Browsing by Author "Heavey C."

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Citation - WoS: 3
Citation - Scopus: 5
Demonstration of the Feasibility of Real Time Application of Machine Learning To Production Scheduling
(Institute of Electrical and Electronics Engineers Inc., 2022) Ghasemi A.; Kabak K.E.; Heavey C.
Industry 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.
Citation - WoS: 3
Citation - Scopus: 4
Generating Operating Curves in Complex Systems Using Machine Learning
(Institute of Electrical and Electronics Engineers Inc., 2015) Can B.; Heavey C.; Kabak K.E.
This paper proposes using data analytic tools to generate operating curves in complex systems. Operating curves are productivity tools that benchmark factory performance based on key metrics, cycle time and throughput. We apply a machine learning approach on the flow time data gathered from a manufacturing system to derive predictive functions for these metrics. To perform this, we investigate incorporation of detailed shop-floor data typically available from manufacturing execution systems. These functions are in explicit mathematical form and have the ability to predict the operating points and operating curves. Simulation of a real system from semiconductor manufacturing is used to demonstrate the proposed approach. © 2014 IEEE.
Citation - WoS: 8
Citation - Scopus: 7
Implementing a New Genetic Algorithm To Solve the Capacity Allocation Problem in the Photolithography Area
(Institute of Electrical and Electronics Engineers Inc., 2019) Ghasemi A.; Heavey C.; Kabak K.E.
Photolithography plays a key role in semiconductor manufacturing systems. In this paper, we address the capacity allocation problem in the photolithography area (CAPPA) subject to machine dedication and tool capability constraints. After proposing the mathematical model of the considered problem, we present a new genetic algorithm named RGA which was derived from a psychological concept called Reference Group in society. Finally, to evaluate the efficiency of the algorithm, we solve a real case study problem from a semiconductor manufacturing company in Ireland and compare the results with one of the genetic algorithms proposed in the literature. Results show the effectiveness and efficiency of RGA to solve CAPPA in a reasonable time. © 2018 IEEE