Effective hierarchical optimization by a hierarchical multi-space competitive genetic algorithm for the flexible job-shop scheduling problem

Expert Systems with Applications

Volumn 42, Issue 24, 2015, Pages 9434-9440

  Ishikawa, Shudai ^a   Kubota, Ryosuke ^b   Horio, Keiichi ^a  

^a KYUSHU INSTITUTE OF TECHNOLOGY   (Japan)

^b UBE NATIONAL COLLEGE OF TECHNOLOGY   (Japan)

Author keywords

Flexible job shop scheduling problem; Genetic algorithm; Hierarchical optimization; Multiple solution space; Solution space competition

Indexed keywords

ALGORITHMS; COSTS; GENETIC ALGORITHMS; JOB SHOP SCHEDULING; OPTIMAL SYSTEMS; OPTIMIZATION; SCHEDULING;

COMBINATIONAL OPTIMIZATION; DISTRIBUTED GENETIC ALGORITHMS; FLEXIBLE JOB-SHOP SCHEDULING PROBLEM; HIERARCHICAL GENETIC ALGORITHM; HIERARCHICAL OPTIMIZATION; MULTIPLE SOLUTIONS; OPTIMIZATION TECHNIQUES; SOLUTION SPACE;

PROBLEM SOLVING;

EID: 84942311256     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2015.08.003     Document Type: Article

References (27)

1. Brandimarte P. Routing and scheduling in a flexible job shop by tabu search Annals of Operations Research 41 1993 157 183
2. Brucker P., and Schlie R. Job-shop scheduling with multi-purpose machines Computing 45 4 1990 369 375
3. Chen H., Ihlow J., and Lehmann C. A genetic algorithm for flexible job-shop scheduling Proceedings of the IEEE international conference on robotics and automation, Detroit 1999 1120 1125
4. Davis L. Applying adaptive algorithms to epistatic domains Proceeding of the International Joint Conference on Artificial Intelligence, IEEE Computer Society Press, Los Angeles 1985 162 164
5. Garey M.R., Johnson D.S., and Sethi R. The complexity of flowshop and jobshop scheduling Mathematics of Operation Research 1 1976 117 129
6. Glover F. Future paths for integer programming and links to artificial intelligence Computers and Operations Research 13 1986 533 549
7. Goldberg D. Alleles loci and the tsp Proceedings of the First International Conference on Genetic Algorithms and Their Application, Lawrence Erolaum, New Jersy 1985 154 159
8. Holland J. Outline for a logical theory of adaptive systems Journal of the Association for Computing 9 1962 297 314
9. Ida K., and Oka K. Flexible job-shop scheduling problem by genetic algorithm Electrical Engineering in Japan 177 3 2009 505 511
10. Ishikawa S., Kubota R., and Horio K. Hierarchical optimization using hierarchical multi-competitive genetic algorithm and its application to multiple vehicle routing problem Proceedings of The 11th International Conference on Signal Processing and Multimedia Applications 2014 269 274
11. Ishikawa S., Misawa H., Kubota R., Tokiwa T., Horio K., and Yamakawa T. Multi-space competitive dga for model selection and its application to localization of multiple signal sources Journal of Advanced Computational Intelligence Intelligent Informatics 15(9) 2011 1320 1328
12. Janson S., and Middendorf M. A hierarchical particle swarm optimizer and its adaptive variant IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 35 6 2005 1272 1282
13. de Jong E.D., Thierens D., and Watson R.A. Hierarchical genetic algorithms Parallel Problem Solving from Nature - PPSN VIII 3242 2004 232 241
14. Kennedy J. Particle swarm optimization Encyclopedia of Machine Learning 2010 760 766
15. Kyung-Mi L., Yamakawa T., and Keon-Myun L. A genetic algorithm for general machine scheduling problems International Jounal of Knowledge-Based Electronic 2 1998 60 66
16. Laporte G. The vehicle routing problem: An overview of exact and approximate algorithms European Journal of Operational Research 59 1992 345 358
17. M.Baker B., and Ayechew M. A genetic algorithm for the vehicle routing problem Computers & Operations Research 30 5 2003 787 800
18. Mendes J., Araujo R., Matias T., Seco R., and Belchior C. Automatic extraction of the fuzzy control system by a hierarchical genetic algorithm Engineering Applications of Artificial Intelligence 29 2014 70 78
19. Oliver I., Smith D., and Holland J. Proceedings of the second international conference, lawrence erolaum, new jersy A Study of Permutation Crossover Operator on the TSP 1987 224 230
20. Pezzella F., Morganti G., and Ciaschetti G. A genetic algorithm for the flexible job-shop scheduling problem Computers & Operations Research 35 10 2008 3202 3212
21. Sanchez D., and Melin P. Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure Engineering Applications of Artificial Intelligence 27 2014 41 56
22. Simon H. The science of artifical 1968 MIT Press Cambridge, MA
23. Tanese R. Distributed genetic algorithm Proceedings of the International Conference on Genetic Algorithms 1989 434 439
24. Teekeng W., and Thammano A. Modified genetic algorithm for flexible job-shop scheduling problems Procedia Computer Science 12 2012 122 128
25. Young-Jou L. Hierarchical optimization: a satisfactory solution Fuzzy Sets and Systems 77 1996 321 335
26. Zhang G., Gao L., and Shi Y. An efficient hybridized genetic algorithm architecture for the flexible job-shop scheduling problem Flexible Services and Manufacturing Journal 23 2011 64 85
27. Zhao Y., Zheng S., Wang H., and Yang L. Simultaneous optimization of photostrictive actuator locations, numbers and light intensities for structural shape control using hierarchical genetic algorithm Advances in Engineering Software 88 2015 21 29