Emergency Decision-making Process Research Articles

Picture fuzzy prospect theory-MAIRCA method considering optimal reference point for emergency decision-making

In the emergency decision-making process, decision-makers usually cannot give rational evaluations, and existing decision-making methods do not adequately consider the risk attitude of decision-makers either. To solve these problems, a combined method based on the prospect theory and the multi-attributive ideal-real comparative analysis (MAIRCA) method is put forward in the picture fuzzy environment. Firstly, the optimal aggregation (OA) model is proposed to obtain the ideal evaluations with the least disagreement among decision-makers. Regarding the evaluations as reference systems, the OA-based prospect theory is put forward, which could calculate the prospect matrix more reasonably. Secondly, considering the prospect matrix and alternative preference, the improved MAIRCA method is proposed, which overcomes the defects of theory and has the better ranking ability. Then, the OA-based prospect theory-MAIRCA method is further put forward to effectively complete the decision-making process with risk attitudes. Finally, an illustrative example of earthquake emergency assessment and a series of comparative experiments are presented. The analyses of results show that the proposed method has great guiding significance in the field of emergency decision-making management.

A task-level emergency experience reuse method for freeway accidents onsite disposal with policy distilled reinforcement learning

A large number of freeway accident disposals are well-recorded by accident reports and surveillance videos, but it is not easy to get the emergency experience reused from past recorded accidents. To reuse emergency experience for better emergency decision-making, this paper proposed a knowledge-based experience transfer method to transfer task-level freeway accident disposal experience via multi-agent reinforcement learning algorithm with policy distillation. First, the Markov decision process is used to simulate the emergency decision-making process of multi-type freeway accident scene at the task level. Then, an adaptive knowledge transfer method named policy distilled multi-agent deep deterministic policy gradient (PD-MADDPG) algorithm is proposed to reuse experience from past freeway accident records to current accidents for fast decision-making and optimal onsite disposal. The performance of the proposed algorithm is evaluated on instantiated cases of freeway accidents that occurred on the freeway in Shaanxi Province, China. Aside from achieving better emergency decisions performance than various typical decision-making methods, the result shows decision maker with transferred knowledge owns 65.22%, 11.37%, 9.23%, 7.76% and 1.71% higher average reward than those without in the five studied cases, respectively. Indicating that the emergency experience transferred from past accidents contributes to fast emergency decision-making and optimal accident onsite disposal.

In Flight Emergency Decision-making Process: Does Intuition Matter?

Objective. This research aimed to describe the origins and contributions of pilot intuition in the decision-making process and gain knowledge on the urgency and importance of pilot intuition in decision-making.Method. The qualitative methodology used by the researcher is presented along with the phenomenology approach. Three pilots participated in the subject research. In-depth interviews, observation, and data documentation were used to collect the data. The researcher used the triangulation technique (other researchers, methods, theories, and data resources) to affirm data quality and results.Result. The research result showed that the pilot's intuitive decisions to take necessary action are based on ‘unconscious thought’ at the beginning of decision-making. However, it develops a more cognitive process where a pilot has time-critical analysis to rationalize his intuitive yet best decision. Such a decision-making process leads to prompt spontaneous and automatic action in the event of emergencies where data and information needed are lacking.
 Intuitive decisions and involuntary action are closely associated with the experience and knowledge of pilots in aviation. In an intuitive decision-making process, when time is limited and information/data is adequate, an analysis process can help a pilot identify the problem pattern and help them evaluate possible ideal decisions. An analysis process cannot replace intuition which plays a crucial role in the decision-making process during an emergency.Conclusion. A pilot’s intuition plays a strategic role in decision-making because aviation conditions are dynamic,situational, and constantly changing. Pilot’s intuition can be developed by sharpening their awareness of active and evolving aviation situations (situational and safety awareness). In addition, pilots must build longer flying hours, attend training, and expand their knowledge of aviation policies and procedures.
 
