Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems

Annals of Operations Research

Volumn 180, Issue 1, 2010, Pages 197-211

  Chang, Pei Chann ^a   Chen, Shih Hsin ^b   Fan, Chin Yuan ^a   Mani, V ^c  

^a YUAN ZE UNIVERSITY   (Taiwan)

^b NANHUA UNIVERSITY   (Taiwan)

^c INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

Artificial chromosomes; Evolutionary algorithm with probabilistic models; Flowshop machine scheduling; Single machine scheduling; Total deviations

Indexed keywords

EID: 77958482967     PISSN: 02545330     EISSN: 15729338     Source Type: Journal    
DOI: 10.1007/s10479-008-0489-9     Document Type: Article

References (52)

1. Abdul-Razaq, T., & Potts, C. (1988). Dynamic programming state-space relaxation for single-machine scheduling. The Journal of the Operational Research Society, 39(2), 141-152.
2. Ackley, D. H. (1987). A connectionist machine for genetic hillclimbing. Dordrecht: Kluwer Academic.
3. Akturk, M. S., & Ozdemir, D. (2001). A new dominance rule to minimize total weighted tardiness with unequal release dates. European Journal of Operational Research, 135(2), 394-412.
4. Alves, M. J., & Almeida, M. (2007). MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem. Computers and Operations Research, 34(11), 3458-3470.
5. Baker, K. R. (1974). Introduction to sequencing and scheduling. New York: Wiley.
6. Baluja, S. (1995). An empirical comparison of seven iterative and evolutionary function optimization heuristics.
7. Baluja, S., & Davies, S. (1998). Fast probabilistic modeling for combinatorial optimization. In Proceedings of the fifteenth national/tenth conference on artificial intelligence/innovative applications of artificial intelligence table of contents (pp. 469-476).
8. Baraglia, R., Hidalgo, J., Perego, R., NUCE I, & CNR P (2001). A hybrid heuristic for the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 5(6), 613-622.
9. Belouadah, H., Posner, M., & Potts, C. (1992). Scheduling with release dates on a single machine to minimize total weighted completion time. Discrete Applied Mathematics, 36(3), 213-231.
10. Birbil, Ş., & Fang, S. C. (2003). An electromagnetism-like mechanism for global optimization. Journal of Global Optimization, 25(3), 263-282.
11. Chang, P. C. (1999). A branch and bound approach for single machine scheduling with earliness and tardiness penalties. Computers and Mathematics with Applications, 37(10), 133-144.
12. Chang, P. C., & Lee, H. C. (1992a). A greedy heuristic for bicriterion single machine scheduling problems. Computers and Industrial Engineering, 22(2), 121-131.
13. Chang, P. C., & Lee, H. C. (1992b). A two phase approach for single machine scheduling: minimizing the total absolute deviation. Journal of the Chinese Institute of Engineers, 15(6), 735-742.
14. Chang, P. C., Chen, S. H., & Fan, C. Y. (2008a). A novel electromagnetism-like algorithm in single machine scheduling problem with distinct due dates. Expert Systems with Applications 39(3).
15. Chang, P. C., Chen, S. H., & Fan, C. Y. (2008b). Mining gene structures to inject artificial chromosomes for genetic algorithm in single machine scheduling problems. Applied Soft Computing Journal, 8(1), 767-777.
16. Chang, P. C., Chen, S. H., & Mani, V. (2009). A hybrid genetic algorithm with dominance properties for single machine scheduling with dependent penalties. Applied Mathematical Modeling, 33(1), 579-596.
17. Corne, D., Dorigo, M., Glover, F., Dasgupta, D., Moscato, P., Poli, R., & Price, K. (1999). New ideas in optimization. New York: McGraw-Hill.
18. Dimopoulos, C., & Zalzala, A. (2000). Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons. IEEE Transactions on Evolutionary Computation, 4(2), 93-113.
19. Framinan, J. M., Gupta, J. N. D., & Leisten, R. (2004). A review and classification of heuristics for permutation flow-shop scheduling with makespan objective. Journal of the Operational Research Society, 55(12), 1243-1255.
20. Glover, F., & Kochenberger, G. (1996). Critical event tabu search for multidimensional knapsack problems. In Meta-heuristics: theory & applications. Dordrecht: Kluwer Academic.
21. Harik, G. (1999). Linkage learning via probabilistic modeling in the ECGA. Urbana, 51, 61, 801.
22. Harik, G., Lobo, F., & Goldberg, D. (1999). The compact genetic algorithm. IEEE Transactions on Evolutionary Computation, 3(4), 287-297.
23. Hejazi, S., & Saghafian, S. (2005). Flowshop-scheduling problems with makespan criterion: a review. International Journal of Production Research, 43(14), 2895-2929.
