A genetic algorithm for the dynamic single machine scheduling problem

This paper starts by studying the performance of two interrelated genetic algorithms (GA) for the static Single Machine Scheduling Problem (SMSP). One is a single start GA, the other, called MetaGA, is a multi-start version GA. The performance is evaluated for total weighted tardiness, on the basis of the quality of scheduling solutions obtained for a limit on computation time. Then, a scheduling system, based on Genetic Algorithms is proposed, for the resolution of the dynamic version of the same problem. The approach used adapts the resolution of the static problem to the dynamic one in which changes may occur continually. This takes into account dynamic occurrences in a system and adapts the current population to a new regenerated population

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