TY - GEN
T1 - Transforming Discrete Event Models To Machine Learning Models
AU - Sarjoughian, Hessam S.
AU - Fallah, Forouzan
AU - Saeidi, Seyyedamirhossein
AU - Yellig, Edward J.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Discrete event simulation, formalized as deductive modeling, has been shown to be effective for studying dynamical systems. Development of models, however, is challenging when numerous interacting components are involved and should operate under different conditions. Machine Learning (ML) holds the promise to help reduce the effort needed to develop models. Toward this goal, a collection of ML algorithms, including Automatic Relevance Determination is used. Parallel Discrete Event System Specification (PDEVS) models are developed for Single-stage and Two-stage cascade factories. Each model is simulated under different demand profiles. The simulated data sets are partitioned into subsets, each for one or more model components. The ML algorithms are applied to the data sets for generating models. The throughputs predicted by the ML models closely match those in the PDEVS simulated data. This study contributes to modeling by demonstrating the potential benefits and complications of utilizing ML for discrete-event systems.
AB - Discrete event simulation, formalized as deductive modeling, has been shown to be effective for studying dynamical systems. Development of models, however, is challenging when numerous interacting components are involved and should operate under different conditions. Machine Learning (ML) holds the promise to help reduce the effort needed to develop models. Toward this goal, a collection of ML algorithms, including Automatic Relevance Determination is used. Parallel Discrete Event System Specification (PDEVS) models are developed for Single-stage and Two-stage cascade factories. Each model is simulated under different demand profiles. The simulated data sets are partitioned into subsets, each for one or more model components. The ML algorithms are applied to the data sets for generating models. The throughputs predicted by the ML models closely match those in the PDEVS simulated data. This study contributes to modeling by demonstrating the potential benefits and complications of utilizing ML for discrete-event systems.
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U2 - 10.1109/WSC60868.2023.10407348
DO - 10.1109/WSC60868.2023.10407348
M3 - Conference contribution
AN - SCOPUS:85185370641
T3 - Proceedings - Winter Simulation Conference
SP - 2662
EP - 2673
BT - 2023 Winter Simulation Conference, WSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Winter Simulation Conference, WSC 2023
Y2 - 10 December 2023 through 13 December 2023
ER -