This study develops a dynamic scheduling model for parallel machine scheduling problem based on genetic algorithm (GA). GA combined with discrete event simulation to minimize the makespan and verifies the effectiveness of the developed model. This research consists of two stages. In the first stage, work sequence will be generated using GA, and the second stage developed work schedule applied to a real work area to verify that it could be executed in real work environment and remove the overlapping work, which causes bottleneck and long lead time. If not, go back to the first stage and develop another schedule until satisfied. Small size problem was experimented and suggested a reasonable schedule within limited resources. As a result of this research, work efficiency is increased, cycle time is decreased, and due date is satisfied within existed resources.

Changes in manufacturing system are those that occur during production and cause the systems to behave unpredictably. So scheduling problem in this dynamic industrial environments is very complex. The main concept of this dissertation is to continuously m

Change in manufacturing systems are those that occur during production and cause the systems to behave unpredictably. So scheduling problem in this dynamic industrial environments is very complex. The main concept of This dissertation is to continuously monitor a manufacturing system' status(RPJ, RLJ, RSDJ, JIT) and detect or predict a change so that scheduling system will react by Modifying production schedule(dispaching rule) to lessen the effects of this change.