Emergency Decision-making Problems Research Articles

A model for emergency supply management under extended EDAS method and spherical hesitant fuzzy soft aggregation information

Due to the frequent occurrence of numerous emergency events that have significantly damaged society and the economy, the need for emergency decision-making has been manifest recently. It assumes a controllable function when it is critical to limit property and personal catastrophes and lessen their negative consequences on the natural and social course of events. In emergency decision-making problems, the aggregation method is crucial, especially when there are more competing criteria. Based on these factors, we first introduced some basic concepts about SHFSS, and then we introduced some new aggregation operators such as the spherical hesitant fuzzy soft weighted average, spherical hesitant fuzzy soft ordered weighted average, spherical hesitant fuzzy weighted geometric aggregation, spherical hesitant fuzzy soft ordered weighted geometric aggregation, spherical hesitant fuzzy soft hybrid average, and spherical hesitant fuzzy soft hybrid geometric aggregation operator. The characteristics of these operators are also thoroughly covered. Also, an algorithm is developed within the spherical hesitant fuzzy soft environment. Furthermore, we extend our investigation to the Evaluation based on the Distance from Average Solution method in multiple attribute group decision-making with spherical hesitant fuzzy soft averaging operators. And a numerical illustration for “supply of emergency aid in post-flooding the situation” is given to show the accuracy of the mentioned work. Then a comparison between these operators and the EDAS method is also established in order to further highlight the superiority of the established work.

Non-Cooperative Behavior Management in Large-Scale Group Decision-Making Considering the Altruistic Behaviors of Experts and Its Application in Emergency Alternative Selection

Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns, leading to non-cooperative behaviors during the consensus-reaching process. Many studies on non-cooperative behavior management assumed that the maximum degree of cooperation of experts is to totally accept the revisions suggested by the moderator, which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process. In addition, when grouping a large group into subgroups by clustering methods, existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously. In this study, we introduce a clustering method considering the similarity of evaluation values and the trust relations of experts and then develop a consensus model taking into account the altruistic behaviors of experts. First, we cluster experts into subgroups by a constrained K-means clustering algorithm according to the opinion similarity and trust relationship of experts. Then, we calculate the weights of experts and clusters based on the centrality degrees of experts. Next, to enhance the quality of consensus reaching, we identify three kinds of non-cooperative behaviors and propose corresponding feedback mechanisms relying on the altruistic behaviors of experts. A numerical example is given to show the effectiveness and practicality of the proposed method in emergency decision-making. The study finds that integrating altruistic behavior analysis in group decision-making can safeguard the interests of experts and ensure the integrity of decision-making information.

Interval‐valued Pythagorean fuzzy linguistic KPCA model based on TOPSIS and its application for emergency group decision making

This paper investigates the emergency group decision-making problem based on interval-valued Pythagorean fuzzy language sets (IVPFLSs). The frequent occurrence of emergency events can bring huge economic damage to human beings. To reduce the loss, it is very important to make reasonable emergency decisions effectively and timely. In the emergency decision making (EDM), these problems are few studied, such as high dimension problem, data nonlinearity, and correlation. For EDM problems, the advantage of IVPFLSs is that it can reasonably express the evaluation information given by decision makers (DMs) through both qualitative and quantitative aspects. However, if the dimension and nonlinear relationship of the decision data keep growing, and the traditional decision-making methods will fail. The distance measure between decision data is necessary to calculate in the process of dimensionality reduction, and the current research does not propose the definition of IVPFLSs distance measure. On the basis of this, this paper first uses the attributes and DMs as variables to define the standard Euclidean distance measure between IVPFLSs. For nonlinear features, we construct the interval-valued Pythagorean fuzzy language kernel principal component analysis (IVPFL-KPCA) model to reduce the dimensionality. What is more, we also obtain the reasonable weight vectors of the attribute and DMs from cumulative contribution rate. For low-dimensional decision data, according to the technique for order performance by similarity to ideal solution method, the best emergency plan is selected for the information variables after dimensionality reduction. In sum, the IVPFL-KPCA model not only avoids multicollinearity and nonlinear separability between decision data, but also obtains reasonable weights. It further improves the efficiency of decision-making operations and reduces the difficulty of the algorithm. Finally, an example of earthquake emergency plan is shown to demonstrate the feasibility and practicability of the proposed method. Besides, we also compare it with the existing methods, which proves the effectiveness of the method.

