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Preference structure representation using convex cones in multicriteria integer programming. (English) Zbl 0683.90089
Summary: A new efficient system of representing the decision-maker’s preference structure in solving multicriteria integer programming problems is developed. The problem is solved by an interactive branch-and-bound method that employs the procedure of S. Zionts and J. Wallenius [ibid. 29, 519-529 (1983; Zbl 0519.90083)] for multicriteria linear programming. The decision-maker’s underlying utility function is assumed to be pseudoconcave, and his pairwise comparisons of decision alternatives are used to determine his preference structure in terms of certain convex cones in the objective function space and constraints on the weights on the objectives in the weight space. The two forms of preference structure representation are interrelated, and their underlying theory is developed. The primary objective of a representation scheme is exactness, and, in this respect, it is shown that the constraints on the weights are not adequate for representing nonlinear utility functions. On the other hand, the convex cones exactly represent any quasiconcave utility function and clearly avoid the approximations and inaccuracies in other utility assessment systems. Accordingly, an efficient ordered representation using convex cones is developed. An algorithmic framework for multicriteria integer programming that integrates the representation using convex cones with the branch-and- bound solution procedure is developed. Computational experience with bicriteria problems having up to 80 variables and 40 constraints is presented.

MSC:
90C31 Sensitivity, stability, parametric optimization
90C10 Integer programming
91B16 Utility theory
91B08 Individual preferences
90B50 Management decision making, including multiple objectives
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