The paper concerns the application of advanced evolutionary algorithms for solving a complex scheduling problem of manufacturing tasks. The case of scheduling of independent, non-preemptive tasks on unrelated moving executors, to minimize the makespan is considered. New heuristic solution algorithms based on an evolutionary computation are proposed. They use three different ways of multiple crossovers. The computer simulation experiments have been performed to investigate the dependence of results of the tasks scheduling on parameters of evolutionary algorithms as well as the quality of the solution algorithms in terms of the makespan and the time of computation. The evolutionary algorithms have been compared with a traditional solution algorithm that has been determined using the functional decomposition of the problem. A numerical example together with conclusions completes the paper.
A new method for computation of minimal compartmental realizations of a proper transfer function is proposed. The method is based on a suitable transformation of an asymptotically stable positive realization to the desired compartmental realization. The transformation is characterized by a generalized permutation matrix. Sufficient conditions for the existence of minimal compartmental realizations are established. Two-step procedures for computation of minimal compartmental realizations for both continuous-time and discrete-time linear systems are derived and illustrated by a numerical example.
The paper concerns task and resource allocation in a complex of operations which may be considered as a part of the knowledge-based project management system. The brief overview of concepts and results concerning the allocation problem under uncertainty described by uncertain variables is presented. An application of two-level decomposition of the complex and the allocation taking into account uncertain and random parameters in the description of the operations are discussed. Two simple examples illustrate the approach presented.
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