Random and evolution algorithms of the tasks scheduling and of the production scheduling

This work presents contemporary artificial intelligence tools - evolution algorithms and random algorithms designed for the optimalisation of the production scheduling problem for multi-assortment short-series production. The essential idea of SZEZA method has been presented. SZEZA method has the ability of part control of the sequence of tasks. The efficiency of SZEZA has been compared with the efficiency of genetic algorithm AGHAR and with efficiency of other metaheuritstic methods. Problems related with the further development of mentioned algorithms for the purpose of a choice and defining their optimal decision factors and constraints have been described.