Some Linear Diophantine Fuzzy Similarity Measures and Their Application in Decision Making Problem

The uncertainty in the data information for decision making is a most challenging and critical fear. In order to reduce the uncertainty in the decision making expert information for decision making problem, the Linear Diophantine fuzzy number is taking more critical part in reducing the uncertainty in information. Therefore the primary aim of this paper is to develop some different types of similarity and distance measures for linear Diophantine fuzzy numbers. With the frequent occurrence of emergency events, emergency decision making (EDM) plays a significant role in the emergency situations. It is essential for decision makers to make reliable and reasonable emergency decisions within a short time period since inappropriate decisions may result in enormous economic losses and chaotic social order. Accordingly, to ensure that EDM problems can be solved effectively and quickly, this paper proposes a new EDM method based on the novel distance and similarity measures under Linear Diophantine fuzzy (LDF) information. The similarity measure is one of the beneficial tools to determine the degree of similarity between objects. It has many crucial applications such as decision making, data mining, medical diagnosis, and pattern recognition. In this study, some novel distances and similarity measures of linear Diophantine fuzzy sets are presented. Then, the Jaccard similarity measure, exponential similarity measure, Cosine and Cotangent function based on similarity measures for LDFSs were proposed. The newly defined similarity measures are applied to medical diagnosis problem for COVID-19 virus and the results are discussed. A comparative study for the new similarity measures is established, and some advantages of the proposed work are discussed.

Emergency decision-making method based on multi-attribute intuitionistic fuzzy and evidence theory

Multi-attribute emergency decision-making problems have the characteristics of incomplete information and shortage of response time. Evidence theory can effectively express uncertain information in the decision-making process. However, evidence theory requires that the condition of evidence independence is met, and the evaluation information of experts is often vague. Therefore, an emergency decision-making method based on intuitionistic fuzzy sets and evidence theory is proposed. First, each expert gives an intuitionistic fuzzy evaluation of each emergency plan. Secondly, the proposed intuitionistic fuzzy similarity calculation method is used to obtain the similarity between experts and determine the expert weight. The attribute weight is known, the intuitionistic fuzzy evaluation is converted into a Mass function, and the evaluation of expert's decision-making plan is revised and fused using evidence theory to obtain the final decision. Finally, an example analysis proves that the model is feasible and effective.

A consensus model to manage the non-cooperative behaviors of individuals in uncertain group decision making problems during the COVID-19 outbreak

The COVID-19 pandemic has brought lots of losses to the global economy. Within the context of COVID-19 outbreak, many emergency decision-making problems with uncertain information arose and a number of individuals were involved to solve such complicated problems. For instance, the selection of the first entry point to China is important for oversea flights during the epidemic outbreak given that reducing imported virus from abroad becomes the top priority of China since China has achieved remarkable achievements regarding the epidemic control. In such a large-scale group decision making problem, the non-cooperative behaviors of experts are common due to the different backgrounds of the experts. The non-cooperative behaviors of experts have a negative impact on the efficiency of a decision-making process in terms of decision time and cost. Given that the non-cooperative behaviors of experts were rarely considered in existing large-scale group decision making methods, this study aims to propose a novel consensus model to manage the non-cooperative behaviors of experts in large-scale group decision making problems. A group consistency index simultaneously considering fuzzy preference values and cooperation degrees is introduced to detect the non-cooperative behaviors of experts. We combine the cooperation degrees and fuzzy preference similarities of experts when clustering experts. To reduce the negative influence of the experts with low degrees of cooperation on the quality of a decision-making process, we implement a dynamic weight punishment mechanism to non-cooperative experts so as to improve the consensus level of a group. An illustrative example about the selection of the first point of entry for the flights entering Beijing from Toronto during the COVID-19 outbreak is presented to show the validity of the proposed model.

Emergency decision support modeling for COVID-19 based on spherical fuzzy information.

Significant emergency measures should be taken until an emergency event occurs. It is understood that the emergency is characterized by limited time and information, harmfulness and uncertainty, and decision‐makers are always critically bound by uncertainty and risk. This paper introduces many novel approaches to addressing the emergency situation of COVID‐19 under spherical fuzzy environment. Fundamentally, the paper includes six main sections to achieve appropriate and accurate measures to address the situation of emergency decision‐making. As the spherical fuzzy set (FS) is a generalized framework of fuzzy structure to handle more uncertainty and ambiguity in decision‐making problems (DMPs). First, we discuss basic algebraic operational laws (AOLs) under spherical FS. In addition, elaborate on the deficiency of existing AOLs and present three cases to address the validity of the proposed novel AOLs under spherical fuzzy settings. Second, we present a list of Einstein aggregation operators (AgOp) based on the Einstein norm to aggregate uncertain information in DMPs. Thirdly, we are introducing two techniques to demonstrate the unknown weight of the criteria. Fourthly, we develop extended TOPSIS and Gray relational analysis approaches based on AgOp with unknown weight information of the criteria. In fifth, we design three algorithms to address the uncertainty and ambiguity information in emergency DMPs. Finally, the numerical case study of the novel carnivorous (COVID‐19) situation is provided as an application for emergency decision‐making based on the proposed three algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID‐19.

