Abstract
We deal with the linear programming problem in which input data can vary in some given real compact intervals. The aim is to compute the exact range of the optimal value function. We present a general approach to the situation the feasible set is described by an arbitrary linear interval system. Moreover, certain dependencies between the constraint matrix coefficients can be involved. As long as we are able to characterize the primal and dual solution set (the set of all possible primal and dual feasible solutions, respectively), the bounds of the objective function result from two nonlinear programming problems. We demonstrate our approach on various cases of the interval linear programming problem (with and without dependencies).
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Alefeld G., Herzberger J. (1983) Introduction to interval computations. Academic Press, London
Alefeld G., Kreinovich V., Mayer G. (1998) The shape of the solution set for systems of interval linear equations with dependent coefficients. Mathematische Nachrichten 192: 23–36
Alefeld, G., Kreinovich, V., & Mayer, G. (2003a). On symmetric solution sets. In J. Herzberger(ed.), Inclusion methods for nonlinear problems. With applications in engineering, economics and physics. Proceedings of the international GAMM-workshop, Munich and Oberschleissheim, December 15–18, 2000. Wien, Springer, Comput. Suppl., 16, 1–22.
Alefeld G., Kreinovich V., Mayer G. (2003b) On the solution sets of particular classes of linear interval systems. Journal of Computational and Applied Mathematics 152(1–2): 1–15
Chanas S., Kuchta D. (1996) Multiobjective programming in optimization of interval objective functions – a generalized approach. European Journal of Operational Research 94(3): 594–598
Chinneck J.W., Ramadan K. (2000) Linear programming with interval coefficients. Journal of the Operational Research Society 51(2): 209–220
Fiedler M., Nedoma J., Ramik J., Rohn J., Zimmermann K. (2006) Linear optimization problems with inexact data. Springer–Verlag, New York
Gerlach W. (1981) Zur Lösung linearer Ungleichungssysteme bei Störung der rechten Seite und der Koeffizientenmatrix. Mathematische Operationsforschung und Statistik, Series Optimization 12: 41–43
Hladík M. (2007) Solution set characterization of linear interval systems with a specific dependence structure. Reliable Computing 13(4): 361–374
Inuiguchi M., Ramik J., Tanino T., Vlach M. (2003) Satisficing solutions and duality in interval and fuzzy linear programming. Fuzzy Sets and Systems 135(1): 151–177
Jansson C., Rump S.M. (1991) Rigorous solution of linear programming problems with uncertain data. Zeitschrift für Operations Research 35(2): 87–111
Kolev L.V. (2004) Solving linear systems whose elements are nonlinear functions of interval parameters. Numerical Algorithms 37: 199–212
Machost, B. (1970). Numerische Behandlung des Simplexverfahrens mit intervallanalytischen Methoden, Berichte der Gesellschaft für Mathematik und Datenverarbeitung, Nr. 30, Bonn.
Mráz F. (1998) Calculating the exact bounds of optimal values in LP with interval coefficients. Annals of Operations Research 81: 51–62
Oettli W., Prager W. (1964) Compatibility of approximate solution of linear equations with given error bounds for coefficients and right-hand sides. Numerische Mathematik 6: 405–409
Popova E. (2001) On the solution of parameterised linear systems. In: Kraemer W., Gudenberg J. (eds) Scientific computing, validated numerics, interval methods. Kluwer Academic, Boston, pp 127–138
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hladík, M. Optimal value range in interval linear programming. Fuzzy Optim Decis Making 8, 283–294 (2009). https://doi.org/10.1007/s10700-009-9060-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10700-009-9060-7