| dc.description.abstract | In the semiconductor environment, consider the scheduling of n jobs and m parallel machines. Each job has a different recipe, the same recipe can be batched together, and the batch processing time is given by the longest job processing time included in the batch processing. We have machine eligibility for each machine, and different jobs will have time window constraints, they must be processed within a certain time, otherwise scrapping will occur, causing a burden on the process cost. Therefore, our research objective is to find the minimum makespan under these environmental conditions and reduce the total waste of material.
In order to find a solution to this problem, this research methodology is based on genetic algorithm, coupled with the saving method in the traditional car dispatching problem, combined with batch characteristics and machine eligibility on the chromosome structure, and improved the crossover and mutation researches studied by previous researchers and add the saving method to the algorithm. Then we use mixed integer programming to compare the traditional genetic algorithm and the saving based genetic algorithm. Finally, we explore whether there is a statistically significant effect.
According to research, we found that in the scheduling environment of small problems, the saving based genetic algorithm will not cause a significant difference, but in the scheduling environment of the big problem, the saving based genetic algorithm effectively reduces the makespan and materials were wasted, so a better solution was found. | en_US |