Quantum-inspired genetic algorithms for flow shop scheduling

Studies in Computational Intelligence

Volumn 121, Issue , 2008, Pages 17-56

  Wang, Ling ^a   Li, Bin Bin ^b  

^a TSINGHUA UNIVERSITY   (China)

^b BOSTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 44649152304     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-78532-3_2     Document Type: Article

References (50)

1. Grover LK (1996) A fast quantum mechanical algorithm for database search. In: Proc. 28th ACM Symp. Theory of Computing, Pennsylvania:212-221
2. Shor PW (1994) Algorithms for quantum computation: Discrete logarithms and factoring. In: Proc. 35th Symp. Foundation of Computer Science, Los Alamitos:20-22
3. Wang L (2001) Intelligence optimization with applications. Beijing: Tsinghua Univ. and Springer Press
4. Narayanan A, Moore M (1996) Quantum-inspired genetic algorithm. In: Proc. 1996 IEEE Int. Conf. Evolutionary Computation, Piscataway, NJ: IEEE Press:61-66
5. Han KH, Kim JH (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evol. Comput., vol. 6, no. 6:580-593
6. Han KH, Kim JH (2004) A quantum-inspired evolutionary algorithm with a new termination criterion, gate, and two-phase scheme. IEEE Trans. Evol. Comput., vol. 8, no.2:156-169
7. Han KH, Kim JH (2000) Genetic quantum algorithm and its application to combinatorial optimization problem. In: Proc.2000 Congr. Evolutionary Computation, Piscataway, NJ: IEEE Press, vol. 2:1354-1360
8. Wang L (2003) Shop scheduling with genetic algorithms. Beijing: Tsinghua Univ. and Springer Press
9. Garey MR, Johonson DS (1979) Computers and intractability: A guide to the theory of NP-completeness. San Francisco: Freeman Press
10. Nawaz M, Enscore EJ, Ham I (1983) A heuristic algorithm for the m-machine, n-job flowshop sequencing problem. Omega, vol. 11:91-95
11. Ogbu FA, Smith DK (1990) Simulated annealing for the permutation flowshop problem. Omega, vol. 19:64-67
12. Wang L, Zhang L, Zheng DZ (2003) A class of order-based genetic algorithm for flow shop scheduling. Int. J. Adv. Manuf. Technol., vol. 22:828-835
13. Nowicki E, Smutnick C (1996) A fast tabu search algorithm for the permutation flow-shop problem. Eur. J. Oper. Res., vol. 91:160-176
14. Wang L, Zheng DZ (2003) An effective hybrid heuristic for flow shop scheduling. Int. J. Adv. Manuf. Technol., vol. 21:38-44
15. T'kindt V, Billaut JC (2002) Multicriteria Scheduling: Theory, Models and Algorithms. Berlin: Springer Press.
16. Silva JDL, Burke EK, Petrovic S (2004) An introduction to multiobjective metaheuristics for scheduling and timetabling. Lecture Notes in Economics and Mathematical Systems, vol. 535:91-129
17. Jaszkiewicz A (2002) Genetic local search for multi-objective combinatorial optimization. Eur. J. Oper. Res., vol. 137, no.1:50-71
18. Murata T, Ishibuchi H, Tanaka H (1996) Multiobjective genetic algorithm and its applications to flowshop scheduling. Comput. Ind. Eng., vol. 30, no. 4:957-968
19. Ishibuchi H, Murata T (1998) A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Trans. Syst. Man Cy. C.: Application and Reviews, vol. 28, no. 3:392-403
20. Murata T, Ishibuchi H, Gen M (2000) Cellular genetic local search for multi-objective optimization. In: Proc. 2000 Genetic and Evolutionary Computation Conf., Las Vegas, NV:307-314
21. Murata T, Ishibuchi H, Gen M (2001) Specification of genetic search directions in cellular multiobjective genetic algorithms. Lecture Notes in Computer Science:pP.82-95
22. Ishibuchi H, Yoshida T, Murata T (2002) Selection of initial solutions for local search in multiobjective genetic local search. In: Proc. 2002 on Congress Evolutionary Computation, Honolulu, HI:950-955
23. Ishibuchi H, Yoshida T, Murata T (2003) Balanced between genetic search and local search in memtic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans. Evol. Comput., vol. 7, no. 2
24. Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Proc. 1st Int. Conf. on Genetic Algorithms., Pittsburgh, PA:93-100
25. Arroyo JEC, Armentano VA (2005) Genetic local search for multi-objective flowshop scheduling problems. Eur. J. Oper. Res., vol. 167:717-738
26. Talbi H, Draa A, Batouche M (2004) A new quantum-inspired genetic algorithm for solving the traveling salesman problem. IEEE Int. Conf. Ind. Technol.:1192-1197
27. Wang L, Tang F, Wu H (2005) Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation. Appl. Math. Comput., vol. 171, no. 2:1141-1156
28. Bean JC (1994) Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing, vol. 6:154-160
29. Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. MA: Addison-Wesley.
30. Srinivas N, Deb K (1995) Multi-objective optimization function optimization using non-dominated sorting genetic algorithms. Evol. Comput., vol. 2, no. 3:221-448
31. Deb K, Pratap A, Agarwal A, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput., vol. 6, no. 2:182-197
32. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans. Evol. Comput., vol. 1:67-82
33. Veldhuizen DAV (1999) Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. Ph.D. dissertation, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, Ohio
34. Zitzler E (1999) Evolutionary algorithms for multiobjective optimization: Methods and applications. Ph.D. dissertation, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
35. Czyak P, Jaskiewicz A (1998) Pareto simulated annealing - a metaheuristic technique for multiple objective combinatorial optimization. Journal of Multi-Criteria Decision Making, vol. 7:34-47
36. Ulungu EL, Teghem J, Ost C (1998) Efficiency of interactive multi-objective simulated annealing through a case study. J. Oper. Res. Soc., vol. 49:1044-1050
37. Tan KC, Goh CK, Yang YJ, Lee TH (2006) Evolving better population distribution and exploration in evolutionary multi-objective optimization. Eur. J. Oper. Res., vol. 171:463-495
38. Jaszkiewicz A (2003) Do multiple-objective metaheuristcs deliver on their promises? A computational experiment on the set-covering problem. IEEE Trans. Evol. Comput., vol. 7, no. 2:133-143
39. Carlier J (1978) Ordonnancements a contraintes disjonctives. R.A.I.R.O. Recherche operationelle/Oper. Res., vol. 12:333-351
40. Reeves CR (1995) A genetic algorithm for flowshop sequencing. Comput. Oper. Res., vol. 22:5-13
41. Nelson RT, Sarin RK, Daniels RL (1986) Scheduling with multiple performances measures: The one machine case Manage. Sci., vol. 2:464-479
42. Shanthikumar JG (1983) Scheduling n jobs on one machine to minimize the maximum tardiness with minimum number tardy jobs. Comput. Oper. Res., vol. 10, no. 3:255-266
43. Selen WJ, Hott D (1986) A mixed integer goal programming formulation of the standard flow shop scheduling problem. J. Oper. Res. Soc., vol. 37, no. 12:1121-1126
44. Ho JC, Chang YL (1991) A new heuristic for n-job, m-shop problem. Eur. J. Oper. Res., vol. 52:194-202
45. Rajendran C (1992) Two-stage flowshop scheduling problem with bicriteria. J. Oper. Res. Soc., vol. 43, no. 9:871-884
46. Liao CJ, Yu WC, Joe CB (1997) Bicriteria scheduling in the two machines flow shop. J. Oper. Res. Soc., vol. 48:929-935
47. Loukil T, Teghem J, Tuyttens D (2005) Solving multi-objective production scheduling problems using metaheuristics. Eur. J. Oper. Res., vol. 161:42-61
48. Armento VA, Ronconi DP (1999) Tabu search for total tardiness minimization in flowshop scheduling problems. Comput. Oper. Res., vol. 26:219-235
49. Potts CN, Wassenhove LNV (1982) A decomposition algorithm for the single machine total tardiness problem. Oper. Res. Lett., vol. 1:177-181
50. Taillard E (1993) Benchmarks for basic scheduling problems. Eur. J. Oper. Res., vol. 64:278-285