 

A probabilistic linguistic opinion dynamics method based on the DeGroot model for emergency decision-making in response to COVID-19

Emergency decision-making entails a multi-criteria problem with a short period and urgent events, which creates difficulties for decision makers to undertake an optimal decision. To ensure the validity and rationality of decision results, the probabilistic linguistic term set is adopted to represent the evaluation information of experts because it can assign different probabilities or importance to different linguistic terms, which is closely related to human cognition. In addition, to portray the dynamic changes in the emergency decision-making process, this study develops a new dynamics method based on the DeGroot model with probabilistic linguistic information. First, to simulate the transition matrix of probabilistic linguistic opinions, the basic operational rules are defined based on the transformation function and expectation function. Next, three forms of influence matrices incorporating similarity, self-persistence, and authority degrees are constructed, and the consensus conditions of the models are discussed. Then, considering the social networks and incomplete trust relationships between experts, a fourth trust-based influence matrix is devised. A case study of emergency decision-making for assessing response plans to COVID-19 is performed to verify the feasibility and effectiveness of the dynamic method. Furthermore, a sensitivity analysis is conducted. Finally, comparisons with classical methods are performed to illustrate the superiorities of the proposed algorithms.

New method for emergency decision making with an integrated regret theory-EDAS method in 2-tuple spherical linguistic environment.

With the frequent occurrence of various emergency events, emergency decision making (EDM) has become an important research focus recently and many studies have been conducted to decrease the negative impact of emergencies. Normally, it is essential for decision makers to make satisfactory and reasonable emergency decisions in the shortest possible time as inappropriate decisions may result in enormous economic losses and serious social consequences. To ensure that an emergency response can be made efficiently, we propose a new EDM method by integrating regret theory and evaluation based on distance from average solution (EDAS) method within the 2-tuple spherical linguistic environment. First, the 2-tuple spherical linguistic term sets (TSLTSs) are employed by decision makers to express their uncertain and vague evaluation information on emergency alternatives. Then, an integrated EDM method based on regret theory and EDAS method is proposed to rank emergency alternatives and find out the optimal one. Besides, the criteria importance through inter-criteria correlation (CRITIC) method is used to determine criteria weights objectively in the EDM process. Finally, the proposed regret theory-EDAS method is applied to select the optimal response solution for a public health emergency in China. The superiority and practicality of the designed method are further justified through a comparative analysis with other EDM methods.

Ambiguity-incorporated opinion formation model for multi-risk large-group emergency decision-making in social networks

PurposeVarious decision opinions comprise the foundation of emergency decision-making. However, decision-makers have difficulty establishing trust relationships within a short time because of decision-making groups being temporary. The paper aims to develop an ambiguity-incorporated opinion formation model that considers ambiguous opinions on relevant risks from a psychological perspective during the consensus reaching process.Design/methodology/approachAddressing the problem of forming a consensus decision-making opinion in an ambiguous environment and relevant risk opinions, different social network structures were first proposed. Subsequently, psychological factors affecting the decision-makers' perception of ambiguous opinions and tolerance for ambiguity under the multi-risk factors were considered. Accordingly, an ambiguity-incorporated opinion formation model was proposed by considering the ambiguity and relevant opinions on multi-risk factors.FindingsA comparison between the ambiguity-incorporated opinion formation model and the F–J model illustrates the superiority of the proposed model. By applying the two types of network structures in the simulation process, the results indicate that the convergence of opinions will be affected by different decision-making network structures.Originality/valueThe research provides a novel opinion formation model incorporating psychological factors and relevant opinions in the emergency decision-making process and provides decision support for practitioners to quantify the influence of ambiguous opinions. The research allows the practitioners to be aware of the influence of different social network structures on opinion formation and avoid inaccurate opinion formation due to unreasonable grouping in emergency decision-making.

Emergency Decision-making Method of Unconventional Emergencies in Higher Education Based on Intensive Learning.