Hesitant Fuzzy Linguistic Consensus Model Based on Trust-Recommendation Mechanism for Hospital Expert Consultation

Aiming at validly improving the efficiency and quality of hospital expert consultation, a consensus model under uncertainty is established to operate a hospital decision support system (HDSS). Considering that expert consultation is commonly used in emergency events associated with life rescue where uncertainty and complexity exist, hesitant fuzzy linguistic terms are adopted. Additionally, since pairwise comparison information is efficient to express individual judgment, a novel concept of satisfaction degree toward hesitant fuzzy linguistic preference relation is utilized to measure the group consensus. A trust-recommendation mechanism is built to generate advice for experts. A consensus model is constructed to handle the HDSS in emergency decision-making problems. Some simulation experiments are designed to verify the availability and reasonability of the proposed model. Then, the proposed model is applied to address the selection of an emergency response plan for the influenza H7N9 virus, and finally, the strong robustness and powerful capability of the proposed model in the consensus reaching process is exhibited by making a comparative analysis among several recommendation mechanisms.

An extended prospect theory–VIKOR approach for emergency decision making with 2-dimension uncertain linguistic information

Emergency situations often require high-quality decisions which have to be made within a short period of time. An emergency event is often complex and can cause huge loss of lives and property if a wrong decision is made. Therefore, emergency decision making (EDM) has become a very high-profile research topic in recent years. In this paper, a new integrated approach based on prospect theory and VIKOR (VIsekriterijumska optimizacija i KOm-promisno Resenje), called linguistic PT-VIKOR, is proposed for solving the EDM problems with 2-dimension uncertain linguistic information. First, the preference information and the weights of criteria provided by experts for all statuses are expressed in the form of 2-dimension uncertain linguistic variables (2DULVs). Then, the individual experts’ opinions of each status are aggregated into a group decision matrix according to the aggregation operators of 2DULVs. Next, the group decision matrices of all statuses are transformed into a prospect decision matrix on the basis of the prospect theory, and the VIKOR technique is applied to rank the alternative solutions. Finally, the feasibility and practicability of the proposed linguistic PT-VIKOR approach is illustrated by an illustrate example and a comparative analysis.

A dynamic approach for emergency decision making based on prospect theory with interval-valued Pythagorean fuzzy linguistic variables

Emergency events can bring huge economic losses and casualties to human beings if not properly managed. To reduce all kinds of losses and prevent the escalation of disasters, it is of great importance to make reasonable decisions for emergencies in the short time. In the emergency decision making (EDM), however, decision information is often vague and uncertain and most of the current decision methods assume that decision makers are completely rational. Accordingly, this paper presents a dynamic approach based on interval-valued Pythagorean fuzzy linguistic variables (IVPFLVs) and prospect theory, called dynamic IVPFL-PT, for dealing with the EDM problems. First, the fuzzy assessments of decision makers are represented as IVPFLVs, and the weights of criteria are obtained by using a combination weighting method. Then, the prospect theory which can describe the psychological behaviors of decision makers is applied to rank emergency solutions. Furthermore, the proposed EDM approach can determine the best solution dynamically under different status. The new dynamic IVPFL-PT approach is implemented in a real example and compared with existing methods to demonstrate its feasibility and practicability.

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 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.

Managing Non-Homogeneous Information and Experts’ Psychological Behavior in Group Emergency Decision Making

After an emergency event (EE) happens, emergency decision making (EDM) is a common and effective way to deal with the emergency situation, which plays an important role in mitigating its level of harm. In the real world, it is a big challenge for an individual emergency manager (EM) to make a proper and comprehensive decision for coping with an EE. Consequently, many practical EDM problems drive group emergency decision making (GEDM) problems whose main limitations are related to the lack of flexibility in knowledge elicitation, disagreements in the group and the consideration of experts’ psychological behavior in the decision process. Hence, this paper proposes a novel GEDM approach that allows more flexibility for preference elicitation under uncertainty, provides a consensus process to avoid disagreements and considers experts’ psychological behavior by using the fuzzy TODIM method based on prospect theory. Eventually, a group decision support system (GDSS) is developed to support the whole GEDM process defined in the proposed method demonstrating its novelty, validity and feasibility.

