Scheduling project networks with resource constraints and time windows
Annals of Operations Research
Artificial Intelligence Review - Special issue on lazy learning
Computers and Operations Research
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
A holonic approach for manufacturing execution system design: An industrial application
Engineering Applications of Artificial Intelligence
Analyzing scheduling in the food-processing industry: structure and tasks
Cognition, Technology and Work
Manufacturing Execution System - MES
Manufacturing Execution System - MES
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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, setup 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.