A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support
Introduction
Unconventional emergency events, such as earthquakes and hurricanes, often lead to unexpected catastrophic consequences [5]. When such devastating incidents occur, emergency planning and management play a crucial role in reduction and mitigation of their effects. In the emergency planning and management, there are a great many emergency decision-making problems that need to be solved, to handle effects of the destructive events. Usually, an emergency decision has the following two distinct features. First, an emergency decision must often be made in a short period of time using partial or incomplete information, especially in the early stages of the disaster occurrence. Accordingly emergence group decision-making (GDM) is an intractable task, particularly when handling some unconventional high impact emergency events. Second, these decisions may have potentially serious outcomes. In many situations, a wrong decision could result in deadly consequences [13]. In view of the unique characteristics of emergency decisions, using group decision support systems (GDSS) [6], [8], [9] to handle emergency decision problems could be extremely valuable.
Some previous studies [13], [15], [27] also revealed that the GDSS has great potential applications in modern emergency planning and management. For example, Levy and Taji [13] proposed a group analytic network process (GANP) to construct a GDSS to support hazard planning and emergency management under incomplete information. In their study, a typical unconventional emergency event, a chemical spill in the city of Brandon, Manitoba is simulated. With application in evacuation and shelter-in-place decisions, it is shown that the proposed GANP model improves emergency management effectiveness, decision transparency, and user satisfaction [13]. Zografos et al. [27] presented a methodological framework for developing a hazardous material emergency response (HAMER) decision support system (DSS) to manage emergency response operations for large-scale industrial accidents in Western Attica, Greece. Similarly, Mendonca et al. [15] designed a gaming simulation to assess GDSS for emergency response in emergency management.
Although these existing studies have shown that GDSS can improve emergency management effectiveness and decision transparency due to the fact that it can integrate group wisdom of multiple decision-makers into one group wisdom, there are two key issues that are apparently not solved well by GDSS. On the one hand, in the process of multi-criteria decision-making (MCDM), determining a set of suitable weights for multiple evaluation criteria is often considered to be a very difficult task. In the existing literature, many researchers usually set some arbitrary weights for each criterion to solve specified decision-making problems in terms of subjective judgments of decision-makers. But such a processing method will add the subjectivity and thus reducing the decision accuracy, sometimes leading to wrong decision results. On the other hand, in the process of using GDM, evolving an effective group consensus out of different judgments from different decision-makers, is still an unsolved issue in the previous studies.
Inspired by the GDSS, this study attempts to propose a distance-based multi-criteria group decision-making (GDM) methodology to support multi-person emergency decision problems. As is known, GDM is one of the most active research fields within MCDM [3]. In GDM, group members (i.e., decision-makers) first make their own judgments on the same decision problem independently, i.e. decision actions and alternatives, based on multiple evaluation criteria. These judgments from different decision-makers are then aggregated into a group consensus to support the final decision. Different from previous studies, this study tries to give an effective solution to the two unresolved issues, and to construct a distance-based multi-criteria GDM methodology for multi-person emergency decision support.
Generally, the proposed distance-based multi-criteria GDM methodology is comprised of three stages. In the first stage, some decision-makers (DMs) are first identified to formulate a GDM framework. Then a standard MCDM process is performed on the specific decision-making problems, and accordingly different decision results are obtained from different decision-makers in the second stage. In the third stage, these different decision results are aggregated into a group consensus to support the final decision. The main purpose of this study is to propose a new distance-based multi-criteria GDM model to support unconventional emergency decision-making problems. Using the proposed distance-based GDM model, many practical emergency decision-making problems can be solved effectively. For these real-world problems, decisions are made on the basis of a set of pre-defined criteria. Therefore, the proposed distance-based multi-criteria GDM methodology is suitable for solving these multi-person emergency decision-making problems.
The main contribution of this study is that a new distance-based multi-criteria GDM methodology is proposed to support unconventional emergency decisions, by providing a rational solution to the two unresolved key issues. Compared with traditional GDM methods, our proposed distance-based multi-criteria GDM model has three distinct characteristics. First, the decision-makers' judgments/evaluations are made on the basis of a set of criteria to formulate a multi-person multi-criteria GDM framework. This makes the decision results more objective than traditional single-person MCDM methods [10], [11], [14], [16], [17]. Second, the weights of evaluation criteria are determined based upon the data itself, thus reducing decision bias and adding the objectiveness to the proposed GDM methodology. Finally, different from previous subjective methods and traditional time-consuming iterative procedures, this paper proposes a fast optimization technique to integrate the different decision opinions, and to make the aggregation of different decision opinions simple.
The main purpose of the proposed multi-criteria GDM methodology is to improve decision accuracy, and to enhance decision transparency and thus to increase decision effectiveness. The rest of this paper is organized as follows. In Section 2, the proposed distance-based multi-criteria GDM methodology is described in detail. For illustration and verification purposes, Section 3 presents a numerical example and a practical emergency decision case to illustrate the implementation process, and to verify the effectiveness of the proposed distance-based multi-criteria GDM methodology. Finally, some concluding remarks are drawn in Section 4.
Section snippets
Formulation of distance-based multi-criteria GDM methodology
In this section, a general framework for multi-criteria GDM methodology is first presented. Then some main procedures or steps involved in the proposed distance-based multi-criteria GDM methodology are described in detail. Finally a summary for distance-based multi-criteria GDM methodology is given.
Experimental analysis
In this section, an illustrative numerical example is first presented to explain the implementation process of the proposed distance-based multi-person multi-criteria GDM methodology. Then one real-world emergency decision problem for a chemical spill emergency management is simulated, using the proposed distance-based multi-criteria GDM methodology. Accordingly, some interesting results are produced by comparison of these results with some existing methods.
Concluding remarks
In this paper, a distance-based multi-criteria GDM methodology is proposed for multi-person emergency decision support. In terms of experimental results, it is easy to find that across different models and three different evaluation criteria, for the test cases of numerical and practical examples, the proposed distance-based multi-criteria GDM methodology can effectively solve the multi-person multi-criteria decision-making (MCDM) problems. In the presented practical cases, decision results of
Acknowledgements
Authors would like to thank the guest editors and anonymous referees for their valuable comments and suggestions. Their comments helped improve the quality of the paper immensely. This work is partially supported by grants from the National Science Fund for Distinguished Young Scholars (NSFC No. 71025005), National Natural Science Foundation of China (NSFC No. 90924024) and Knowledge Innovation Program of the Chinese Academy of Sciences (CAS).
Lean Yu is Associate Professor in the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS). His research interests include artificial intelligence, computer simulation, decision support systems, knowledge management, and financial forecasting.
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Lean Yu is Associate Professor in the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS). His research interests include artificial intelligence, computer simulation, decision support systems, knowledge management, and financial forecasting.
Kin Keung Lai is Chair Professor of Management Science at City University of Hong Kong, Hong Kong. His main research interests include logistics and operations management, computer simulation, artificial intelligence, and business decision modeling.