Learning-Based Metaheuristic for Scheduling Unrelated Parallel Machines With Uncertain Setup Times

Setup time consists of all the activities that need to be completed before the production process takes place. The extant scheduling predominantly relies on simplistic methods, like the average value obtained from historical data, to estimate setup times. However, such methods are incapable of repre...

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Bibliographic Details
Published in:	IEEE access 2020, Vol.8, p.74065-74082
Main Authors:	Cheng, Chen-Yang, Pourhejazy, Pourya, Ying, Kuo-Ching, Li, Shu-Fen, Chang, Chieh-Wen
Format:	Article
Language:	eng
Subjects:	Decision trees Estimation Forging Heuristic methods Job shop scheduling Job shops metaheuristic Radio frequency random-forest Scheduling Setup times Uncertainty unrelated parallel machines Vegetation
Online Access:	Get full text
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Summary:	Setup time consists of all the activities that need to be completed before the production process takes place. The extant scheduling predominantly relies on simplistic methods, like the average value obtained from historical data, to estimate setup times. However, such methods are incapable of representing the real industry situation, especially when the setup time is subject to significant uncertainties. In this situation, the estimation error increases proportionally to the problem size. This study proposes a Random-Forest-based metaheuristic to minimize the makespan in an Unrelated Parallel Machines Scheduling Problem (UPMSP) with uncertain machine-dependent and job sequence-dependent setup times (MDJSDSTs). Taking the forging industry as an example, the numerical experiments show that the error percentage for the setup time estimation substantially decreases when the proposed approach is applied. This improvement is particularly significant when large-scale problems are sought. Overall, this study highlights the role of advanced analytics in bridging the gap between scheduling theory and practice.
ISSN:	2169-3536 2169-3536