In order to improve the emergency decision-making and management ability of unconventional emergencies in Colleges and universities, an emergency decision-making method for unconventional emergencies in Colleges and Universities based on reinforcement learning is proposed. The function of the emergency decision-making model of unconventional emergencies in Colleges and universities is constructed, and the optimal control of the emergency decision-making process of unconventional emergencies in Colleges and universities is realized by using the queuing theory model. Based on the sub-sequence decomposition method, the reinforcement learning function is optimized. Combined with big data scheduling and the empirical mode decomposition method, the effective probability density function of emergency decision-making for unconventional emergencies in higher education is calculated, and the optimal solution vector analysis of emergency management and scheduling decision-making for unconventional emergencies in Colleges and universities is realized according to parameter estimation and quantitative optimization results. The test results show that this method has a good ability to optimize and schedule the emergency decision-making of unconventional emergencies in higher education, and has strong convergence. This method improves the emergency response-ability, and the time cost is short.

A Multistage Dynamic Emergency Decision-Making Method considering the Satisfaction under Uncertainty Information

Emergency decision-making (EDM) is of paramount importance, especially when the emergency occurs. The evolution nature of the emergency, such as multistage, uncertainty, dynamic, and information updating, has been playing a key role in the dynamic emergency decision-making process. However, most existing studies ignored the aforementioned nature. Our approach accounts for the dynamics inherent to a real emergency decision-making process and presents a multistage dynamic emergency decision-making (MSDEDM) procedure of a dynamic programming model based on decision-makers’ psychological reference satisfactory degree. Firstly, interval-valued trapezoidal intuitionistic fuzzy numbers (IVTrIFNs) are used to depict the relevant fuzziness and uncertainty of information. Secondly, by considering the dynamic evolution process of emergency and the decision-makers’ psychological reference expectation effect, the principle of MSDEDM approach is presented. Based on the analysis, the dynamic model on the new psychological reference satisfactory parameter formula is presented to obtain the optimal satisfaction and weight of each stage. Then, the value utility function based on the DMs’ risk attitude is proposed to obtain the comprehensive value of each emergency alternative for each stage and achieve the ranking results of each stage. Furthermore, a case study involving the transportation emergency decision-making problem demonstrates that the proposed method can achieve selection of the optimal alternatives for each stage, as well as adjustment of the alternatives for neighbouring stages. Finally, the comparative analysis and sensitivity analysis for the results are used to further verify the feasibility and practicability of the proposed method.

Heterogeneous multi-attribute case retrieval method based on group decision making using incomplete weight information

Case-based reasoning (CBR) is one of the most popular methods used in emergency decision making (EDM). Case retrieval plays a key role in EDM processes based on CBR and usually functions by retrieving similar historical cases using similarity measurements. Decision makers (DMs), thus, choose the most appropriate historical cases. Although uncertainty and fuzziness are present in the EDM process, in-depth research on these issues is still lacking. In this study, a heterogeneous multi-attribute case retrieval method based on group decision making (GDM) with incomplete weight information is developed. First, the case similarities between historical and target cases are calculated, and a set of similar historical cases is constructed. Six formats of case attributes are considered, namely crisp numbers, interval numbers, linguistic variables, intuitionistic fuzzy numbers, single-valued neutrosophic numbers (NNs) and interval-valued NNs. Next, the evaluation information from the DMs is expressed using single-valued NNs. Additionally, the evaluation utilities of similar historical cases are obtained by aggregating the evaluation information. The comprehensive utilities of similar historical cases are obtained using case similarities and evaluation utilities. In this process, the weights of incomplete information are determined by constructing optimization models. Furthermore, the most appropriate similar historical case is selected according to the comprehensive utilities. Finally, the proposed method is demonstrated using two examples; its performance is then compared with those of other similar methods to demonstrate its validity and efficacy.

