A genetic algorithm for a setup operator constrained flexible flow shop lot streaming with detached and sequence dependent setups
This thesis explores the use of genetic algorithms to optimize the flexible flow shop scheduling process in the presence of dual resource constraints. The objective is to minimize the makespan, which is a critical factor in improving production efficiency. Lot streaming is used to reduce the processing time of the jobs and the genetic algorithm is applied to identify the optimal sequence of jobs. The proposed approach is tested on a set of benchmark problems and compared with other solution representations in GA. The results show that the proposed approach can effectively reduce the makespan and improve the overall performance of the scheduling process in flexible flow shop systems with dual resource constraints.