Probabilistic Linguistic Term Research Articles

Analyzing the risk of the ammonia storage facility using extended FMEA model based on probabilistic linguistic GLDS method with consensus reaching

Due to the energy crisis, ammonia stands out as an efficient hydrogen energy carrier. However, liquid ammonia's nature of toxicity, corrosiveness, volatility, flammability, etc., make its storage highly risky. It is essential to analyze the risk of the ammonia storage facility for its safety improvement. Failure mode and effect analysis (FMEA) has been a popular risk analysis method due to its ease of implementation. However, traditional FMEA is insufficient to model the uncertainty of risk assessment information, enhance the consensus among experts, aggregate experts' risk evaluation information, and derive reliable risk analysis results. To address these limitations, we introduce a hybrid probabilistic linguistic FMEA framework to analyze failure risk in the ammonia storage facility. First, the probabilistic linguistic terms (PLTs) are introduced to express uncertain risk evaluation information of failure modes. Second, a risk acceptability-based consensus-reaching process is introduced to measure experts' consensus on each failure mode's risk and improve their consensus. Then, a consensus degree-based ordered weighted averaging operator is introduced to fuse experts' risk evaluation information, where experts' weight is determined by their consensus degree. Furthermore, the gained and lost dominance score method is improved on the basis of the expected value of the PLTs to prioritize failure modes' risk. Finally, a case study of the risk analysis for the ammonia storage facility is performed to show the efficiency of the extended FMEA. The case study indicated that condenser cooling water pump failure and failure of level transfer are the top two failure modes and have risk scores of 0.126 and 0.091, respectively.

Consensus-based generalized TODIM approach for occupational health and safety risk analysis with opinion interactions

Occupational health and safety (OHS) risk analysis serves as a foundation for identifying, preventing, and controlling OHS hazards to reduce occupational accidents. As a representative risk analysis approach, Fine-Kinney has been commonly applied to control hazards. However, current Fine-Kinney studies ranked hazards without considering the consensus reaching process (CRP) with incomplete information, insufficient to tackle decision makers’ (DMs’) dissatisfaction. Besides, risk analysis mainly relies on DMs’ subjective assessments, where opinion interactions inevitably exist because of DMs’ communication during the assessment process. This paper aims to develop a hybrid generalized TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) approach in the Fine-Kinney framework, integrating CRP with dynamic social influence network (SIN), and probabilistic linguistic terms (PLTSs). The PLTSs are used to cope with the complex and incomplete DMs’ opinions. The dynamic SIN is proposed to calculate the weights of DMs and describe the opinion interactions considering the psychological behaviors of DMs. Then, a new CRP is developed including a two-fold personalized feedback mechanism based on the dynamic SIN. The generalized TODIM method is introduced to rank all identified potential occupational hazards based on the collective opinions after CRP. Finally, a numerical example is conducted to verify the efficiency of the proposed approach. Comparison and sensitivity studies are also carried out to test the rationality and efficiency of the proposed approach.

Evaluation of Emergency Intelligence Capability of Major Public Health Events in Probabilistic Uncertain Language Environment

Emergency intelligence capability is an important index reflecting the intelligence level of emergency management. The accuracy of emergency intelligence capability evaluation is related to the scientific nature of emergency decision-making. By analyzing the operation process and mechanism of the emergency intelligence system for major public health events, this paper establishes the evaluation index system of the emergency intelligence capability. By using the decision method of VIKOR and considering the preference of experts in the evaluation process, this paper proposes the evaluation model based on the probabilistic uncertain language environment. The use of probabilistic uncertain linguistic term set (PULTS) to describe the uncertain information is helpful to improve the scientific and accuracy of emergency rescue decision-making of major public health events and then realize the organic unity of emergency information and scientific decision-making of public health events.

Consensus model based on probability K-means clustering algorithm for large scale group decision making

Nowadays, the increasing complexity of the social environment brings much difficulty in group decision making. The more uncertainty exists in a decision-making problem, the more collective wisdom is needed. Therefore, large scale group decision making has attracted a lot of researchers to investigate. Since the probabilistic linguistic terms have impressive performance in expressing DMs’ opinions, this paper proposes a novel method for large scale group decision making with probabilistic linguistic preference relations. More specifically, (1) a probability k-means clustering algorithm is introduced to segment DMs with similar features into different sub-groups; (2) an integration method is proposed to construct the collective probabilistic preference relation that retains initial information to the most extent; (3) taking the personality of each DM into account, a consensus model is constructed to improve the rationality and efficiency of consensus reaching process. Several simulation experiments are designed to analyze the influence factor in the feedback mechanism and make some comparative analysis with the existing method. Finally, an illustrative example of contractor selection is conducted to verify the validity of the proposed method.

