Engineering Applications of Artificial Intelligence vol:25 issue:2 pages:222-228
The present paper offers an integrated approach to real-world production scheduling for the food processing industries. A manufacturing execution system is very appropriate to monitor and control the activities on the shop floor. Therefore, a specialized scheduler, which is the focus of this paper, has been developed to run at the core of such a system. The scheduler builds on the very general Resource Constrained Project Scheduling Problem with
Generalized Precedence Relations. Each local decision step (e.g. choosing a route in the plant layout) is modeled as a separate module interconnected in a feedback loop. The quality of the generated schedules will guide the overall search process to continuously improve the decisions at an intermediate level by using local search strategies. Besides optimization methods, data mining techniques are applied to historical data in order to feed the scheduling process with realistic background knowledge on key performance indicators, such as processing times, set-up times, breakdowns, etc. The approach leads to substantial speed and quality improvements of the scheduling process compared to the manual practice common in production companies. Moreover, our modular approach allows for further extending or improving modules separately, without interfering with other modules.