THE CONCEPT OF GUARANTEED SOLUTION THROUGH THE FUNCTIONAL FOR MUTIDIMENTIONAL KNAPSACK PROBLEM AND METHODS OF ITS CONSTRUCTION
Keywords:one-dimensional and multidimensional knapsack problems, guaranteed solution and guaranteed suboptimal solution through the functional, non-linear multicriteria the problem of Boolean programming, dichotomy approach, computational experiment.
AbstractContex. The problem of constructing a guaranteed suboptimal (approximate) solution with respect to a functional in one-dimensional and multidimensional knapsack problems is considered. The object of the study was a model with an increment of the coefficients of the objective function.
Objective. The methods of constructing guaranteed suboptimal solution through the functional in one-dimensional and multidimensional
knapsack problem has been developed.
That is it is necessary to find such minimal changes coefficient of the objective function in the set of integer intervals so that the solution
found guarantees the value of the functional not less than the predetermined value.
Method. The concept of guaranteed solution and guaranteed suboptimal solution relative to the objective function in the satchel problem
is introduced. It is necessary to find such minimal changes coefficient of the objective function in the set of integer intervals so that the
solution found guarantees the value of the functional not less than the predetermined value. Such kind of solution we name as guaranteed
solution through the functional for one-dimensional and multidimensional knapsack problem. The methods of their construction has been developed. A software package was developed to find these solutions and numerous computational experiments were performed on random large-dimensional problems.
Results. The algorithm of constructing guaranteed suboptimal solution through the functional in one-dimensional and multidimensional
knapsack problem has been developed.
Conclusions. A software package was developed to find the concept of guaranteed solution and guaranteed suboptimal solutions and
numerous computational experiments were performed on random large-dimensional problems.
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