Genetic-based Approaches to Predictive-Reactive Scheduling in Flexible Manufacturing Systems

Scheduling plays a vital role in ensuring the effectiveness of the production control of a flexible manufacturing system (FMS). The scheduling problem in advance manufacturing system such as flexible manufacturing system (FMS) is considered as dynamic as new orders may arrive intermittently day-to-day. The newly arrived orders need to be desegregated with the existing production schedule immediately by preserving the efficiency and the stability of the existing schedule. This research focus on addressing the FMS rescheduling problem based on predictive-reactive approach. Constrainedchromosomes together with appropriate mutation and crossover are used in order to assist Genetic Algorithms (GA) in providing high performance predictive schedule with minimum computational cost. A good quality schedule is crucial in order to ensure the efficiency and the stability of the existing schedule after rescheduling. Then, reshuffle-based and nonreshuffle-based match-up genetic approaches are used to accommodate new orders by manipulating available idle times on machines and sequencing the operations to the allocated machines, respectively. The efficiency and the stability of the schedules produced by the rescheduling algorithms with different insertion time on the different level of schedule saturation was investigated and compared. Four datasets from two different FMS environments have been used for experiments. The experiments show that the satisfaction grades of the efficiency and the stability of normal orders are higher than rush orders. Based on the observation, we can conclude that the non-reshuffle-based genetic match-up algorithms can be expected to maintain the efficiency and stability of the schedule after rescheduling when new orders arrive.