A multi-stage conflict style large group emergency decision-making method

Unconventional emergencies usually have the characteristics of complexity, dynamic, and unpredictability, which greatly enhances the difficulty of emergency decision-making. Aiming at the multi-stage large group emergency decision-making problem featuring unknown stage weight and preference information expressed as interval numbers, we propose a new decision-making method. First, we present a similarity measurement formula for interval numbers. Each stage’s preference information is clustered using this similarity. To minimize the conflict of preferences, we derived two relative entropy optimization models to calculate the aggregation and stage weights. Next, we rank the alternatives based on the comprehensive group preference information. Finally, we present an illustrative example to verify the validity and practicability of this approach, and discuss several advantages of this method for managing emergency decision-making problems.

Consensus model for multi-criteria large-group emergency decision making considering non-cooperative behaviors and minority opinions

Emergency situations often demand high-quality decision-making results because a wrong decision may cause incalculable losses. Therefore, in such cases, the views of all decision makers should be fully considered, especially minority opinions, and the interests of all parties should be well balanced. Additionally, an emergency decision often has to be made within a short period of time, which may require the rational treatment of non-cooperative behaviors. Classical consensus models mainly focus on group decision-making problems where a small number of decision makers participate. However, an emergency decision often involves many stakeholders, and thus requires the participation of larger number of decision makers. In this paper, an improved consensus model is proposed for large-group emergency decision making, and an approach to managing minority opinions and non-cooperative behaviors is described. By simulating the consensus reaching process, a method is presented to determine two important parameters: the minimum group consensus threshold and the maximum number of iterations. Finally, an illustrative example proves that the proposed consensus model is feasible and effective, and a detailed discussion highlights the advantages of this model for managing large-group emergency decision-making problems.

A prospect theory-based interval dynamic reference point method for emergency decision making

Urgent or critical situations, such as terrorist attacks and natural disasters, often require decision makers (DMs) to take crucial decisions. Emergency decision making (EDM) problems have become a very active research field in recent years. The existing studies focus mainly on the information inadequacy or incomplete information in emergencies, and selecting ideal emergency alternatives, neglecting the psychological behavior of DMs under emergencies. Few studies consider a DM's psychological behavior, although there is some focus on the dynamic features of emergency events and limited DM judgments under risk and uncertainty. Motivated by such problems, this study proposes a prospect theory-based interval dynamic reference point method for EDM. The technique for order preference by similarity to ideal solution (TOPSIS) method is a popular decision technique, as decision makers are always bounded rational under risk and uncertainty and their psychological behavior plays an important role in EDM. However, the existing TOPSIS methods are seldom concerned with this issue. Based on such a problem, this study proposes a TOPSIS method with an interval reference point, which considers the DM's psychological behavior. Two examples are presented to illustrate the feasibility and validity of the proposed methods for solving EDM problems in the real world. Based on the final rankings of alternatives in the examples, each of our methods validates the other and matches the actual EDM.

A Variation Coefficient Similarity Measure and Its Application in Emergency Group Decision-making

Consider the emergency decision-making problems, we proposed a new similarity measures based on the extension of the Dice, and cosine similarity measures, which satisfy some properties in Ref. [11]. Then, we apply the variation coefficient similarity to the emergency group decision-making problems. Through the weighted similarity measures between each alternative and the ideal alternative, the ranking order of all the alternatives can be determined and the best one(s) can be easily identified as well. Finally, we give a practical example to evaluate the emergency management capability of major snow disaster in Hunan province of China. For comparison, the decision results of different similarity measures are also given, which show that our method have better similarity identification and the proposed method is applicable.

A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support

In this paper, a distance-based group decision-making (GDM) methodology is proposed to solve unconventional multi-person multi-criteria emergency decision-making problems. In this model, some decision-makers are first identified to formulate a group decision-making framework. Then a standard multi-criteria decision-making (MCDM) process is performed on specific decision-making problems and different decision results are obtained from different decision-makers. Finally, these different decision results are aggregated into a group consensus to support the final decision-making. For illustration and verification purposes, a numerical example and a practical unconventional emergency decision case are presented. Experimental results obtained demonstrate that the proposed distance-based multi-criteria GDM methodology can improve decision-making objectivity and emergency management effectiveness.

Computerized support systems for emergency decision making

In all fields of human society, occasional emergencies are almost inevitable. Once an emergency occurs, rapid and proper decision making is required. The purpose of this paper is to explore the design and development of computerized support systems for emergency decision making (EDM). First the characteristics of EDM problems are examined. Then, in view of limited human computer rationality, requirements for a computerized support system for EDM are determined. A conceptual structure for knowledge-based distributed emergency decision support systems is proposed. Finally, a prototype system for safety protection and disaster response in coal mines, developed using the proposed structure, is briefly described.