Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139933
Title: Modeling and optimal cyclic scheduling of time-constrained single-robot-arm cluster tools via Petri nets and linear programming
Authors: Yang, FaJun
Wu, NaiQi
Qiao, Yan
Zhou, MengChu
Su, Rong
Qu, Ting
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2017
Source: Yang, F., Wu, N., Qiao, Y., Zhou, M., Su, R., & Qu, T. (2020). Modeling and optimal cyclic scheduling of time-constrained single-robot-arm cluster tools via Petri nets and linear programming. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(3), 871-883. doi:10.1109/TSMC.2017.2755599
Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Abstract: Scheduling a cluster tool with wafer residency time constraints is challenging and important in wafer manufacturing. With a backward strategy, the scheduling problem of such singlerobot-arm cluster tools is well-studied in the literature. It is much more challenging to schedule a more general case whose optimal scheduling strategy is not limited to the backward one. This work uses a timed Petri net (PN) to model the dynamic behavior of the system and presents a method to determine the optimal scheduling strategy for the system. Based on its PN model and the obtained strategy, it reveals that the key issue to schedule such a tool is to determine when and how long the robot should wait for. Based on this finding, this work establishes for the first time the necessary and sufficient conditions regarding the existence of an optimal and feasible one-wafer cyclic schedule for singlerobot-arm cluster tools. It then formulates a computationally efficient linear program to find it if existing, and finally gives industrial examples to show the application and power of the proposed method.
URI: https://hdl.handle.net/10356/139933
ISSN: 2168-2216
DOI: 10.1109/TSMC.2017.2755599
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/ 10.1109/TSMC.2017.2755599
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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