24. Jouglet, A., Savourey, D., Carlier, J., & Baptiste, P. (2008). Dominance-based heuristics for one-machine total cost scheduling problems. European Journal of Operational Research, 184(3), 879-899.
25. Larrañaga, P., & Lozano, J. A. (2002). Estimation of distribution algorithms: a new tool for evolutionary computation. Dordrecht: Kluwer Academic.
26. Lee, C. Y., Lei, L., & Pinedo, M. (1997). Current trends in deterministic scheduling. Annals of Operations Research, 70, 1-41.
27. Lenstra, J., Kan, A., & Brucker, P. (1975). Complexity of machine scheduling problems. Stud. integer Program, Proc. Workshop Bonn.
28. Li, G. (1997). Single machine earliness and tardiness scheduling. European Journal of Operational Research, 96(3), 546-558.
29. Liaw, C. F. (1999). A branch-and-bound algorithm for the single machine earliness and tardiness scheduling problem. Computers and Operations Research, 26(7), 679-693.
30. Lin, S., & Kernighan, B. (1973). An effective heuristic algorithm for the traveling-salesman problem. Operations Research, 21(2), 498-516.
31. Lozano, J. A. (2006). Towards a new evolutionary computation: advances in the estimation of distribution algorithms. Berlin: Springer.
32. Michalewicz, Z., Dasgupta, D., Le Riche, R., & Schoenauer, M. (1996). Evolutionary algorithms for constrained engineering problems. Computers & Industrial Engineering, 30(4), 851-870.
33. Montgomery, D. C. (2001). Design and analysis of experiments.
34. Muhlenbein, H., & Paaß, G. (1996). From recombination of genes to the estimation of distributions I. Binary parameters. Lecture Notes in Computer Science (Vol. 1141, pp. 178-187). Berlin: Springer.
35. Murata, T., Ishibuchi, H., & Tanaka, H. (1996). Genetic algorithms for flowshop scheduling problems. Computers & Industrial Engineering, 30(4), 1061-1071.
36. Ow, P. S., & Morton, T. E. (1989). The single machine early/tardy problem. Management Science, 35(2), 177-191.
37. Pelikan, M., Goldberg, D. E., & Cantu-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In Proceedings of the genetic and evolutionary computation conference GECCO-99 (Vol. 1, pp. 525-532).
38. Pelikan, M., Goldberg, D. E., & Lobo, F. G. (2002). A survey of optimization by building and using probabilistic models. Computational Optimization and Applications, 21(1), 5-20.
39. Rastegar, R., & Hariri, A. (2006). A step forward in studying the compact genetic algorithm. Evolutionary Computation, 14(3), 277-289.
40. Reeves, C. R. (1995). A genetic algorithm for flowshop sequencing. Computers and Operations Research, 22(1), 5-13.
41. Ruiz, R., & Maroto, C. (2005). A comprehensive review and evaluation of permutation flowshop heuristics. European Journal of Operational Research, 165(2), 479-494.
42. Schoonderwoerd, R., Holland, O. E., Bruten, J. L., & Rothkrantz, L. J. M. (1997). Ant-based load balancing in telecommunications networks. Adaptive Behavior, 5(2), 169.
43. Sim, K. M., & Sun, W. H. (2003). Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Transactions on Systems, Man and Cybernetics, Part A, 33(5), 560-572.
44. Sourd, F., & Kedad-Sidhoum, S. (2003). The one-machine problem with earliness and tardiness penalties. Journal of Scheduling, 6(6), 533-549.
45. Sourd, F., & Kedad-Sidhoum, S. (2007). A faster branch-and-bound algorithm for the earliness-tardiness scheduling problem. Journal of Scheduling (pp. 1-10).
46. Stutzle, T., Hoos, H. H. et al. (2000). MAX-MIN ant system. Future Generation Computer Systems, 16(8), 889-914.
47. Syswerda, G. (1993). Simulated crossover in genetic algorithms. Foundations of Genetic Algorithms, 2, 239-255.
48. Valente, J. M. S., & Alves, R. A. F. S. (2005). Improved heuristics for the early/tardy scheduling problem with no idle time. Computers and Operations Research, 32(3), 557-569.
49. Valente, J. M. S., & Alves, R. A. F. S. (2007). Heuristics for the early/tardy scheduling problem with release dates. International Journal of Production Economics, 106(1), 261-274.
50. Wu, S. D., Storer, R. H., & Chang, P. C. (1993). One-machine rescheduling heuristics with efficiency and stability as criteria. Computers and Operations Research, 20(1), 1-14.
51. Zhang, Q., & Muhlenbein, H. (2004). On the convergence of a class of estimation of distribution algorithms. IEEE Transactions on Evolutionary Computation, 8(2), 127-136.
52. Zhang, Q., Sun, J., & Tsang, E. (2005). An evolutionary algorithm with guided mutation for the maximum clique problem. IEEE Transactions on Evolutionary Computation, 9(2), 192-200.