TY - JOUR
T1 - Comparison of conventional and neural network heuristics for job shop scheduling
AU - Cherkassky, Vladimir
AU - Zhou, Deming N.
PY - 1992/7/1
Y1 - 1992/7/1
N2 - A new neural network for solving job shop scheduling problems is presented. The proposed Scaling Neural Network (SNN) achieves good (linear) scaling properties by employing nonlinear processing in the feedback connections. Extensive comparisons between SNN and conventional heuristics for scheduling are presented. These comparisons indicate that the proposed SNN allows to obtain better scheduling solutions than commonly used heuristics, especially for large problems. Key words: Heuristic, job shop scheduling, neural network, scaling.
AB - A new neural network for solving job shop scheduling problems is presented. The proposed Scaling Neural Network (SNN) achieves good (linear) scaling properties by employing nonlinear processing in the feedback connections. Extensive comparisons between SNN and conventional heuristics for scheduling are presented. These comparisons indicate that the proposed SNN allows to obtain better scheduling solutions than commonly used heuristics, especially for large problems. Key words: Heuristic, job shop scheduling, neural network, scaling.
UR - http://www.scopus.com/inward/record.url?scp=3342879339&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=3342879339&partnerID=8YFLogxK
U2 - 10.1117/12.140142
DO - 10.1117/12.140142
M3 - Conference article
AN - SCOPUS:3342879339
VL - 1710
SP - 815
EP - 825
JO - Proceedings of SPIE - The International Society for Optical Engineering
JF - Proceedings of SPIE - The International Society for Optical Engineering
SN - 0277-786X
T2 - Science of Artificial Neural Networks 1992
Y2 - 20 April 1992
ER -