Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment

Applied Soft Computing Journal

Volumn 8, Issue 4, 2008, Pages 1295-1304

  Lee, Key K ^a  

^a Inje University   (South Korea)

Author keywords

Distributed fuzzy rule; Dynamic learning classifier; Flexible manufacturing system; Job dispatching

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONFORMAL MAPPING; FLEXIBLE MANUFACTURING SYSTEMS; FUZZY RULES; FUZZY SETS; MANUFACTURE; NEURAL NETWORKS; SCHEDULING; SET THEORY; SYSTEMS ENGINEERING;

ADAPTIVE SCHEDULING; CURRENT STATE; DISPATCHING PROBLEM; DISPATCHING RULES; DISTRIBUTED FUZZY RULE; DYNAMIC LEARNING; DYNAMIC LEARNING CLASSIFIER; FLEXIBLE MANUFACTURING SYSTEM; FLEXIBLE MANUFACTURING SYSTEM (FMS); FUZZY NUMBERS; FUZZY RULE-BASED SYSTEMS; HUMAN EXPERTS; JOB-DISPATCHING; MANUFACTURING ENVIRONMENTS; RULE BASE; RULE GENERATION; SIMULATION METHODS; SYSTEM STATES; TRAINING DATA;

LAWS AND LEGISLATION;

EID: 50149101510     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2007.11.005     Document Type: Article

References (28)

1. Aytug H., Bhattacharyya S., Koehler G.J., and Snowdon J.L. A review of machine learning in scheduling. IEEE Trans. Eng. Manage. 41 (1994) 165-171
2. Aytug H., Koehler G.J., and Snowdon J.L. Genetic learning of dynamic scheduling within a simulation environment. Comput. Oper. Res. 21 (1994) 909-925
3. Babiceanu R.F., Frank Chen F., and Sturges R.H. Real-time holonic scheduling of material handling operations in a dynamic manufacturing environment. Robot. Comput.-Int. Manuf. 21 (2005) 328-337
4. Blackstone J.H., Phillips D.T., and Hogg G.L. A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int. J. Prod. Res. 20 (1982) 27-45
5. Chang P., Hsieh J., and Wang C. Adaptive multi-objective genetic algorithms for scheduling of drilling operation in printed circuit board industry. Appl. Softw. Comput. 7 (2007) 800-806
6. Cho S. A distributed time-driven simulation method for enabling real-time manufacturing shop floor control. Comput. Ind. Eng. 49 (2005) 572-590
7. Chong C.S., Sivakumar A., and Gay R. Simulation-based scheduling for dynamic discrete manufacturing. Proceedings of the Winter Simulation Conference (2003) 1465-1473
8. Duncan D.B. Multiple range and multiple F tests. Biometrics 11 (1955) 1-42
9. Gholamian M.R., and Fatemi Chomi S.M.T. Meta knowledge of intelligent manufacturing: an overview of state-of-the-art. Appl. Soft. Comput. 7 (2007) 1-16
10. Haykin S. Neural Networks: A Comprehensive Foundation (1994), Prentice-Hall, New Jersey
11. Ishibuchi H., Nakashima T., and Murata T. Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans. Syst. Man Cybern. B 29 (1999) 601-618
12. Ishibuchi H., Nozaki K., and Tanaka H. Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets Syst. 52 (1992) 21-32
13. Jones A., Rabelo L., and Yih Y. A hybrid approach for real-time sequencing and scheduling. Int. J. Comput. Int. Manuf. 8 (1995) 145-154
14. Kuncheva L.I. How good are fuzzy if-then classifiers?. IEEE Trans. Syst. Man Cybern. B 30 (2000) 501-509
15. Lee K.K., Yoon W.C., and Baek D.H. A classification method using a hybrid genetic algorithm combined with an adaptive procedure for the pool of ellipsoids. Appl. Intell. 25 (2006) 293-304
16. Montazeri M., and Wassenhove L.N. Analysis of scheduling rules for an FMS. Int. J. Prod. Res. 28 4 (1990) 785-802
17. Nakasuka S., and Yoshida T. Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool. Int. J. Prod. Res. 32 (1992) 411-431
18. Oduguwa V., Tiwari A., and Roy R. Evolutionary computing in manufacturing industry: an overview of recent applications. Appl. Soft. Comput. 5 (2005) 281-299
19. Park M.-K., Lee K.K., Shon K.-M., and Yoon W.C. Automating the diagnosis and rectification of deflection yoke production using hybrid knowledge acquisition and case-based reasoning. Appl. Intell. 15 (2001) 25-40
20. Park S.C., Paman N., and Shaw M.J. Adaptive scheduling in dynamic flexible manufacturing systems: a dynamic rule selection approach. IEEE Trans. Robot. Autom. 13 (1997) 486-502
21. Pierreval H., and Ralambondrainy H. A simulation and learning technique for generating knowledge about manufacturing systems behavior. J. Oper. Res. Soc. 41 (1990) 461-474
22. Piramuthu S., Raman N., and Shaw M.J. Integration of simulation modeling and inductive learning in an adaptive decision support system. Decis. Support Syst. 9 (1993) 127-142
23. Pongcharoen P., Hicks C., Braiden P.M., and Stewardson D.J. Determining optimum genetic algorithm parameters for scheduling the manufacturing and assembly of complex products. Int. J. Prod. Econ. 78 (2002) 311-322
24. Rodammer F.A., and White K.P. A recent survey of production scheduling. IEEE Trans. Syst. Man Cybern. 18 (1988) 841-851
25. Sabuncuoglu I., and Hommertzheim D.L. Experimental investigation of FMS machine and AGV scheduling rules against the mean flow-time criterion. Int. J. Prod. Res. 30 (1992) 1617-1635
26. Wei L., and Haibin Y. Learning based dynamic approach to job-shop scheduling. Proceedings of the 2001 International Conference on Info-tech and Info-net. Beijing (2001) 274-279
27. Weiss S.M., and Kulikowski C.A. Computer Systems That Learn (1991), Morgan Kaufmann, San Francisco, CA
28. Wu S.-Y., and Wysk R.A. Multi-pass expert control system-a control/scheduling structure for flexible manufacturing cells. J. Manuf. Syst. 7 (1988) 107-120