Neural networks in production planning and control

Production Planning and Control

Volumn 10, Issue 4, 1999, Pages 324-339

  Garetti, Marco ^a   Taisch, Marco ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

Advanced computing techniques in PPC; Artificial neural networks; Production planning and control

Indexed keywords

COMPUTER SIMULATION; NEURAL NETWORKS; PROBLEM SOLVING; PROCESS ENGINEERING; STRATEGIC PLANNING;

PRODUCTION PLANNING AND CONTROL (PP&C);

PRODUCTION CONTROL;

EID: 0032658233     PISSN: 09537287     EISSN: 13665871     Source Type: Journal    
DOI: 10.1080/095372899233082     Document Type: Article

References (65)

1. Arizono, I., Yamamoto, A., and Ohta, H., 1992, Scheduling for minimizing total actual flow time by neural networks. International Journal of Production Research, 30, 503-511.
2. Cavalieri, S., Rabe, M., and Taisch, M., 1997, A neural network approach to the plant design problem. Proceedings of the 1s International Conference on Engineering Design and Automation, EDA ‘97, Bangkok, Thailand, CD-ROM.
3. Cavalieri, S., and Taisch, M., 1996, Neural networks in hybrid intelligent manufacturing systems. Proceedings of the APMS ‘96, International Conference on Advances in Production Management Systems, Kyoto, Japan, pp. 591-596.
4. Cheng, C.-S., 1997, A neural network approach for the analysis of control chart patterns. International Journal of Production Research, 35, 667-697.
5. Cho, H., and Wysk, R. A., 1993, A robust adaptive scheduler for an intelligent workstation controller. International Journal of Production Research, 31, 771-789.
6. Corradi, E., Portioli, A., and Taisch, M., 1996 Hybrid production control strategies: a comparison between algorithmic and neural based approach. Proceedings of APMS ‘96 International Conference on Advances in Production Management Systems, Kyoto, Japan, pp. 579-584.
7. Currie, K. R., 1992, An intelligent grouping algorithm for cellular manufacturing. Computers and Industrial Engineering, 23, 109-112.
8. Currie, K.R., and LeClair, S.R., 1993, Self-improving process control for molecular beam epitaxy. International Journal of Advanced Manufacturing Technology, 8, 244-251.
9. Cybenko, G., 1989, Approxim ation by superpositions of sigmoidal function. Mathematical Control, Signals, and Systems, 2, 303-314.
10. Dagli, C., and Sittisathanchai, S., 1993, Genetic neuro-scheduler for job shop scheduling. Computers and Industrial Engineering, 25, 267-270.
11. Foo, Y. P. S., and Takefuji, Y., 1988a, Stochastic neural networks for solving job - shops cheduling: Part1. Problem pre-sentation. Proceedings of the IEEE Td International Conference on Neural Networks, San Diego, Vol. 2, pp. 275-282.
12. Foo, Y. P. S., and Takefuji, Y., 1988b, Stochastic neural networks for solving job-shop scheduling: Part 2. Architecture and simulations. Proceedings of the IEEE Td International Conference on Neural Networks, San Diego, Vol. 2, pp. 283-290.
13. Foo, Y. P. S., and Takefuji, Y., 1988c, Integer linear programm ing neural netw orks for job shop scheduling. Proceedings of the IEEE Td International Conference on Neural Networks, San Diego, Vol. 2, pp. 341-348.
14. Giesbert, P., and Van Der Tang, L., 1992, Dynamics of the customer order decoupling point: impact on information systems for production control. Production Planning and Control, 3, 300-313.
15. Gunasekaran, A., Goyal, S. K., Virtanen, I., and Yli-Olli, 1993, Production planning and control in automated manufacturing systems. Production Planning & Control, 4, 2-16.
16. Hao, G., Shang, J. S., and Vargas, L. G., 1995, A neural network model for on-line control of flexible manufacturing systems. International Journal of Production Research, 33, 2835-2854.
17. Higgins, P., and Browne, J., 1992, Master production schedule: a concurrent planning approach. Production Planning & Control, 3, 2-18.
18. Hill, T., O’Conor, M., and Remus, W., 1996, Neural network models for time series forecasts. Management Science, 42, 1082-1092.
