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.

No abstract

PACS 68.55.Jk, 68.55.Ln, 73.61.Ey, 81.15.Gh, 85.30

Al x Ga 1−x N/GaN heterostructure field-effect transistors with different Al concentrations (0.15<x<0.25) and barrier widths (150 Å<WB<350 Å) have been fabricated and characterized. Experimental results were analyzed by using a self-consistent solution of the Schrödinger and Poisson equations with the proper boundary conditions. The total (piezoelectric and spontaneous) polarization has been included as a fitting parameter in the self-consistent calculations. From the analysis of the transistor charge-control experimental data, a linear increase of the polarization field with the Al concentration has been found. Our results indicate that the slope of such dependence, and the magnitude of the total polarization field are lower than the predicted ones using the usually accepted values of the piezoelectric and spontaneous polarization coefficients.

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.