TY - GEN
T1 - Near optimal jobshop scheduling using neural network parallel computing
AU - Hanada, Akira
AU - Ohnishi, Kouhei
PY - 1993/12/1
Y1 - 1993/12/1
N2 - A parallel algorithm based on the neural network model for jobshop scheduling problem is presented in this paper. In the manufacturing system, it is becoming more complex to manage operations of facilities, because of many requirements and constraints such as to increase product throughput, reduce work-in-process and keep the due date. The goal of the proposed parallel algorithm is to find a near-optimum scheduling solution for the given schedule. The proposed parallel algorithm requires N × N processing elements (neurons) where N is the number of operations. Our empirical study on the sequential shows the behavior of the system.
AB - A parallel algorithm based on the neural network model for jobshop scheduling problem is presented in this paper. In the manufacturing system, it is becoming more complex to manage operations of facilities, because of many requirements and constraints such as to increase product throughput, reduce work-in-process and keep the due date. The goal of the proposed parallel algorithm is to find a near-optimum scheduling solution for the given schedule. The proposed parallel algorithm requires N × N processing elements (neurons) where N is the number of operations. Our empirical study on the sequential shows the behavior of the system.
UR - http://www.scopus.com/inward/record.url?scp=0027846413&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:0027846413
SN - 0780308913
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 315
EP - 320
BT - Plenary Session, Emerging Technologies, and Factory Automation
A2 - Anon, null
PB - Publ by IEEE
T2 - Proceedings of the 19th International Conference on Industrial Electronics, Control and Instrumentation
Y2 - 15 November 1993 through 18 November 1993
ER -