مدل اندازه گیری پیچیدگی برای سیستم پشتیبانی تصمیم گیری اضطراری
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|5756||2012||6 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Systems Engineering Procedia, Volume 5, 2012, Pages 289–294
Emergency decision-making is the core of emergency management, and directly determines success or failure of emergency disposal activities. This paper introduced the information entropy in the complexity theory. With the combination of qualitative and quantitative methods, the paper proposed a model of evaluation and decision-making for complexity measurement based on information entropy. The model can effectively choose the best decisions in decision-making programs that have been developed and evaluation system. And an example shows its value.
Emergency is a typical complex sy stem because of its diversity, random ness, sudden, disorderly and other features. The complexity of emergency refers to the states which are difficult to understand, describe, predict and control, but from the perspective of information theory, it is the state of the system that is expected to take quantity of information. The greater of the degree of complex ity, indicates the more uncertainty and unpredictability of the states of incident, and need obtain more information to understand it [1-3]. The co mplexity characteristics of emergency determine the complexity of decision-makin g. And the level of understanding the emergency and the program of decision-making will affect its handling and cont rol effects. Generally, there are two ways that respond to decision-making complexity of emergency. One the hand, it is as possible as to minimize or eliminate complexity from the point of management. The strategy is to simplify emergencies in order to improve their manageability, such as the status of the incident process, reducing the amount of resources. On the other hand, trying to understand and measure the complexity from the persp ective of theoretical methods. That is to say, accord ing to qualitative and quantitative description and analysis of system complexity, t he purpose of effective choice system behaviour will be to achieve. Measurement is the basis for management, and the measure of system complexity is the basis for the studying quantitatively complexity. Based on complexity measure, the specific analysis of the cause for system complexity can quantitatively evaluate different disposal options in order to optimize emergency decision-making [4-6]. The key of emergency decision-making is that how to develop and optimize decision-making program. And the key of program optimization is choice of rational target weight with a certain amount of s ubjective and arbitrary . How to better determine the target weight and eliminate subjectivity and optimize decision-making from a number of alternatives is main research aim of this paper. W ith of complexity measurement based on method of complexity theory, the paper proposed a model of evaluation an d decision-making for complexity measurement based on information entropy, which can be used to describe quantita tively the complexity of target of emergency decision- making and evaluation.
نتیجه گیری انگلیسی
The choice of emergency decision-making program direc tly relates to minimize disaster losses and social impact during the disposal process. Because the traditional optim ization emergency programs re ly solely on the decision- makers and experts to obtain based on data of evalua tion indexes, it is too subjective to response objective complexity of emergency. Using Information entropy method to give weight of each level in the evaluation index system, and calculate the complexity of primary and secondary indexes, and draw comprehensive weight of evaluation index through the revise of the index weight which is evaluated by experts, it is effective to avoid weighting subjectively in the system of emergency decision-making evalua tion. Experimental analysis showed that this measurement method of complexity can choose optimal decision-making programs and point out the lack of the others.