NDLI: SIMD architecture for job shop scheduling problem solving

Content Provider	IEEE Xplore Digital Library
Author	Kuan-Hung Chen Shi-Chung Chang Tzi-Dor Chiueh Luh, P.B. Xing Zhao
Copyright Year	2001
Description	Author affiliation: Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan (Kuan-Hung Chen)
Abstract	Job shop is a typical environment for manufacturing high-variety and low-volume discrete parts. Good scheduling is critical and challenging to the competitiveness of job shops. The Lagrangian relaxation neural network (LRNN) provides an approach of quantifiable quality and successful industrial applications. To further speed up scheduling for large-scale problems, in this paper, the parallelism of the LRNN approach is exploited for hardware implementation. New designs include a SIMD architecture, its associated instruction set and detailed circuits. Logic level simulation of the circuit design shows consistent schedules with those obtained by a software implementation. The hardware implementation is expected to have a one to two orders speed-up over the software one.
Sponsorship	IEEE Circuits & Syst. Soc
Starting Page	530
Ending Page	533
File Size	462811
Page Count	4
File Format	PDF
ISBN	0780366859
DOI	10.1109/ISCAS.2001.922291
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-05-06
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Job shop scheduling Problem-solving Hardware Lagrangian functions Neural networks Manufacturing industries Large-scale systems Computer architecture Circuits Logic design
Content Type	Text
Resource Type	Article

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