19. Hopfield, J. J., and Tank, D. W., 1985, Neural Computation of Decisions in Optimization Problems, Biol. Cybernetics, Vol. 52, pp. 141-152.
20. Hornik, K., Stinchcombe, M., and White, H., 1989, M ultilayer feedforward networks are universal approximators. Neural Networks, 2, 359-366.
21. Hou, T.-H., Lin, L., and Scott, P. D., 1993, A neural network-based automated inspection system with an application to surface mount devices. International Journal of Production Research, 31, 1171-1187.
22. Hruschka, H., 1993, Determining market response functions by neural network modeling: A comparison to econometric techniques. European Journal of Operational Research, 66, 27-35.
23. Huang, H. H., and Wang, H. P., 1993, Machine fault classification using an ART 2 neural network. International Journal of Advanced Manufacturing Technology, 8, 194-199.
24. Huang, C. N., Lim, C. C., and Ming, C. L., 1992, Comparison of image processing algorithms and neural networks in m ach ine visioninspection. Computers and Industrial Engineering, 23, 105-108.
25. Hwarng, H. B., and Hubele, N. F., 1992, Boltzmann machines that learn to recognize patterns on control charts. Statistics and Computing, 2, 191-202.
26. Hwarng, H. B., and Hubele, N. F., 1993a, Back-propagation pattern recognizers for Xcontrol charts: method ology and performance. Computers and Industrial Engineering, 24, 219-235.
27. Hwarng, H. B., and Hubele, N. F., 1993b, X control chart pattern identification through efficient off-line neural network training. IIE Transactions, 25, 27-40.
28. Hwarng, H. B., 1995, Multilayer perceptrons for detecting cyclic data on control charts. International Journal of Production Research, 33, 3101-3117.
29. I man, R., and Conover, W. J., 1983, Modern Business Statistics (New York: Wiley).
30. JONES, A. T., and Rabelo, L. C., 1993. Real-time decision making using neural nets, simulation and genetic algorithms. International Journal of Flexible A utomation and Integrated Manufacturing, 1, 119-131.
31. Kamal, S., and Burke, L. I., 1996. FACT: A new neural network-based clustering algorithm for group technology. International Journal of Production Research, 34, 919-946.
32. Kim, J. S., Nang, J. H., Yoon, H., and Maeng, S. R., 1992, A Boltzmann machine for scheduling multiprocessor tasks with time constraints. Proceedings of the International Joint Conference on Neural Networks, Beijing, China, Vol. 1, pp. 493-499.
33. Kim, S. Y., Lee, Y. H., and Agnihotri, D., 1995, A hybrid approach to sequencing jobs using heuristic rules and neural networks. Production Planning & Control, 6, 445-454.
34. Kung, S. Y., 1993, Digital Neural Networks (New Jersey: Prentice-H all).
35. Lee, Y. H., Bhaskaran, K., and Pinedo, M., 1992, A heuristic to minimize the total weighted tardiness with sequence dependent setups. Technical Report, IEOR Dept., Columbia University, NY, USA.
36. Lee, W. L., Spedding, T. A., De Souza, R., and Lee, S. S. G., 1996, Neural simulation of a prototype keyboard assembly cell with adaptive control. Journal of Intelligent Manufacturing, 7, 379-386.
37. Lin, H., Yih, Y., and Salvendy, G., 1995, Neural network- based fault diagnosis of hydraulic forging presses in China. International Journal of Production Research, 33, 1939-1951.
38. Luxhoj, J. T., Riis, J. O., and Stensballe, B., 1996, A hybrid econometric-neural network modeling approach for sales forecasting. International Journal of Production Economics, 43, 175-192.
39. Moon, Y. B., and JANOWSKI, R., 1995, A neural network approach for smoothing and categorizing noisy data. Computers in Industry, 26, 23-39.
40. Nagasaka, K., Ichihashi, H., and Leonard, R., 1995, Neurofuzzy GMDH and its application to modelling grinding characteristics. International Journal of Production Research, 33, 1229-1240.
41. Pham, D. T., and Oztemel, E., 1992, Control chart pattern recognition using neural networks. Journal of Systems Engineering, 2, 256-262.
42. Pham, D. T., and Oztemel, E., 1994, Control chart pattern recognition using learning vector quantization nets. International Journal of Production Research, 32, 721-729.
