An interactive branch-and-bound algorithm for multiple criteria optimization

Article Abstract:

A branch-and-bound interactive algorithm for multiple criteria optimization is developed. This algorithm differs from previous interactive algorithms in that it can be used with decision problems in which the feasible set is convex or discrete. This new procedure is also computationally simpler than previous models. The decision-maker using the branch-and-bound interactive algorithm for multicriteria optimization is only required to indicate a preference between two vectors.

Branch and bound algorithms

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Inferring preferences in multiple criteria decision analysis using a logistic regression model

Article Abstract:

A technique is given on the analysis of various criteria decision making problems for an interactive environment. Decision-making preferences are not consistent with the basic utility model or are self-inconsistent. The maximum probability estimation technique is used according to the logistic regression model, and it shows that the likelihood of taking one decision over another is a linear function of attribute values.

Stochastic analysis, Management research

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Calibration and the expert problem

Article Abstract:

The decision maker must provide his subjective probability to one event of concern, A. The expert can be expected to furnish advice, since the decision maker has little significant data on the factors that affect A. The expert provides a subjective probability that is based on A.

Calibration

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