A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems

Applied Soft Computing Journal

Volumn 10, Issue 3, 2010, Pages 888-896

  Xing, Li Ning ^a   Chen, Ying Wu ^a   Wang, Peng ^a   Zhao, Qing Song ^a   Xiong, Jian ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Ant Colony Optimization; Combinatorial optimization; Flexible Job Shop Scheduling

Indexed keywords

ANT COLONY OPTIMIZATION; FLEXIBLE JOB-SHOP SCHEDULING; FLEXIBLE JOB-SHOP SCHEDULING PROBLEM; KNOWLEDGE MODEL;

COMBINATORIAL OPTIMIZATION; KNOWLEDGE BASED SYSTEMS; MACHINE SHOPS;

ALGORITHMS;

EID: 77649234984     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2009.10.006     Document Type: Article

References (36)

1. Garey M.R., Johnson D.S., and Sethi R. The complexity of flow shop and job-shop scheduling. Mathematics of Operations Research 1 2 (1996) 117-129
2. Kolonko M. Some new results on Simulated Annealing applied to the Job Shop Scheduling Problem. European Journal of Operational Research 113 1 (1999) 123-136
3. Pezzella F., and Merelli E. A Tabu Search method guided by shifting bottleneck for the Job Shop Scheduling Problem. European Journal of Operational Research 120 2 (2000) 297-310
4. Goncalves J.F., et al. A hybrid genetic algorithm for the Job Shop Scheduling Problem. European Journal of Operational Research 167 1 (2005) 77-95
5. Huang K.L., and Liao C.J. Ant colony optimization combined with taboo search for the job shop scheduling problem. Computers and Operations Research 35 4 (2008) 1030-1046
6. Fonseca D.J., and Navaresse D. Artificial neural networks for job shop simulation. Advanced Engineering Informatics 16 4 (2002) 241-246
7. Tanev I.T., Uozumi T., and Morotome Y. Hybrid evolutionary algorithm-based real-world Flexible Job Shop Scheduling Problem: application service provider approach. Applied Soft Computing 5 1 (2004) 87-100
8. Chen H., and Luh P.B. An alternative framework to Lagrangian relaxation approach for Job Shop Scheduling. European Journal of Operational Research 149 3 (2003) 499-512
9. Huanh W.Q., and Yin A.H. An improved shifting bottleneck procedure for the Job Shop Scheduling Problem. Computers & Operations Research 31 12 (2004) 2093-2110
10. Jansen K., Mastrolilli M., and Solis-Oba R. Approximation schemes for Job Shop Scheduling Problems with controllable processing times. European Journal of Operational Research 167 2 (2005) 297-319
11. Bruker P., and Schlie R. Job-shop scheduling with multi-purpose machines. Computing 45 4 (1990) 369-375
12. Wang L. A hybrid genetic algorithm-neural network strategy for simulation optimization. Applied Mathematics and Computation 170 2 (2005) 1329-1343
13. Reynolds R.G. An introduction to cultural algorithms. Proceedings of the Third Annual Conference on Evolutionary Programming (1994), River Edge, NJ, World Scientific, Singapore 131-139
14. Santosa C.A., Spimb J.A., and Garcia A. Mathematical modeling and optimization strategies (genetic algorithm and knowledge base) applied to the continuous casting of steel. Engineering Applications of Artificial Intelligence 16 5-6 (2003) 511-527
15. Vachhani P., Rengaswamy R., and Venkatasubramanian V. A framework for integrating diagnostic knowledge with nonlinear optimization for data reconciliation and parameter estimation in dynamic systems. Chemical Engineering Science 56 6 (2001) 2133-2148
16. Chung C.J., and Reynolds R.G. A testbed for solving optimization problems using cultural algorithm. Proceedings of the Fifth Annual Conference on Evolutionary Programming (1996), MIT Press, Cambridge 225-236
17. Branke J. Memory-enhanced evolutionary algorithms for dynamic optimization problems. Proceedings of the Congress on Evolutionary Computation (1999), IEEE Press, Piscataway 1875-1882
18. Louis S.J., and McDonnell J. Learning with case-injected genetic algorithms. IEEE Transactions on Evolutionary Computation 8 4 (2004)
19. Michalski R.S. Learnable evolution model: evolution process guided by machine learning. Machine Learning 38 1 (2000) 9-40
20. Ho N.B., Tay J.C., and Lai E.M.K. An effective architecture for learning and evolving flexible job-shop schedules. European Journal of Operational Research 179 2 (2007) 316-333
21. Xia W.J., and Wu Z.M. An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering 48 2 (2005) 409-425
22. Kacem I., Hammadi S., and Borne P. Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man, and Cybernetics, Part C 32 1 (2002) 1-13
23. Brandimarte P. Routing and scheduling in a flexible job shop by Taboo search. Annals of Operations Research 41 1 (1993) 157-183
24. Tung Lf L.F., Li L., and Nagi R. Multi-objective scheduling for the hierarchical control of flexible manufacturing systems. The International Journal of Flexible Manufacturing Systems 11 4 (1999) 379-409
25. Kacem I., Hammadi S., and Borne P. Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation 60 3 (2002) 245-276
26. Hurink E., Jurisch B., and Thole M. Tabu Search for the Job Shop Scheduling Problem with multi-purpose machines. Operations Research Spektrum 15 4 (1994) 205-215
27. Dauzere-Peres S., and Paulli J. An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using Tabu Search. Annals of Operations Research 70 3 (1997) 281-306
28. Mastrolilli M., and Gambardella L.M. Effective neighborhood functions for the flexible job shop problem. Journal of Scheduling 3 1 (2002) 3-20
29. Climer S., and Zhang W. Cut-and-solve: an iteration search strategy for combinatorial optimization problems. Artificial Intelligence 170 8-9 (2006) 714-738
30. Wang L., and Zheng D.Z. An effective hybrid optimization strategy for job-shop scheduling problems. Computers and Operations Research 28 6 (2001) 585-596
31. Yang S.M., Shao D.G., and Luo Y.J. A novel evolution strategy for multiobjective optimization problem. Applied Mathematics and Computation 170 2 (2005) 850-873
32. Kusiak A. Process planning: a knowledge-based and optimization perspective. IEEE Transactions on Robotics and Automation 7 3 (1991) 257-266
33. Dutta A., and Mitra S. Integrating heuristic knowledge and optimization models for communication network design. IEEE Transactions on Knowledge and Data Engineering 5 6 (1993) 999-1017
34. Reynolds R.G., and Zhu S. Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics 31 1 (2001) 1-18
35. Leung Y.W., and Wang F. An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Transactions on Evolutionary Computation 5 1 (2001) 41-53
36. Stuzle T., and Hoos H. MAX-MIN Ant System. Journal of Future Generation Computer Systems 16 8 (2001) 889-914