A neural network job-shop scheduler

Journal of Intelligent Manufacturing

Volumn 19, Issue 2, 2008, Pages 191-201

  Weckman, Gary R ^a   Ganduri, Chandrasekhar V ^a   Koonce, David A ^a  

^a Ohio University   (United States)

Author keywords

Artificial neural networks; Genetic algorithms; Job shop; Machine learning; Scheduling

Indexed keywords

GENETIC ALGORITHMS; JOB ANALYSIS; LEARNING SYSTEMS; SCHEDULING;

BENCHMARK PROBLEM; DOMAIN KNOWLEDGE;

NEURAL NETWORKS;

EID: 40849126576     PISSN: 09565515     EISSN: 15728145     Source Type: Journal    
DOI: 10.1007/s10845-008-0073-9     Document Type: Article

References (30)

1. Adams J., Balas E., Zawack D. (1988). The shifting bottleneck procedure for job shop scheduling. Management Science 34(3): 391-401
2. Agarwal A., Colak S., Eryarsoy E. (2006). Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach. European Journal of Operational Research 169(3): 801-815
3. Baker K.R. (1974). Introduction to sequencing and scheduling. New York, Wiley
4. Blackstone J.H., Jr., Phillips D.T., Hogg G.L. (1982). A state-of-the-art survey of dispatching rules for manufacturing job shop operations. International Journal of Production Research 20(1): 27
5. Cheung J.Y. (1994). Scheduling. In: Dagli C.H. (ed) Artificial neural networks for intelligent manufacturing (1st ed). London New York, Chapman & Hall, pp. 469
6. Dagli C.H., Sittisathanchai S. (1995). Genetic neuro-scheduler: A new approach for job shop scheduling. International Journal of Production Economics 41(1-3): 135-145
7. Dietterich, T. (1996). Machine learning. ACM Computing Surveys (CSUR), 28(4es), 3-es.
8. Fisher H., Thompson G.L. (1963). Probabilistic learning combinations of local job-shop scheduling rules. In: Muth J.F., Thompson G.L. (eds) Industrial scheduling. Englewood Cliffs New Jersy, Prentice Hall, pp. 225-251
9. Fonseca D.J., Navaresse D. (2002). Artificial neural networks for job shop simulation. Advanced Engineering Informatics 16(4): 241-246
10. Foo, Y. S., & Takefuji, T. (1988). Integer linear programming neural networks for job-shop scheduling. IEEE International Conference on Neural Networks, San Diego, California, July 24-27 (Vol. 2, pp. 341-348).
11. Foo S.Y., Takefuji Y., Szu H. (1995). Scaling properties of neural networks for job-shop scheduling. Neurocomputing 8(1): 79-91
12. French S. (1982). Sequencing and scheduling: An introduction to the mathematics of the job-shop. Chichester, West Sussex: E. Horwood
13. Giffler B., Thompson G.L. (1960). Algorithms for solving production-scheduling problems. Operations Research 8(4): 487-503
14. Jain, A. S., & Meeran, S. (1996). Scheduling a job-shop using a modified back-error propagation neural network. Paper presented at the Proceedings of the IMS'96 First Symposium on Intelligent Manufacturing Systems Conference, Adapazari, Turkey, May 30-31 (pp. 462-474).
15. Jain A.S., Meeran S. (1998). Job-shop scheduling using neural networks. International Journal of Production Research 36(5): 1249-1272
16. Käschel, J., Teich, T., Köbernik, G., & Meier, B. (1999). Algorithms for the job shop scheduling problem: A comparison of different methods. In European Symposium on Intelligent Techniques, Greece (pp. 3-4).
17. Koonce D.A., Tsai S.-C. (2000). Using data mining to find patterns in genetic algorithm solutions to a job shop schedule. Computers and Industrial Engineering 38(3): 361-374
18. Lawrence, S. (1984). Resource constrained project scheduling: An experimental investigation of heuristic scheduling techniques (supplement). Pittsburgh, PA: Graduate School of Industrial Administration, Carnegie-Mellon University.
19. Mitchell T.M. (1997). Machine learning. New York, McGraw-Hill
20. Príncipe J.C., Euliano N.R., Lefebvre W.C. (2000). Neural and adaptive systems: Fundamentals through simulations. New York, Wiley
21. Rabelo, L. C., & Alptekin, S. (1989). Using hybrid neural networks/expert systems for intelligent scheduling in flexible manufacturing systems. In IJCNN: International Joint Conference on Neural Networks, June 18-22, Washington (Vol. 2, 608 pp).
22. Rumelhart D.E., Hinton G.E., Williams R.J. (1986). Learning representations by back-propagating errors. Nature 323: 533-536
23. Shah, N., & Koonce, D. A. (2004). Using distributed genetic algorithms for solving job shop scheduling problems. In Proceedings of the IIE 2004 Annual Conference, Houston, TX.
24. Sormaz, D. N. (2003). Application of space search tools in intelligent manufacturing planning. In Industrial Engineering Research Conference, May 18-20, Portland, OR.
25. Turban E., Aronson J.E. (2001). Decision support systems and intelligent systems (6th ed). Upper Saddle River, NJ, Prentice Hall
26. Widrow B., Rumelhart D.E., Lehr M.A. (1994). Neural networks: Applications in industry, business and science. Communications of the ACM 37(3): 93-105
27. Yamada T., Nakano R. (1992). A genetic algorithm applicable to large-scale job-shop instances. In: Manner R., Manderick B. (eds) Parallel instance solving from nature 2. Amsterdam, North-Holland, pp. 281-290
28. Yamada, T., & Nakano, R. (1997). Job-shop scheduling. In A. M. S. Zalzala & P. J. Fleming (Eds.), Genetic algorithms in engineering systems (pp. 134-160). The Institution of Electrical Engineers.
29. Yang S., Wang D. (2000). Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling. IEEE Transactions on Neural Networks 11(2): 474-486
30. Yu H., Liang W. (2001). Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling. Computers and Industrial Engineering 39(3-4): 337-356