Environmental, Social and Governance Investing Assessment Under Incomplete Information by the Power Aggregation Model on Probabilistic Linguistic Terms

Environmental, social and governance(ESG) investing is being a popular strategy in global financial markets, which includes more comprehensive concerns over the environmental, social and governance aspects of the investment targets. However, how to evaluate ESG performance faces multiple challenges because of incomplete information, subjective judgments and multiple attribute decision-making problems. To address those issues, we develop a new multi-attribute group decision making (MAGDM) model based on power average of probabilistic linguistic terms (PLTs), not only can take advantage of PLTs in expressing the information by human natural language, but also effectively aggregates information from the individual evaluations under ambiguous market. The validity and advantages of our model for ranking ESG alternatives can be illustrated by the numerical study in the end.

Resilience Strategy Optimization for Large Aircraft Supply Chain Based on Probabilistic Language QFD

This paper proposes an optimization model of supply chain resilience strategy for large passenger aircraft. A quality function deployment (QFD) framework is conducted to analyze the resilience of the large passenger aircraft supply chain, and the key parameters are characterized based on the probabilistic linguistic term. Then based on the output of the QFD framework an optimization model of the resilience strategy considering the stochastic disturbance faced by the supply chain is constructed. Taking the supply chain for large aircraft cockpit control display module as an example to illustrate the application steps and feasibility of the model, the results demonstrate that change of supply chain management responsibilities, implementing hierarchical management of suppliers, seeking coordinated implementation of inventory management mode, and improving the pre-risk identification system, play prominent roles in enhancing supply chain resilience, and the combination of different strategies can indeed enhance the supply chain resilience under the budget constraint.

Probabilistic-based expressions in behavioral multi-attribute decision making considering pre-evaluation

A behavioral multi-attribute decision making (BMADM) problem with probabilistic-based expressions is studied by considering decision-maker’s (DM) risk attitude and pre-evaluation. With consideration of information expressions for uncertainty, probabilistic interval numbers (PINs) and probabilistic linguistic terms (PLTs) are utilized to depict pre-evaluation information with respect to quantitative and qualitative attributes, respectively. Then surrounding the two kinds of probabilistic-based expressions, we propose a BMADM method with DM’s risk attitude being included based on regret theory. First, through taking into account characteristics of risk, we develop a basic utility function and a regret–rejoice function by considering risk-averse, risk-neutral and risk-seeking preference coefficients. Second, risk-based utility functions are examined for measuring PINs and PLTs. The third element is the establishment of optimization models for handling probability incompleteness to fully utilize the information. In the fourth step, a weighted comprehensive risk-based utility measurement is presented as a basis for making a selection. The final phase of the research is the application of the proposed method to one case, along with sensitivity and comparative analyses, as a means of illustrating the applicability and feasibility of the new method.

A Probabilistic Linguistic Group Decision-Making Method From a Reliability Perspective Based on Evidential Reasoning

A probabilistic linguistic multiattribute group decision-making (PLMAGDM) problem is studied from a reliability perspective based on an evidential reasoning approach and linguistic granulation optimization. As decision-making reliability, indicating the validity and accuracy, greatly depends on the reliabilities of both group similarity and the degree of familiarity, it is helpful to study a decision-making problem from the perspective of information reliability. To this end, a PLMAGDM method which considers reliability is put forward in this paper. First, two sets of information (i.e., probabilistic linguistic terms on alternative grades, and linguistic terms on familiarity) are given by experts as measures of expert reliability and attribute reliability. A granulation optimization model is then constructed to generate the utility of grades while maximizing group similarity, and a model that redistributes the probabilities of grades is established for the situation of incomplete information. We further provide another granulation optimization model to generate the degree of familiarity. Additionally, using the evidential reasoning rule, the aggregated weights of experts are obtained employing an optimization method that is used to retrieve attribute information. Lastly, through the method comparison in two cases as well as the sensitivity analysis, the validity and applicability of the method are verified.

Particle swarm optimization for trust relationship based social network group decision making under a probabilistic linguistic environment

Group decision making (GDM) problems require consensus reaching processes; however, these can be time consuming and costly. As experts change their evaluations after exchanging opinions and being influenced by others, these influences are spread across the various expert trust relationships. Because of the experts’ knowledge limits, the evaluations on the alternatives and the trust relationships are generally described using probabilistic linguistic terms. Therefore, to simplify the decision making process and avoid decision bias, this paper proposes a particle swarm optimization method that incorporates a trust relationship based social network for GDM under a probabilistic linguistic environment. Each expert is regarded as a particle that moves toward the final evaluation and reaches the threshold. A fitness function is built to measure the consensus levels, and the updated function is improved by the trust relationships to derive the new evaluations. A numerical example is then given to illustrate the feasibility of the proposed approach and comparisons given to further elucidate its novelty and validity.