Design of a Decision Support System for use in the Event of a Nuclear Emergency

French, Simon; Papamichail, K N; Ranyard, David C.; Smith, Jim Q.

doi:10.1007/978-94-017-0759-6_1

Cited by 8 publications

(7 citation statements)

References 8 publications

(8 reference statements)

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“…Finally, the fourth procedure applies when the DM decides to use a subjective scale without defining a utility function, because it is difficult to ascertain what the value of each alternative is for the respective subjective attribute. This procedure is implemented in the system by means of a thermometer scale [40]. The DM will enter utility intervals by hand using scrollbars, as shown in Fig.…”

Section: Assessment Of Component Utility Functionsmentioning

confidence: 99%

A generic multi-attribute analysis system

Jiménez

Ríos-Insúa

Mateos

2006

Computers & Operations Research

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This paper describes a generic decision support system based on an additive multiattribute utility model that is intended to allay many of the operational difficulties involved in the multicriteria decision-making process. The system accounts for uncertainty about the alternative consequences and admits incomplete information about the decision-makers' preferences, which leads to classes of utility functions and weight intervals. The additive model is used to assess, on the one hand, average overall utilities, on which the ranking of alternatives is based and, on the other, minimum and maximum overall utilities, which give further insight into the robustness of this ranking. When the information obtained is not meaningful enough so as to definitively recommend an alternative, an iteration process can be carried out by tightening the imprecise parameters and assessing the non-dominated and potentially optimal alternatives or using Monte Carlo simulation techniques to determine useful information about dominance among the alternatives.

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Section: Assessment Of Component Utility Functionsmentioning

confidence: 99%

A generic multi-attribute analysis system

Jiménez

Ríos-Insúa

Mateos

2006

Computers & Operations Research

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“…Finally, the fourth procedure applies when the DM decides to use a subjective scale without defining a utility function and is implemented in the system by means of a thermometer scale, French et al (1997). This procedure is suitable for determining the impact of each strategy on a given subjective attribute rather than a utility function, because it is difficult to ascertain what the value of each strategy is for the respective subjective attribute.…”

Section: Quantification Of Dms' Preferencesmentioning

confidence: 99%

Monte Carlo Simulation Techniques in a Decision Support System for Group Decision Making

2005

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This paper describes a group decision support system based on an additive multi-attribute utility model for identifying a consensus strategy in group decision-making problems where several decision-makers or groups of decision-makers elicit their own preferences separately. On the one hand, the system provides procedures to quantify the DM's or group of DMs' preferences separately. This involves assessing the DM's or group of DMs' component utilities that represent their preferences regarding the respective possible attribute values and objective weights that represent the relative importance of the criteria. On the other hand, we propose Monte Carlo simulation techniques for identifying a consensus strategy. An iterative process will be carried out, where, after the simulations have been performed, the imprecise component utilities and weights corresponding to the different DMs or groups of DMs are tightened to output more meaningful information in the next simulations to achieve a consensus strategy. Finally, an application to the evaluation of remedial strategies for restoring contaminated aquatic ecosystems illustrates the usefulness and flexibility of this decision support tool.

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“…The third procedure applies when the DM decides to use a subjective scale without defining a utility function, implemented in the system by means of a thermometer scale [8], suitable for determining the impact of each strategy on a given subjective attribute rather than a utility function, because it is difficult to ascertain what the value of each strategy is for the respective subjective attribute. For subjective scales, the DM will enter utility intervals by hand using scrollbars, as shown in Fig.…”

Section: Assessment Of Component Utility Functionsmentioning

confidence: 99%

A decision support system for multiattribute utility evaluation based on imprecise assignments

Jiménez

Ríos-Insúa

Mateos

2003

Decision Support Systems

100

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This paper describes a decision support system based on an additive or multiplicative multiattribute utility model for identifying the optimal strategy. This is intended to allay many of the operational difficulties involved in assessing and using multiattribute utility functions. The system admits imprecise assignments for weights and utilities and uncertainty in the multiattribute strategies, which can be defined in terms of ranges for each attribute instead of single values. Inputs can be subjected to different sensitivity analyses, permitting users to test the robustness of the ranking of the strategies and gain insight into and confidence about the final solution. An application of the system to the restoration of a contaminated lake is illustrated throughout the paper.

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Design of a Decision Support System for use in the Event of a Nuclear Emergency

Cited by 8 publications

References 8 publications

A generic multi-attribute analysis system

A generic multi-attribute analysis system

Monte Carlo Simulation Techniques in a Decision Support System for Group Decision Making

A decision support system for multiattribute utility evaluation based on imprecise assignments

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