A hybrid approach of genetic algorithms and local optimizers in cell loading

In this paper, a potential application of evolutionary programming to cell loading is discussed. The objective is to minimize the number of tardy jobs. The proposed approach is a hybrid three-phase approach: 1) evolutionary programming is used to generate a job sequence, 2) a classical scheduling rule is used to assign jobs to the cells, and 3) Moore's algorithm is applied to the jobs assigned to each cell independently. Experimentation results show the impact of number of cells and the strategy adapted on the number of tardy jobs found. The results also indicate that hybrid GA-local optimizer approach improves the solution quality drastically. Finally, it has been also shown that GA alone can duplicate the performance of the hybrid approach with increased population size and number of generations.