Application of genetic algorithms for a tyre production scheduling information system

A optimization scheduling problem in a enterprise of manufacturing tyres is discussed in this paper. And this problem is reduced to a single machine scheduling problem to minimize setup times with batchs setup time depending on sequence. A method for solving tyre production scheduling problem using an effective adaptive hybrid genetic algorithm (AHGA) is proposed. We advance a novel operator (looping & cutting operator) to improve the mountain climbing ability of the genetic algorithm, and put forward adaptive probabilities of crossover and mutation based on information entropy. Computational results show that the proposed adaptive hybrid genetic algorithm is effective and robust.