Manufacturing Lead Time Estimation with the Combination of Simulation and Statistical Learning Methods

Pfeiffer, András and Gyulai, Dávid and Kádár, Botond and Monostori, László (2016) Manufacturing Lead Time Estimation with the Combination of Simulation and Statistical Learning Methods. PROCEDIA CIRP, 41. pp. 75-80. 10.1016/j.procir.2015.12.018


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In the paper, a novel method is introduced for selecting tuning parameters improving accuracy and robustness for multi-model based prediction of manufacturing lead times. Prediction is made by setting up models using statistical learning methods (multivariate regression); trained, validated and tested on log data gathered by manufacturing execution systems (MES). Relevant features, i.e., the predictors most contributing to the response, are selected from a wider range of system parameters. The proposed method is tested on data provided by a discrete event simulation model (as a part of a simulation-based prediction framework) of a small-sized flow-shop system. Accordingly, log data are generated by simulation experiments, substituting the function of a MES system, while considering several different system settings (e.g., job arrival rate, test rejection rate). By inserting the prediction models into a simulation-based decision support system, prospective simulations anticipating near-future deviations and/or disturbances, could be supported. Consequently, simulation could be applied for reactive, disturbance-handling purposes, and, moreover, for training the prediction models. (C) 2015 The Authors. Published by Elsevier B.V.

Item Type: Article
Uncontrolled Keywords: SYSTEMS; Statistical learning; Robust prediction; SIMULATION; Lead time
Subjects: Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions: Research Laboratory on Engineering & Management Intelligence
SWORD Depositor: MTMT Injector
Depositing User: MTMT Injector
Date Deposited: 10 Jan 2017 08:31
Last Modified: 21 Jul 2019 14:15

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