Rolling time horizon job shop scheduling strategy in dynamic environment

The job shop scheduling problem with multi-objective and multi-resource constraints for the batch process in dynamic manufacturing systems is investigated. Then a robust procedure is used for solving this problem. In the procedure, periodic and event-driven rescheduling strategies are employed and the dynamic scheduling problem is decomposed into a series of static scheduling problems, and then the multi-objective genetic algorithms are adopted to optimize each of the static scheduling problems. How to decide rescheduling period, how to split a batch into small batches and how to gather new information from the current shop floor status are discussed. Representation based on operation, machine, worker and vehicle is designed. Then crossover operation, mutation operation and decoding operation for the designed representation are explored. A scheduling example shows that the method is available and efficient.