43. Pollard, J. F., Broussard, M. R., Garrison, D. B., and San, K. Y., 1992, Process identification using neural networks. Computers and Chemical Engineering, 16, 253-270.
44. Rogers, D. M., and Dagli, C. H., 1996, An intelligent control system using neural networks. Integrated Computer-Aided Engineering, 3, 199-211.
45. Sabuncuoglu, I., and Gurgun, B., 1996, A neural network m odel for scheduling prob lem s. European Journal of Operational Research, 93, 288-299.
46. Shtub, A., and ZIMERMAN, Y., 1993, A neural-network- based approach for estimating the cost of assembly systems. International Journal of Production Economics, 32, 189-207.
47. Sim, S. K., Yeo, K.T., and Lee, W. H., 1994, An expert neural net ork system for dynamic job shop schedulin g. International Journal of Production Research, 32, 1759-1773.
48. Smith, A. E., 1994, X-bara R control chart interpretation using neural computing. International Journal of Production Research, 32, 309-320.
49. Sun, Y.-L., and Yih, Y., 1996, An intelligent controller for manufacturing cells. International Journal of Production Research, 34, 2353-2373.
50. Suresh, N. C., and Kaparthi, S., 1994, Performance of fuzzy ART neural network for group technology cell formation. International Journal of Production Research, 32, 1693-1713.
51. Thawonmas, R., Shiratori, N., and Noguchi, S., 1992, Realtime neural network scheduler. Proceedings of the International Joint Conference on Neural Networks, Beijing, China, Vol. 1, pp. 391-396.
52. Toure, S., Rabelo, L., and elasco, T., 1993, Artificial neural networks for flexible manufacturing systems scheduling. Computers and Industrial Engineering, 25, 385-388.
53. Vaithyanathan, S., and IGNIZO, J. P., 1992, Stochastic neural network for re source constrained scheduling. Computers and Operations Research, 19, 241-254.
54. Vaithyanathan, S., Burke, L. I., and Magent, M. A., 1996, Massively parallel analog tabu search using neural networks applied to simple plant location problems. European Journal of Operational Research, 93, 317-330.
55. Van Hulle, M. M., 1991, A goal programming network for mixed integerlinear programming: acasestudy for the job shop scheduling problem. International Journal of Neural Systems, 2, 201-209.
56. Venugopal, V., and Narendran, T. T., 1992, Neural network model for design retrievalin manufacturing systems. Computers in Industry, 20, 11-23.
57. Vepsalainen, A., and Morton, T. E., 1987, Priority rules for job shops with weighted tardiness costs. Management Science, 33, 1035-1047.
58. Violante, N., Pontrandolfo, P., and Okogbaa, O. G., 1996, Hopfield neural network for a transnational enterprise’s manufacturing network. In L. F. McGinnis, W. G. Sullivan and M. M. Ahmad (eds) Flexible Automation and Intelligent Manufacturing (New York: Begell House), pp. 88-98.
59. Vollmann, T. E., Berry, W. L., and Whybark, D. C., 1991, Manufacturing Planning and Control System (Homewood, IL: Irwin).
60. Western Electric, 1958, Statistical Quality Control Handbook (Indianapolis: Western Electric).
61. Widrow, B., Rumelhart, D. E., and Lehr, M. A., 1994, Neural networks: applications in industry, business and science. Communications of the ACM, 37, 93-105.
62. Wu, H.J., 1993, A hybrid approach for optimal product design with multiple criteria decision making. Proceedings of the INNS 1993 International Conference of Neural Network Society, Portland, Oregon, Vol. 1, pp. 424-427.
63. Yan, D., Cheng, M., Popplewell, N., and Balakrishnan, S., 1995, Application of neural networks for surface roughness m easurement in finish turning. International Journal of Production Research, 33, 3425-3438.
64. Zhang, C.-S., Yan, P.-F., and Chang, T., 1991, Solving jobshop scheduling problem with priority using neural network. Proceedings of the International Joint Conference on Neural Networks, Singapore, pp. 1361-1366.
65. Zhou, D. N., Cherkassky, V., Baldwin, T. R., and Hong, D. W., 1990, Scaling neural network for job-shop scheduling. Proceedings of the International Joint Conference on Neural Networks, San Diego, Vol. 3, pp. 889-894.