Knowledge Enriched Short-term Scheduling for Engineer-to-order Products

Contemporary shop-floors are highly affected by the ever-increasing complexity that is caused by the fluctuating customer demands. Therefore, a high degree of flexibility is needed and the scheduling of manufacturing tasks must be agile to changes. For addressing this challenge, this research work proposes a knowledge enriched short-term job-shop scheduling engine. More precisely, it focuses on the short-term scheduling of the resources of the machine shop, through an artificial intelligence algorithm that generates and evaluates alternative assignments of resources to tasks. Based on the requirements of a new order, a similarity mechanism retrieves successfully executed past orders together with a dataset that includes the processing times, the job and task sequence and the suitable resources. Afterwards it adapts these parameters to the requirements of the new order so as to evaluate the alternative schedules and identify a good alternative in a timely manner. The deriving schedule can be presented on mobile devices and it can be manipulated by the planner on-the-fly respecting tasks precedence constraints and machine availability. A case study from the mold making industry is used for validating the proposed framework.