Emergency Decision-Making Based on q-Rung Orthopair Fuzzy Rough Aggregation Information

With the frequent occurrences of emergency events, emergency decision making (EDM) plays an increasingly significant role in coping with such situations and has become an important and challenging research area in recent times. It is essential for decision makers to make reliable and reasonable emergency decisions within a short span of time, since inappropriate decisions may result in enormous economic losses and social disorder. To handle emergency effectively and quickly, this paper proposes a new EDM method based on the novel concept of q-rung orthopair fuzzy rough (q-ROPR) set. A novel list of q-ROFR aggregation information, detailed description of the fundamental characteristics of the developed aggregation operators and the q-ROFR entropy measure that determine the unknown weight information of decision makers as well as the criteria weights are specified. Further an algorithm is given to tackle the uncertain scenario in emergency to give reliable and reasonable emergency decisions. By using proposed list of q-ROFR aggregation information all emergency alternatives are ranked to get the optimal one. Besides this, the q-ROFR entropy measure method is used to determine criteria and experts’ weights objectively in the EDM process. Finally, through an illustrative example of COVID-19 analysis is compared with existing EDM methods. The results verify the effectiveness and practicability of the proposed methodology.

Case Reasoning-Based Emergency Decision Making for Oil and Gas Accidents

Throughout the digitization of the petrochemical industry, the Beidou technology-based disaster monitoring, evaluation, and early warning network system has supported emergency decision making for oil and gas accidents. Many problems arise throughout the emergency decision-making process during oil–gas accidents, such as the limited time for decision making, high complexity, and inadequate emergency plans. Targeting these issues, we propose the construction of a case library using a Bayesian network. This way, when a new accident occurs, its similarity and deviation indexes could be matched against those of historical cases registered in the database. As such, the candidate cases are adjusted using a case combination and pruning method, yielding the final qualified case model. A case verification of the “11.22” Sinopec Oil pipeline leak and explosion in Qingdao reveals that the proposed method only requires an oil and gas accident database to be built in advance, eliminating the need for sampling data to make decisions, and reducing the search space. Using the proposed case-based reasoning, historical data and experience regarding oil and gas emergency decisions can be activated and reused, which would greatly improve the modeling efficiency of the Bayesian network.

OFF-SITE RADIOLOGICAL DOSE ASSESSMENT IN A SEVERE NUCLEAR ACCIDENT OF APR1400.

Shin Kori unit 3 of Korea Hydro and Nuclear Power Co. is the pressurized water reactor of advanced power reactor 1400 (APR1400) in the Republic of Korea. A hypothetical accident was assumed for Shin Kori unit 3 to perform the time-dependent dose calculations along with the release rates of radionuclides to the environment. In order to assess the accident scenarios, Radiological Assessment System for Consequence Analysis code was used, which is a set of tools for emergency response applications developed by US Nuclear Regulatory Commission. A straight-line Gaussian model was used for the near-field atmospheric calculations and a Lagrangian Gaussian puff model was used for the far-field simulations where released radioactive materials to the environment were transported, dispersed and deposited. In this study, long-term station blackout in spring, summer, autumn and winter season was considered in 2016-2018 based on reactor condition and seasonal effects. It was found that the worst season is the spring and the worst scenario found during the evening time of spring in 2017. The calculated maximum values of total effective dose equivalent (TEDE) and thyroid committed dose equivalent (CDE) are 22mSv and 390mSv, respectively, around 5km of precautionary action zone (PAZ) in 2days after the accident. According to Korean regulations on urgent public protective actions, for indoor sheltering, the criteria are 10mSv in 2 days. For public evacuation 50mSv in 1 week and iodine prophylaxis should be introduced if thyroid protection level is 100mSv. Calculated values of TEDE are more than double and thyroid CDE is almost four times in the PAZ compared with Korean regulations; hence, it was found that indoor sheltering and supply of iodine prophylaxis should be executed for short/mid-term protective measures. In this situation, indoor sheltering should be decided by off-site emergency management center, which was accountable for emergency decision-making process in nuclear accident under nuclear safety and security commission in Korea.

