method show that such a construction project can be judged as having a certain risk grade, but more importantly it can identify the key factors and loss indicators affecting the overall risk grade of the pit, whereupon risk control measures can be adopted in a targeted manner. In comparison with traditional methods, the proposed method is shown to be practical and effective, providing a reference basis for analyzing the risks of similar projects in the future and guaranteeing construction safety.

On the determination and rank for the environmental risk aspects for ship navigating in the Arctic based on big Earth data.

The unique, ambiguous, and complex navigable environment determines the essential difference between Arctic shipping routes and conventional routes in regard to safety issues. To achieve a scientific understanding of the characteristics and variations of environmental risks involved in the Arctic shipping, it is essential to rationally address the uncertainty and incompleteness of environment-related risk information. In this study, fuzzy evidential reasoning is introduced to carry out multisource heterogeneous data fusion and spatiotemporal dynamic assessment of navigable environmental risks for Arctic shipping routes. Based on big Earth data collected from the European Center for Medium-Range Weather Forecasts, National Snow And Ice Data Center, National Center for Environmental Information, and University of Bremen from 2012 to 2019, a case study of the Northeast Passage is considered to demonstrate the feasibility of the proposed methodology. Finally, the results are described from three aspects: spatial distribution, temporal changes, and sensitivity analysis, with consideration of the entire passage and five marginal seas at the same time. Based on these findings, the prospect of application of big Earth data in risk assessment is further discussed from two aspects of knowledge acquisition by big data and risk analysis at different scales, to inspire sustainable development of Arctic shipping.

Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory

In the medical field, the detection of breast cancer may be a mysterious task. Physicians must deduce a conclusion from a significantly vague knowledge base. A mammogram can offer early diagnosis at a low cost if the breasts' satisfactory mammogram images are analyzed. A multi-decision Intuitionistic Fuzzy Evidential Reasoning (IFER) approach is introduced in this paper to deal with imprecise mammogram classification efficiently. The proposed IFER approach combines intuitionistic trapezoidal fuzzy numbers and inclusion measures to improve representation and reasoning accuracy. The results of the proposed technique are approved through simulation. The simulation is created utilizing MATLAB software. The screening results are classified and finally grouped into three categories: normal, malignant, and benign. Simulation results show that this IFER method performs classification with accuracy almost 95% compared to the already existing algorithms. The IFER mammography provides high accuracy in providing early diagnosis, and it is a convenient diagnostic tool for physicians.

A novel risk assessment approach for strait/canal security evaluation along the 21st Century Maritime Silk Road

The 21st Century Maritime Silk Road (MSR) designed by China is of great importance to maritime transportation, economic development, and environmental protection. However, the straits/canals along the MSR have been struggling with pirate attacks, terrorism, and accidents which pose challenges for the security of MSR. There is a strong need for further investigation in strait/canal security evaluation. However, the traditional risk modelling approach used in risk assessment indicates challenges due to its incapability of dealing with incomplete data, uncertainties, and subjective judgment. Thus, we propose a novel strait/canal security assessment framework to evaluate the security of the strait/canal along the MSR on the basis of a fuzzy evidential reasoning approach. The subjective risk analysis information collection and processing process from multiple experts can be embedded in the framework in a systematic way to provide maritime stakeholders to evaluate maritime security along the MSR. The results provide decision makers with useful insights and standard tools on enhancing strait/canal security, effective routes planning as well as operational efficiency.

