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According to the management by objectives (MBO) theory, performance evaluations should take into account the achievement of management objectives (MOs). The most commonly used performance evaluation method — the cross-efficiency evaluation method — seldom considers the role of MOs as a reference point. According to the prospect theory, decision-makers underestimate the benefits that exceed the reference point; they also exaggerate the losses that fall below the reference point. This irrational psychology is more obvious when evaluating the performance evaluation of peers. As such, this paper proposes a cross-efficiency evaluation method based on prospect theory, which takes MOs as a reference point from a peer perspective. First, taking MOs as reference points, a decision-making unit (DMU) chooses a set of weights for each peer, in order to maximize or minimize the prospects of the peer, according to the benevolent or aggressive attitude of the DMU. In order to improve the adaptability of the method, the precise number of MOs is further extended to be an interval number. Finally, the relationship between models, which are based on precise MOs and interval MOs, is illustrated by propositions. Finally, numerical examples are provided to illustrate the applications of the proposed cross-efficiency evaluation method. [ABSTRACT FROM AUTHOR]

In decision-making analysis, the description on decision makers' risk attitude under uncertainty is a focus topic. This paper presents a novel decision analysis framework that considers uncertainty and risk using the cumulative prospect theory. The proposed approach describes uncertain preference information using interval-valued fuzzy sets, maintaining this representation without converting intervals into crisp numbers throughout the entire process. To quantify the distance between interval-valued fuzzy sets, we introduce a comprehensive interval-valued distance model that accounts for multiple situations determined based on the relative positions between intervals. Afterwards, some related theorems about the proposed interval-valued distance model are explored mathematically. Then, with the aid of different reference points and interval-valued distance model, the overall interval-valued prospect value of each alternative is investigated. Finally, an Enhanced Minimax Regret-based (EMR) approach is developed to compare and rank the obtained overall interval-valued prospect values. An illustrative example, accompanied by detailed discussions, is provided to demonstrate the flexibility and superiority of the proposed decision analysis framework. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the wake of a large-scale disaster, volunteer teams provide invaluable assistance. There is a pressing need to design a suitable model to solve the problem of volunteer team dispatching. This paper proposes a novel approach from the perspective of two-sided matching, which could stimulate the enthusiasm of volunteers. However, it is almost impossible to use one or more fixed indicators to evaluate each volunteer team or rescue task. Even if the indicators can be determined, the values of some indicators are difficult to obtain. Therefore, the proposed approach uses the available information to recommend potential matching targets and revises some key targets based on the subjective preferences of participants. It effectively avoids the bias in decision-making caused by the unavailability of sufficient information and improves the satisfaction of bilateral participants. Furthermore, it can effectively solve the two-sided matching problem when the preference order is unknown. The proposed approach is illustrated with a simulation case that focuses on an explosion accident in China. The rationality and effectiveness of the approach is validated through comparisons and analyses of the experimental results. • Optimal volunteer team assignment is performed based on two-sided matching. • Potential matching targets are recommended based on prospect theory. • Potential matching targets are revised to address information unavailability. • The matching model is built under volunteer team and rescue task uncertainty. [ABSTRACT FROM AUTHOR]