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Dynamic production system identification for smart manufacturing systems

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journal contribution
posted on 23.05.2018 by Peter Denno, Charles Dickerson, Jennifer Harding
This paper presents a methodology, called production system identification, to produce a model of a manufacturing system from logs of the system's operation. The model produced is intended to aid in making production scheduling decisions. Production system identification is similar to machine-learning methods of process mining in that they both use logs of operations. However, process mining falls short of addressing important requirements; process mining does not (1) account for infrequent exceptional events that may provide insight into system capabilities and reliability, (2) offer means to validate the model relative to an understanding of causes, and (3) updated the model as the situation on the production floor changes. The paper describes a genetic programming (GP) methodology that uses Petri nets, probabilistic neural nets, and a causal model of production system dynamics to address these shortcomings. A coloured Petri net formalism appropriate to GP is developed and used to interpret the log. Interpreted logs provide a relation between Petri net states and exceptional system states that can be learned by means of novel formulation of probabilistic neural nets (PNNs). A generalized stochastic Petri net and the PNNs are used to validate the GP-generated solutions. The methodology is evaluated with an example based on an automotive assembly system.



  • Mechanical, Electrical and Manufacturing Engineering

Published in

Journal of Manufacturing Systems


DENNO, P., DICKERSON, C.E. and HARDING, J.A., 2018. Dynamic production system identification for smart manufacturing systems. Journal of Manufacturing Systems, 48, Part C, pp.192-203.


Elsevier © The Society of Manufacturing Engineers


AM (Accepted Manuscript)

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This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at:

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This paper was accepted for publication in the journal Journal of Manufacturing Systems and the definitive published version is available at






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