Description
This paper addresses the scheduling problem of a complex manufacturing environment. Two approaches are investigated—autonomous scheduling and genetic algorithms-based multi-objective optimisation. Both approaches are compared based on computational time required, re-scheduling frequency, and the quality of the generated schedule. Results analysis demonstrates that autonomous scheduling provides better quality solutions in shorter time than a genetic algorithms-based approach. The proposed algorithms have been verified by case study.