Random problem generation and the computation of efficient extreme points in multiple objective linear programming

Random problem generation and the computation of efficient extreme points in multiple objective linear programming

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Article ID: iaor19951892
Country: United States
Volume: 3
Issue: 4
Start Page Number: 333
End Page Number: 347
Publication Date: Oct 1994
Journal: Computational Optimization and Applications
Authors:
Abstract:

This paper looks at the task of computing efficient extreme points in multiple objective linear programing. Vector maximization software is reviewed and the ADBASE solver for computing all efficient extreme points of a multiple objective linear program is described. To create MOLP test problems, models for random problem generation are discussed. In the computational part of the paper, the numbers of efficient extreme points possessed by MOLPs (including multiple objective transportation problems) of different sizes are reported. In addition, the way the utility values of the efficient extreme points might be distributed over the efficient set for different types of utility functions is investigated. Not surprisingly, results show that it should be easier to find good near-optimal solutions with linear utility functions than with, for instance, Tchebycheff types of utility functions.

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