Abstract
In so many combinatorial optimization problems, job shop scheduling problems have earned a reputation for being difficult to solve. Genetic algorithm has demonstrated considerable success in providing efficient solutions to many nonpolynomial-hard optimization problems. In the field of job shop scheduling, genetic algorithm has been intensively researched, and nine methods were proposed to encode a chromosome to represent a solution. In this paper, we proposed a novel genetic chromosome-encoding approach; in this encoding method, the operation of crossover and mutation was done in three-dimensional coded space. Some big benchmark problems were tried with the proposed three-dimensional encoding genetic algorithm for validation and the results are encouraging.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Mattfeld DC (1996) Evolutionary search and the job shop: investigations on genetic algorithm for production scheduling. Springer, Heidelberg, pp 52–60
Baker KR (1974) Introduction to sequencing and scheduling. Wiley, New York
Franch S (1981) Sequencing and scheduling: an introduction to the mathematics of the job-shop. Wiley, New York
Nakano R, Yamada T (1991) Conventional genetic algorithms for job-shop problems. In: Belew RK, Booker LB (eds) Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, pp 477–479
Yamada T, Nakano R (1992) A genetic algorithm applicable to large-scale job shop problems. In: Manner R, Manderick B (eds) Parallel problem solving from nature: PPSN II. Elsevier Science, Amsterdam, pp 281–290
Fang H, Ross P, Corne D (1993) A promising genetic algorithm approach to job shop scheduling, rescheduling and open shop scheduling. In: Forrest S (ed) Proceedings of the fifth international conference on genetic algorithms. Morgan Kaufmann, San Mateo, pp 375–382
Gen M, Tsujimura J, Kubota E (1994) Solving job-shop scheduling problem using genetic algorithms. In: Gen M, Kobayashi S (eds) Proceedings of the 16th international conference on computers and industrial engineering. Ashikaga, Japan, pp 576–579
Portmann MC (1997) Scheduling methodologies: optimization and compusearch approaches. In: Artiba A, Elmagharaby SE (ed) The planning and scheduling of production systems, Chap.9. pp. 271–300
Ponnambalam SG, Jawahar N, Kumar BS (2003) Estimation of optimum genetic control parameters for job shop scheduling. Int J Adv Manuf Technol 19(3):224–234
Orvosh D, Davis L (1994) Using a genetic algorithm to optimize problems with feasibility constraints. Proc. of the First IEEE Conf. on Evolutionary Computation. IEEE, New York, pp 548–552
Cheng RW, Gen M, Tsujimura Y (1996) A tutorial survey of job-shop scheduling problems using genetic algorithms-I: representation. Comput Ind Eng 30(4):986–995
Amirthagadeswaran KS, Arunachalam VP (2007) Enhancement of performance of genetic algorithm for job shop scheduling problems through inversion operator. Int J Adv Manuf Technol 32(7-8):780–786. doi:10.1007/s00170-005-0392-3
De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. Dissertation, University of Michigan, pp. 76–81
Grefenstette JJ (1999) Optimization of control parameters for genetic algorithms. IEEE Trans Syst Man Cybern 16(1):122–128. doi:10.1109/TSMC.1986.289288
Wang L, Zheng DZ (2003) An effective hybrid heuristic for flow shop scheduling. Int J Adv Manuf Technol 21(1):38–44. doi:10.1007/s001700300005
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading
Kubota A (1995) Study on optimal scheduling for manufacturing system by genetic algorithms. Master’s thesis, Ashikaga Institute of Technology, Ashikaga, Japan March, 1995
Arafeh M (2001) A genetic algorithm for minimizing the total weighted tardiness in a job shop. Doctor Dissertation, Cleveland state university May, 2001
Pinedo M (2001) Scheduling: theory, algorithms and systems, 2nd edn. Prentice Hall, Englewood Cliffs
Storer RH, Wu SD, Vaccari R (1992) New search spaces for sequencing problems with applications to job-shop scheduling. Manage Sci 38(10):1495–1509. doi:10.1287/mnsc.38.10.1495
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, Y.M., Yin, H.L. & Wang, J. Genetic algorithm with new encoding scheme for job shop scheduling. Int J Adv Manuf Technol 44, 977–984 (2009). https://doi.org/10.1007/s00170-008-1898-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-008-1898-2