An Adaptive Genetic Algorithm based approach for production reactive scheduling of manufacturing systems

The problem for scheduling the manufacturing systems production involves the system modeling task and the application of a technique to solve it. There are several ways used to model the scheduling problem and search strategies have been applied on the models to find a solution. The solutions consider performance parameters like makespan. However, depending on the size and complexity of the system, the response time becomes critical, mostly when itpsilas necessary to reschedule. Researches aim to use Genetic Algorithms as a search method to solve the scheduling problem. This paper proposes the use of Adaptive Genetic Algorithm (AGA) to solve this problem having as performance criteria the minimum makespan and the response time. The probability of crossover and mutation is dynamically adjusted according to the individualpsilas fitness value. The proposed approach is compared with a traditional Genetic Algorithm (GA).