Integration of simulation modeling and inductive learning in an adaptive decision support system
Decision Support Systems - Special issue on model management systems
Simulation-based real-time scheduling: review of recent developments
WSC '95 Proceedings of the 27th conference on Winter simulation
Selective rerouting using simulated steady state system data
Proceedings of the 29th conference on Winter simulation
Machine Learning - Special issue on learning with probabilistic representations
Real-time control of a manufacturing cell using knowledge-based simulation
WSC '91 Proceedings of the 23rd conference on Winter simulation
Machine Learning
Adaptive flow control in flexible flow shop production systems: a knowledge-based approach
Winter Simulation Conference
Simulation aided, knowledge based routing for AGVs in a distribution warehouse
Proceedings of the Winter Simulation Conference
Simulation-based adaption of scheduling knowledge
Proceedings of the Winter Simulation Conference
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The requirements on production systems and their planning and control systems are constantly growing. Systems have to be flexible and provide viable solutions at the same time. Different planning and control approaches, such as optimization, simulation and combination of techniques etc., that attempt to solve the scheduling problems are available. Mathematical solutions which can be found in literature didn't solve the real-world problems in an appropriate way. Current knowledge based solutions did not give any value about decision reliability as well as their decision attributes are not differentiate enough. We are developing a new rule based approach by using a combination of simulation and a knowledge generation within a dynamic production planning and -control for flow-shops. Ideas of how knowledge can be trained by simulation are presented. Furthermore which kind of rules and attributes can be used and how decisions about the rule selection can be made are shown.