Addressing site selection for earthquake shelters with hesitant multiplicative linguistic preference relation

To reduce the damage caused by earthquakes, the paper develops a decision-making method under uncertainty, which validly addresses the site selection for earthquake shelters. Since site selection after earthquakes is an emergency decision-making process, during which there always exists inaccuracy and complexity, it is more rational to adopt fuzzy theory to handle this problem. Therefore, we introduce a hesitant multiplicative linguistic preference relation (HMLPR) and propose its consistency measure based on the eigenvector method. An adjustment method is provided to repair the unacceptably consistent HMLPR into acceptably consistent one, and the parameter of the adjustment method is also discussed. To manifest the validity and availability of the proposed method, we choose the Wenchuan Earthquake on May 12th 2008 as the background event. Upon establishing the indicators including scale and location, risk of disaster, rescue facilities, feasibility and resident aspect, three experts from Institute for Disaster Management and Reconstruction of Sichuan University are invited to address the site selection for temporary shelters. The obtained result is approved by the experts as the optimal choice in the case of this paper. Finally, we complete the comparative analysis. The processing efficiency of the proposed method keeps stable and fast when the number of experts and the number of criteria increase from 3 to 10 respectively, which demonstrates that the proposed method is more robust and efficient than the existing method.

Expected consistency-based emergency decision making with incomplete probabilistic linguistic preference relations

In emergency decision making, it can be difficult for decision-makers (DMs) to identify all possible scenarios due to a lack of information and the evolution of emergency situations. Therefore, this paper presents an incomplete probabilistic linguistic term set (InPLTS), which is a generalized hesitant fuzzy linguistic term set (HFLTS). The InPLTS can more appropriately describe a case in which a DM considers several possible linguistic terms with uncertain probabilities. Furthermore, this work extends the InPLTS to an incomplete probabilistic linguistic preference relation (InPLPR) and proposes a complete algorithm based on an emergency fault tree analysis (EFTA) to estimate missing entries of the InPLPR. The work also investigates the expected consistency, acceptable expected consistency, and consistency-improving methods for the reasonable application of the InPLPR. Then, a consistency-based emergency decision-making method using the InPLPR is proposed to address issues related to a lack of information, uncertainties and dynamic trends. In using this method, DMs can evaluate emergency alternatives of different possible scenarios with the InPLPR, and the impacts of different emergency responses on the evolution of emergencies can also be considered. Finally, the InPLPRs and the abovementioned method are applied to a public health emergency decision-making process to illustrate the advantages of the proposed method.

A Method of Emergent Event Evolution Reasoning Based on Ontology Cluster and Bayesian Network

Comprehensive real-time event information is critical to policymakers during emergency response and decision-making process. However, the development process of the emergent events has great uncertainty, and situational evolutions of emergencies are often difficult to use the fixed reasoning mode to attain. For this reason, this paper proposes a new method based on the ontology cluster for the evolution reasoning of emergency scenarios and extends the sematic web rule language to realize the scenario deduction, which can apply the Bayesian network to perform the conditional probability reasoning. A counterpart modeling and modifying of the Bayesian network optimization process is introduced. Besides, the probabilistic interpretation rules of atom components in context evolution are described with detailed query examples of emergency situation deducting and reasoning. The experimental results show that this approach is efficient in describing and capable of calculating the occurrence possibilities of the emergent events.

An Emergency Decision Making Method for Different Situation Response Based on Game Theory and Prospect Theory

Because of the continuous burst of emergency events (EEs) recently, emergency decision making (EDM) has become an active research topic due to its crucial role in relieving and reducing various losses and damages (property, lives, environment, etc.) caused by EEs. Current EDM studies based on prospect theory (PT) have considered decision maker’s (DM’s) psychological behavior, which is very important in the EDM process because it affects DM’s decision behavior directly, particularly under the uncertainty decision environment. However, those studies neglected an important fact that different emergency situations should be handled by different measures to show the pertinence and effectiveness of the emergency response in the real world, which has been taken into consideration in EDM studies based on game theory (GT). Different behavior experiments show that DMs usually have limited rationality when involved in risk and an uncertain decision environment, in which their psychological behavior has distinct impacts on their decision choice and behavior. Nevertheless, the existing studies of EDM based on GT build on an assumption that DMs are totally rational; however, it is obvious that such an assumption is unreasonable and far from the real-world situation. Motivated by these limitations pointed out previously, this study proposes a novel EDM method combining GT and PT that considers not only the DM’s psychological behavior, but also takes different situations’ handling for EEs into account, which is closer to the EDM problems in reality. An example and comparison with other methods are provided to demonstrate the validity and rationality of the proposed method for coping with real-world EDM problems.

