TY - GEN
T1 - Discrete event optimization
T2 - Winter Simulation Conference, WSC 2015
AU - Pedrielli, Giulia
AU - Matta, Andrea
AU - Alfieri, Arianna
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/16
Y1 - 2016/2/16
N2 - Optimization of discrete event systems conventionally uses 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. In this contribution, a framework previously proposed by the authors, based on the mathematical programming methodology, is presented under a wider perspective. We show how to derive mathematical models for solving optimization problems while simultaneously considering the dynamics of the system to be optimized. Concerning the solution methodology, we refer back to retrospective optimization (RO) and sample path optimization (SPO) settings. Advantages and drawbacks deriving from the use of mathematical programming as work models within the RO (SPO) framework will be analyzed and its convergence properties will be discussed.
AB - Optimization of discrete event systems conventionally uses 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. In this contribution, a framework previously proposed by the authors, based on the mathematical programming methodology, is presented under a wider perspective. We show how to derive mathematical models for solving optimization problems while simultaneously considering the dynamics of the system to be optimized. Concerning the solution methodology, we refer back to retrospective optimization (RO) and sample path optimization (SPO) settings. Advantages and drawbacks deriving from the use of mathematical programming as work models within the RO (SPO) framework will be analyzed and its convergence properties will be discussed.
UR - http://www.scopus.com/inward/record.url?scp=84962861969&partnerID=8YFLogxK
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U2 - 10.1109/WSC.2015.7408515
DO - 10.1109/WSC.2015.7408515
M3 - Conference contribution
AN - SCOPUS:84962861969
T3 - Proceedings - Winter Simulation Conference
SP - 3557
EP - 3568
BT - 2015 Winter Simulation Conference, WSC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 December 2015 through 9 December 2015
ER -