An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers

Information Sciences

Volumn 181, Issue 3, 2011, Pages 668-685

  Pan, Quan Ke ^a   Wang, Ling ^b   Gao, Liang ^c   Li, W D ^d  

^a LIAOCHENG UNIVERSITY   (China)

^b TSINGHUA UNIVERSITY   (China)

^c HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^d Institute for Clean Growth and Future Mobility   (United Kingdom)

Author keywords

Discrete differential evolution; Flow shop with intermediate buffers; Hybrid algorithm; Local search; Makespan

Indexed keywords

DIFFERENTIAL EVOLUTION; FLOW-SHOPS; HYBRID ALGORITHMS; LOCAL SEARCH; MAKESPAN;

MACHINE SHOP PRACTICE; PARTICLE SWARM OPTIMIZATION (PSO); SCHEDULING ALGORITHMS;

BIOLOGY;

EID: 78449271541     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2010.10.009     Document Type: Article

References (34)

1. P. Brucker, S. Heitmann, and J. Hurink Flow-shops with intermediate buffers OR Spectrum 25 2003 549 574
2. F.C. Chang, H.C. Huang, A refactoring method for cache-efficient swarm intelligence algorithms, Information Sciences, doi:10.1016/j.ins.2010.02.025.
3. L.K. Duclos, and M.S. Spencer The impact of a constraint buffer in a flow shop International Journal of Production Economics 42 1995 175 185
4. J. Grabowski, and J. Pempera Some local search algorithms for no-wait flow-shop problem with makespan criterion Computers and Operations Research 32 2005 2197 2212
5. J. Grabowski, and J. Pempera The permutation flow shop problem with blocking. A tabu search approach OMEGA 35 2007 302 311
6. N.G. Hall, and C. Sriskandarajah A survey of machine scheduling problems with blocking and no-wait in process Operations Research 44 1996 510 525
7. R. Leisten Flow shop sequencing problems with limited buffer storage International Journal of Production Research 28 1990 2085 2100
8. B. Liu, L. Wang, and Y.H. Jin An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers Computers and Operation Research 35 2008 2791 2806
9. F. Liu, Y. Qi, Z. Xia, H. Hao, Discrete differential evolution algorithm for the job shop scheduling problem, Shanghai, China GEC 09, 2009, pp. 879-882.
10. N. Mladenovic, and P. Hansen Variable neighborhood search Computers and Operations Research 24 1997 1097 1100
11. M. Nawaz, E.E.J. Enscore, and I. Ham A heuristic algorithm for the m-machine, n-job flow shop sequencing problem OMEGA 11 1983 91 95
12. B.A. Norman Scheduling flowshops with finite buffers and sequence-dependent setup times Computers and Industrial Engineering 36 1996 163 177
13. E. Nowicki The permutation flow shop with buffers: a tabu search approach European Journal of Operational Research 116 1999 205 219
14. G.C. Onwubolu, and D. Davendra Scheduling flow shops using differential evolution algorithm European Journal of Operational Research 171 2006 674 692
15. I. Osman, and C. Potts Simulated annealing for permutation flow shop scheduling OMEGA 17 1989 551 557
16. Q.K. Pan, P.N. Suganthan, M.F. Tasgetiren, and T.J. Chua A novel artificial bee colony algorithm for a lot-streaming flow shop scheduling problem Information Sciences 2010 10.1016/j.ins.2009.12.025
17. Q.K. Pan, M.F. Tasgetiren, and Y.C. Liang A discrete differential evolution algorithm for the permutation flowshop scheduling problem Computers and Industrial Engineering 55 2008 795 816
18. C.H. Papadimitriou, and P.C. Kanellakis Flow shop scheduling with limited temporary storage Journal of Association Computing Machine 27 1980 533 549
19. M. Pinedo Scheduling: Theory Algorithms and Systems 2002 Prentice-Hall New Jersey
20. B. Naderi, R. Ruiz, and M. Zandieh Algorithms for a realistic variant of flowshop scheduling Computers and Operations Research 37 2010 236 246
21. B. Qian, L. Wang, and R. Hu A hybrid differential evolution for permutation flow-shop scheduling International Journal of Advanced Manufacturing Technology 38 2008 757 777
22. B. Qian, L. Wang, D.X. Huang, W.L. Wang, and X. Wang An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers Computers and Operations Research 36 2009 209 233
23. R. Ruiz, C. Maroto, and J. Alcaraz Two new genetic algorithms for the flowshop scheduling problem OMEGA-The International Journal of Management Science 34 2006 461 476
24. R. Ruiz, and T. Stutzle A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem European Journal of Operational Research 177 2007 2033 2049
25. B. Ruzek, and M. Kvasnicka Differential evolution in the earthquake hypocenter location Pure and Applied Geophysics 158 2001 667 693
26. C. Smutnicki A two-machine permutation flow shop scheduling problem with buffers OR Spectrum 20 1998 29 235
27. R. Storn, and K. Price Differential evolution-A simple and efficient adaptive scheme for global optimization over continuous spaces Journal of Global Optimization 11 1997 341 359
28. R. Storn Designing Digital Filters with Differential Evolution D. Corne, M. Dorigo, F. Glover, New Ideas in Optimization 1999 McGraw-Hill, UK London
29. E. Taillard Benchmarks for basic scheduling problems European Journal of Operational Research 64 1993 278 285
30. E. Vallada, and R. Ruiz Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem OMEGA 38 2010 57 67
31. E. Vallada, R. Ruiz, and G. Minella Minimising total tardiness in the m-machine flowshop problem: a review and evaluation of heuristics and metaheuristics Computers and Operations Research 35 2008 1350 1373
32. H.W. Thornton, and J.L. Hunsucker A new heuristic for minimal makespan in flow shops with multiple processors and no intermediate storage European Journal of Operational Research 152 2004 96 114
33. L. Wang, Q.K. Pan, and P.N. Suganthan A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems Computers and Operations Research 37 2010 509 520
34. L. Wang, L. Zhang, and D.Z. Zheng An effective hybrid genetic algorithm for flow shop scheduling with limited buffers Computers and Operation Research 33 2006 2960 2971