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Digital Library

of the European Council for Modelling and Simulation

 

Title:

Discrete Event Optimization: Theory, Applications And Future Challenges

Authors:

Andrea Matta

Published in:

 

 

(2016).ECMS 2016 Proceedings edited by: Thorsen Claus, Frank Herrmann, Michael Manitz, Oliver Rose, European Council for Modeling and Simulation. doi:10.7148/2016

 

 

ISBN: 978-0-9932440-2-5

 

30th European Conference on Modelling and Simulation,

Regensburg Germany, May 31st – June 3rd, 2016

 

Citation format:

Andrea Matta (2016). Discrete Event Optimization: Theory, Applications And Future Challenges, ECMS 2016 Proceedings edited by: Thorsten Claus, Frank Herrmann, Michael Manitz, Oliver Rose  European Council for Modeling and Simulation. doi:10.7148/2016-0005

DOI:

http://dx.doi.org/10.7148/2016-0005

Abstract:

Optimization of discrete event systems is often time consuming and also requires specific approaches due to the fact that general methodologies cannot be successfully applied to any kind of system. Conventional approaches use simulation as a black-box oracle to estimate performance at design points generated by a separate optimization algorithm. This decoupled approach fails to exploit an important advantage: simulation codes are white-boxes, at least to their creators. In fact, the full integration of the simulation model and the optimization algorithm is possible in many situations.

The methodology Discrete Event Optimization (DEO) is presented. DEO allows the development of integrated simulation-optimization models for queueing systems by means of the ERGLite formalism, a subclass of ERGs (Entity Relationships Graphs). Furthermore, DEO provides a formal way to map ERGLs into mathematical formulations for optimization of queueing systems. In case the obtained model is a MILP (Mixed Integer Linear programming), DEO also provides a formal way to approximate the obtained models based. The analytical properties of the obtained models are analyzed in the frameworks of Sample Path Optimization and Mathematical Programming. Several examples will be presented to show the applicability of DEO and to point out its strengths and drawbacks. Research challenges will also be identified.

 

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