The work deals with the derivation of procedures for postoptimization analysis in multicriteria linear programming tasks for selected methods with a priori information. The selected methods are aggregation of objective functions, lexikographical compromise method, minimization of the distance from the ideal according to linear and Chebyshev metrics, compromise solutions according to minimum components and goal programming. Based on procedures for single-criteria linear programming and a specific... show full abstractThe work deals with the derivation of procedures for postoptimization analysis in multicriteria linear programming tasks for selected methods with a priori information. The selected methods are aggregation of objective functions, lexikographical compromise method, minimization of the distance from the ideal according to linear and Chebyshev metrics, compromise solutions according to minimum components and goal programming. Based on procedures for single-criteria linear programming and a specific method, procedures for sensitivity analysis of each mentioned method are derived in the theoretical part of the thesis. Furthermore, the work includes a derivation for postoptimization changes, especially the change in model dimensions, for one selected method of multicriteria linear programming, which is the principle of aggregation of objective functions. In the practical part of the thesis, these procedures are shown and tested on an example. |