Ghasemi A.Kabak K.E.Heavey C.2023-06-162023-06-1620229.80E+120891-7736https://doi.org/10.1109/WSC57314.2022.10015436https://hdl.handle.net/20.500.14365/36612022 Winter Simulation Conference, WSC 2022 -- 11 December 2022 through 14 December 2022 -- 186263Industry 4.0 has placed an emphasis on real-time decision making in the execution of systems, such as semiconductor manufacturing. This article will evaluate a scheduling methodology called Evolutionary Learning Based Simulation Optimization (ELBSO) using data generated by a Manufacturing Execution System (MES) for scheduling a Stochastic Job Shop Scheduling Problem (SJSSP). ELBSO is embedded within Ordinal Optimization (OO), where in the first phase it uses a meta model, which previously was trained by a Discrete Event Simulation model of a SJSSP. The meta model used within ELBSO uses Genetic Programming (GP)-based Machine Learning (ML). Therefore, instead of using the DES model to train and test the meta model, this article uses historical data from a front-end fab to train and test. The results were statistically evaluated for the quality of the fit generated by the meta-model. © 2022 IEEE.eninfo:eu-repo/semantics/closedAccessDecision makingDiscrete event simulationGenetic algorithmsGenetic programmingJob shop schedulingProduction controlQuality controlReal time systemsSemiconductor device manufactureStochastic systemsEvolutionary LearningMachine-learningMeta modelMetamodelingProduction SchedulingReal time decision-makingReal-time applicationReal-time decision makingSimulation optimizationStochastic job shop scheduling problemMachine learningConference Object10.1109/WSC57314.2022.100154362-s2.0-85147456582