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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 | Schools: | School of Electrical and Electronic Engineering | 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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