AN AGENT-BASED MODEL TO REPRESENT SPACE-TIME PROPAGATION OF FOREST-FIRE SMOKE

Abstract. Agent-based modeling approaches have been used for various case studies related to the geospatial dynamics of complex systems. The proliferation of forest-fire smoke and the associated airborne particulate matter that behaves as complex systems make it important to develop reliable geospatial models that can simulate the propagation process to avoid impacts to human health and the environment. Therefore, the main objective of this research study is the development and implementation of an agent-based model (ABM) for the propagation of forest-fire smoke and other airborne particulate matter for use in studying patterns of spatio-temporal propagation. The developed ABM operates on a two-dimensional plane in the landscape where agents representing forest fires emit agents representing smoke. These smoke agents propagate through the study area based on measured atmospheric conditions. The model was developed using data from the 2017 forest fire season in British Columbia (BC) and parts of Alberta, Canada, particularly during the period August 10th–25th. The obtained simulation results provided patterns of spatio-temporal propagation of fire smoke over large areas of BC and Alberta, and were compared to the real smoke patterns covering the Edmonton metropolitan area, Canada on a similar date. The developed agent-based model can be used to support the emergency planning and decision-making process such as in regulating forest fire evacuations and in the prevention of health problems triggered by the exposure to smoke.

An Emergency Decision Making Method Based on Prospect Theory for Different Emergency Situations

Emergency decision making (EDM) is an effective way to deal with emergency situations because of its prominent role in alleviating the losses of properties and lives caused by emergency events. It has drawn increasing attention from both governments and academia, and become an important research topic in recent years. Studies show that decision makers are usually guided by bounded rationality under risk and uncertainty conditions. Their psychological behavior plays an important role in the decision making process, and EDM problems are usually characterized by high risk and uncertainty. Thus, decision makers’ psychological behavior has been considered in existing EDM approaches based on prospect theory. An emergency event might evolve into different situations due to its dynamic evolution, which is one of the distinctive features of emergency events. This important issue has been discussed in existing EDM approaches, in which different emergency situations are dealt with by devising different solutions. However, existing EDM approaches do not consider decision makers’ psychological behavior together with the different emergency situations and the different solutions. Motivated by such limitation, this study proposed a novel approach based on prospect theory considering emergency situations, which considers not only decision makers’ psychological behavior, but also different emergency situations in the EDM process. Two examples and related comparison are provided to illustrate the feasibility and validity of this approach.

Research on Railway Emergency Rescue Decision-Making Method Based on Clustering and SVD Algorithm

This paper applied a new method based on clustering and SVD to railway emergency rescue decision-making, for it can provide an applicable, intelligent and efficient decision-making method for railway emergency rescue with high-dimensional data. The method SVD has been used to process the feature attributes of cases according to feature attribute extraction of emergency cases. Then the paper structured a railway emergency rescue decision-making model based on clustering method. Following it, a specific example was used to illustrate the railway unconventional emergency decision-making process based on clustering and SVD method, and the results showed the clustering and SVD method can meet the railway emergency decision-making requirement to a large extent. This paper made up the shortage of speculative knowledge in this aspect, offered a new method and idea in the railway emergency decision support system intelligent area which mainly depends on the individual experience.

Study on Emergency Decision-Making of Natural Disaster Based on Collaboration of Multi-Agent

Based on the occurrence and evolution of the natural disaster is characteristic of uncertainty and complexity, in this article, Agent theory and technology is applied to emergency decision-making of natural disaster in China, built the disaster emergency collaborative decision-making framework based on multi-agent, design the collaborative decision-making model, discuss the emergency decision-making mechanism based on scenario-response, through effective collaboration based on multi-agent, achieve natural disaster dynamic emergency decision-making process.