期刊
IEEE ACCESS
卷 8, 期 -, 页码 74065-74082出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2988274
关键词
Job shop scheduling; Uncertainty; Decision trees; Radio frequency; Estimation; Vegetation; Scheduling; unrelated parallel machines; setup times; random-forest; metaheuristic
资金
- Ministry of Science and Technology, Taiwan, R. O. C. [MOST108-2221-E-027-025]
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.
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