Scheduling is an important element of manufacturing systems because it allows to improve the system performance and serves as an overall plant on which system activities are based. The main purpose of this paper is to explore the use of evolutionary computation techniques for solving real world optimization problems. These classes of problems have additional difficulties for the traditional optimization techniques. This paper presents a simple and general framework based on Genetic Algorithms to solve dynamic Job-Shop scheduling problems. A new generation of initial individual and population is proposed. The proposed framework adapts the resolution of the deterministic problem to the non-deterministic one in which changes may occur continually. THis takes into account dynamic occurrences in a manufacturing system and adapts the current population.
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