Abstract
Job-shop scheduling is usually a strongly NP-hard problem of combinatorial optimization problems and is one of the most typical production scheduling problem. It is usually very hard to find its optimal solution. In this paper, a new hybrid approach in dealing with this job-shop scheduling problem based on artificial neural network and genetic algorithm (GA) is presented. The GA is used for optimization of sequence and neural network (NN) is used for optimization of operation start times with a fixed sequence. New type of neurons which can represent processing restrictions and resolve constraint conflict are defined to construct a constraint neural network (CNN). CNN with a gradient search algorithm is applied to the optimization of operation start times with a fixed processing sequence. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency.
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References
Solimanpur, M., Vrat, P., Shankar, R.: A Neuro-Tabu Search Heuristic for the Flow Shop Scheduling Problem. Computers and Operations Research 13, 2151–2164 (2004)
Geyik, Faruk, Cedimoglu, Ismail Hakki.:The Strategies and Parameters of Tabu Search for Job-Shop Scheduling 4, 439–448 (2004)
Tang, L., Liu, J., Rong, A., Yang, Z.: A Review of Planning and Scheduling Systems and Methods for Integrated Steel Production. European Journal of Operational Research 1, 1–20 (2001)
Feng, Y., Feng, Z., Peng, Q.: Intelligent Hybrid Optimization Strategy and its Application to Flow-Shop Scheduling. Journal of Xi’an Jiaotong University 8, 779–782 (2004)
Fonseca, D.J., Navaresse, D.: Artificial Neural Networks for Job Shop Simulation. Advanced Engineering Informatics 4, 241–246 (2002)
Chen, P., Liu, J., Luo, J.: Study on Pattern Recognition of the Quality Control Chart Based on Neural Network. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA), pp. 790–793 (2002)
Dominic, P.D.D., Kaliyamoorthy, S., Kumar, Saravana, M.: Efficient Dispatching Rules for Dynamic Job Shop Scheduling. International Journal of Advanced Manufacturing Technology 2, 70–75 (2004)
Wang, L., Zheng, D.Z.: A Modified Genetic Algorithm for Job Shop Scheduling. International Journal of Advanced Manufacturing Technology 1, 72–76 (2002)
Wang, Z., Chen, Y., Wang, N.: Research on Dynamic Process Planning System Considering Decision about Machines. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA), pp. 2758–2762 (2004)
Haq, A.N., Ravindran, D., Aruna, V., et al.: A Hybridisation of Metaheuristics for Flow Shop Scheduling. International Journal of Advanced Manufacturing Technology 5, 376–380 (2004)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Muth, Thompson, G.L.: Industrial Scheduling. Prentice Hall, Englewood Cliffs (1963)
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhao, F., Hong, Y., Yu, D., Chen, X., Yang, Y. (2005). Integration of Artificial Neural Networks and Genetic Algorithm for Job-Shop Scheduling Problem. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_123
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DOI: https://doi.org/10.1007/11427391_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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