Title
Interval linear programming under transformations: optimal solutions and optimal value range
Abstract
Interval linear programming provides a tool for solving real-world optimization problems under interval-valued uncertainty. Instead of approximating or estimating crisp input data, the coefficients of an interval program may perturb independently within the given lower and upper bounds. However, contrarily to classical linear programming, an interval program cannot always be converted into a desired form without affecting its properties, due to the so-called dependency problem. In this paper, we discuss the common transformations used in linear programming, such as imposing non-negativity on free variables or splitting equations into inequalities, and their effects on interval programs. Specifically, we examine changes in the set of all optimal solutions, optimal values and the optimal value range. Since some of the considered properties do not holds in the general case, we also study a special class of interval programs, in which uncertainty only affects the objective function and the right-hand-side vector. For this class, we obtain stronger results.
Year
DOI
Venue
2019
10.1007/s10100-018-0580-5
Central European Journal of Operations Research
Keywords
Field
DocType
Interval linear programming, Optimal set, Optimal value range, Transformations
Mathematical optimization,Interval linear programming,Free variables and bound variables,Linear programming,Optimization problem,Mathematics
Journal
Volume
Issue
ISSN
27
3
1613-9178
Citations 
PageRank 
References 
3
0.43
7
Authors
3
Name
Order
Citations
PageRank
Elif Garajová131.44
Milan Hladík226836.33
Miroslav Rada330.43