The evidential reasoning approach for renewable energy resources evaluation under interval type-2 fuzzy uncertainty

Selecting the right renewable energy resources (RESs) is emerging as a solution to alleviate energy crisis and environmental pollution. Due to the limitation of human knowledge and the complexity of reality, the selection process usually involves multiple uncertainties. In this paper, an interval type-2 fuzzy evidential reasoning approach is proposed to solve the RESs evaluation problem with uncertain information. First, the linguistic terms involved in the RESs evaluation process are encoded into the interval type-2 fuzzy sets (IT2FSs). Second, a new interval type-2 fuzzy distance model is developed to measure the distance between the IT2FSs. After obtaining the distance, two new information transformation techniques are respectively defined to transform the IT2FSs and the crisp numbers into the interval belief structures. Then, an interval type-2 fuzzy entropy measure is proposed to determine the weights of attributes and the corresponding axioms are proved mathematically. Finally, the interval expected utility of each alternative is generated by a pair of nonlinear optimization models and then ranked by an enhanced minimax regret approach. A case study about the RESs evaluation is provided to illustrate the effectiveness of the proposed approach, comparisons and discussions are also conducted to show the superiority.

Assessing the Outbreak Risk of Epidemics Using Fuzzy Evidential Reasoning.

Epidemic diseases (EDs) present a significant but challenging risk endangering public health, evidenced by the outbreak of COVID‐19. Compared to other risks affecting public health such as flooding, EDs attract little attention in terms of risk assessment in the current literature. It does not well respond to the high practical demand for advanced techniques capable of tackling ED risks. To bridge this gap, an adapted fuzzy evidence reasoning method is proposed to realize the quantitative analysis of ED outbreak risk assessment (EDRA) with high uncertainty in risk data. The novelty of this article lies in (1) taking the lead to establish the outbreak risk evaluation system of epidemics covering the whole epidemic developing process, (2) combining quantitative and qualitative analysis in the fields of epidemic risk evaluation, (3) collecting substantial first‐hand data by reviewing transaction data and interviewing the frontier experts and policymakers from Chinese Centers for Disease Control and Chinese National Medical Products Administration. This work provides useful insights for the regulatory bodies to (1) understand the risk levels of different EDs in a quantitative manner and (2) the sensitivity of different EDs to the identified risk factors for their effective control. For instance, in the case study, we use real data to disclose that influenza has the highest breakout risk level in Beijing. The proposed method also provides a potential tool for evaluating the outbreak risk of COVID‐19.

Performance measurement of sustainable freight transportation: a consensus model and FERA approach

Sustainable freight transportation is aimed at reducing environmental emissions and social inequity along with economic inefficiency to drive the business. Lack of a continuous sustainability assessment and monitoring tool increases the reluctance of freight transporters to adopt world-class sustainability practices. This paper attempts to develop the performance index for a sustainable freight transportation system by innovatively integrating the Consensus Model (CM) with the Fuzzy Evidential Reasoning Algorithm (FERA). A CM has been used to determine the degree of importance of each Key Performance Indicators identified across three dimensions of sustainability. FERA has been used to aggregate subjective judgments with crisp quantitative values. This approach has a unique capability to handle various uncertainties related to impreciseness in decision-making. This study has demonstrated the use of an integrated approach for developing a performance index of the freight transportation system. Sensitivity Analysis of the model provides logical inferences and an understanding of the robustness of the model outputs. It can be interpreted from the results of the study that the firms with higher profitability in the market are focusing more on making their operations sustainable. A proportionate approach of firms towards the economic, environment, and societal well-being, may assist in achieving higher sustainability.

Performance assessment framework based on competitive priorities for sustainable freight transportation systems

Stringent environmental norms and growing demands for energy-efficient and cost-effective freight shipping have put organizations under immense pressure to consider sustainability aspects while assessing their performance. Moreover, amid growing awareness about competitive priorities, it has become prudent to understand how an organization’s sustainability performance is balanced against competitive priorities. Therefore, this study attempts to develop an integrated performance assessment framework (PAF) on the basis of competitive priorities for sustainable freight transportation (SFT) systems. A unified approach consisting of fuzzy group decision-making, fuzzy evidential reasoning approach, and expected utility concept is utilized to assess critical success factors of SFT based on four competitive priorities. The applicability of the developed model is exhibited through a case example, and sensitivity analysis is also performed to examine its robustness. This competitive performance score based PAF would facilitate logistics professionals in selecting the most competitive SFT system.