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DEFINITIONS OF SUBOPTIMISTIC AND SUBPESSIMISTIC SOLUTIONS AND THEIR CONSTRUCTION IN THE INTERVAL BOOLEAN PROGRAMMING PROBLEM

Науковий журнал «Радіоелектроніка, інформатика, управління»

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##plugins.schemas.marc.fields.042.name## dc
 
##plugins.schemas.marc.fields.245.name## DEFINITIONS OF SUBOPTIMISTIC AND SUBPESSIMISTIC SOLUTIONS AND THEIR CONSTRUCTION IN THE INTERVAL BOOLEAN PROGRAMMING PROBLEM
 
##plugins.schemas.marc.fields.720.name## Mamedov, K. Sh.; Azerbaijan National Academy of Sciences, Baku, Azerbaijan
Mamedova, A. H.; Azerbaijan National Academy of Sciences, Baku, Azerbaijan
 
##plugins.schemas.marc.fields.653.name## Boolean programming problem, integer interval data, optimistic solution, pessimistic solution, suboptimistic solution, subpessimistic solution, non-linear penalty function, Lagrange function, computational experiments.
 
##plugins.schemas.marc.fields.520.name## The work considers an interval examples of Bulian programming.Given some economic interpretation to this problem which result in the<br />constructed economic-mathematical model.Introduced the definitions of optimistic, pessimistic, and suboptimistic, subpessimistic solutions<br />for the Boolean programming problem with integer interval data are introduced. On the basis of the economic interpretation of the problem<br />two algorithms are developed for the constructing of the suboptimistic and subpessimistic solutions of this task. Of course, when solutions may<br />different from suboptimistic and subpessimistic solutions.It is therefore necessary to estimate the relative error of the found to estimate the<br />error of the suboptimistic and subpessimistic solutions from optimistic and pessimistic solutions appropriately. For this purpose Lagrange type<br />majoring function is constructed. It is proved, that the minimum value of this function is the upper bound of the optimistic and pessimistic<br />values of the objective function appropriately. Minimization of this function is in the upper border of the suboptimistic and subpessimistic<br />values of the performance function. Numerous computational experiments on the examples with different dimensions are provided.
 
##plugins.schemas.marc.fields.260.name## Zaporizhzhya National Technical University
2016-10-27 12:28:19
 
##plugins.schemas.marc.fields.856.name## application/pdf
http://ric.zntu.edu.ua/article/view/81370
 
##plugins.schemas.marc.fields.786.name## Radio Electronics, Computer Science, Control; No 3 (2016): Radio Electronics, Computer Science, Control
 
##plugins.schemas.marc.fields.546.name## ru
 
##plugins.schemas.marc.fields.540.name## Copyright (c) 2016 K. Sh. Mamedov, A. H. Mamedova