Skip to main content
SpringerLink
Log in

Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The paper considers the problems of scheduling n jobs that are released over time on a machine in order to optimize one or more objectives. The problems are dynamic single-machine scheduling problems (DSMSPs) with job release dates and needed to be solved urgently because they exist widely in practical production environment. Gene expression programming-based scheduling rules constructor (GEPSRC) was proposed to construct effective scheduling rules (SRs) for DSMSPs with job release dates automatically. In GEPSRC, Gene Expression Programming (GEP) worked as a heuristic search to search the space of SRs. Many experiments were conducted, and comparisons were made between GEPSRC and some previous methods. The results showed that GEPSRC achieved significant improvement.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Jakobovic D, Budin L (2006) Dynamic scheduling with genetic programming. Lect Notes Comput Sci 3905:73–84

    Article  Google Scholar 

  2. Holthaus O, Rajendran C (1997) New dispatching rules for scheduling in a job shop - an experimental study. Int J Adv Manuf Technol 13(2):148–153

    Article  Google Scholar 

  3. Kianfar K, Ghomi SMT, Karimi B (2009) New dispatching rules to minimize rejection and tardiness costs in a dynamic flexible flow shop. Int J Adv Manuf Technol 45(7–8):759–771

    Article  Google Scholar 

  4. Wang JB, Wang LY, Wang D, Huang X, Wang XR (2009) A note on single-machine total completion time problem with general deteriorating function. Int J Adv Manuf Technol 44(11–12):1213–1218

    Article  Google Scholar 

  5. Ham M, Fowler JW (2008) Scheduling of wet etch and furnace operations with next arrival control heuristic. Int J Adv Manuf Technol 38(9–10):1006–1017

    Article  Google Scholar 

  6. Norman B, Bean J (1999) A genetic algorithm methodology for complex scheduling problems. Nav Res Logist 46:199–211

    Article  MATH  MathSciNet  Google Scholar 

  7. Panneerselvam R (2006) Simple heuristic to minimize total tardiness in a single machine scheduling problem. Int J Adv Manuf Technol 30(7–8):722–726

    Article  Google Scholar 

  8. Haupt R (1989) A survey of priority rule-based scheduling. OR Spektrum 11:3–16

    Article  MATH  MathSciNet  Google Scholar 

  9. Catoni O (1998) Solving scheduling problems by simulated annealing. SIAM J Control Optim 36(5):1639–1675

    Article  MathSciNet  Google Scholar 

  10. Finke DA, Medeiros DJ, Traband MT (2002) Shop scheduling using Tabu search and simulation. In: Yucesan E, Chen CH, Snowdon JL, Charnes JM (eds) Proceedings of the 2002 winter simulation conference, 8–11 December 2002 San Diego. WSC, New York, pp 1013–1017

    Google Scholar 

  11. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison–Wesley, Boston

    MATH  Google Scholar 

  12. Aytug H, Lawley MA, Mckay K, Mohan S, Uzsoy R (2005) Executing production schedules in the face of uncertainties: a review and some future directions. Eur J Oper Res 161(1):86–110

    Article  MATH  MathSciNet  Google Scholar 

  13. Graham RL (1966) Bounds for certain multiprocessor anomalies. Bell Syst Tech J 45:1563–1581

    Google Scholar 

  14. Smith WE (1956) Various optimizers for single-stage production. Nav Res Logist Q 3:59–66

    Article  Google Scholar 

  15. Baker KR (1974) Introduction to sequencing and scheduling. Wiley, New York

    Google Scholar 

  16. Hoogeveen JA, Vestjens APA (1996) Optimal on-line algorithms for single-machine scheduling. Lect Notes Comput Sci 1084:404–414

    MathSciNet  Google Scholar 

  17. Phillips C, Stein C, Wein J (1995) Minimizing average completion time in the presence of release dates. Math Program 82:199–223

    MathSciNet  Google Scholar 

  18. Kellerer H, Tautenhahn T, Woeginger GJ (1999) Approximability and nonapproximability results for minimizing total flow time on a single machine. SIAM J Comput 28(4):1155–1166

    Article  MATH  MathSciNet  Google Scholar 

  19. Guo Y, Lim A, Rodriguesc B, Yu S (2004) Minimizing total flow time in single machine environment with release time: an experimental analysis. Comput Ind Eng 47:123–140

    Article  Google Scholar 

  20. Sgall J (1998) On-line scheduling. Lect Notes Comput Sci 442:196–231

    Article  MathSciNet  Google Scholar 

  21. Pruhs K, Sgall J, Torng E (2004) Online scheduling. In: Leung JYT (ed) Handbook of scheduling: algorithms, models and performance analysis. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

  22. Blackstone JH, Phillips DT, Hogg GL (1982) A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int J Prod Res 20(1):27–45

    Article  Google Scholar 

  23. Jeong KC, Kim YD (1998) A real-time scheduling mechanism for a flexible manufacturing system using simulation and dispatching rules. Int J Prod Res 36(9):2609–2626

    Article  MATH  Google Scholar 

  24. Yin YL, Rau H (2006) Dynamic selection of sequencing rules for a class-based unit-load automated storage and retrieval system. Int J Adv Manuf Technol 29(11–12):1259–1266

    Article  Google Scholar 

  25. Chen CC, Yih Y (1996) Identifying attributes for knowledge-base development in dynamic scheduling environments. Int J Prod Res 34(6):1739–1755

    Article  MATH  Google Scholar 

  26. El-Bouri A, Shah P (2006) A neural network for dispatching rule selection in a job shop. Int J Adv Manuf Technol 31(3–4):342–349

    Article  Google Scholar 

  27. Aytug H, Koehler GJ, Snowdon JL (1994) Genetic learning of dynamic scheduling within a simulation environment. Comput Oper Res 21(8):909–925

    Article  MATH  Google Scholar 

  28. Trappey AJC, Lin GYP, Ku CC, Ho PS (2007) Design and analysis of a rule-based knowledge system supporting intelligent dispatching and its application in the TFT-LCD industry. Int J Adv Manuf Technol 35(3–4):385–393

    Article  Google Scholar 

  29. Singh A, Mehta NK, Jain PK (2007) Multicriteria dynamic scheduling by swapping of dispatching rules. Int J Adv Manuf Technol 34(9–10):988–1007

    Article  Google Scholar 

  30. Yang HB, Yan HS (2009) An adaptive approach to dynamic scheduling in knowledgeable manufacturing cell. Int J Adv Manuf Technol 42(3–4):312–320

    Article  Google Scholar 

  31. Geiger CD, Uzsoy R, Aytug H (2006) Rapid modeling and discovery of priority dispatching rules: an autonomous learning approach. J Sched 9:7–34

    Article  MATH  Google Scholar 

  32. Koza JR (2007) Introduction to genetic programming. In: Lipson H (ed) Proceedings of GECCO 2007: genetic and evolutionary computation conference, 7-11 July 2007 London. ACM, London, pp 3323–3365

    Chapter  Google Scholar 

  33. Dimopoulos C, Zalzala AMS (2001) Investigating the use of genetic programming for a classic one-machine scheduling problem. Adv Eng Softw 32(6):489–498

    Article  MATH  Google Scholar 

  34. Yin WJ, Liu M, Wu C (2003) Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming. In: Sarker R et al (eds) Proceeding of CEC2003: congress on evolutionary computation, 9-12 December 2003 Canberra, Australia. IEEE, Piscataway, pp 1050–1055

    Google Scholar 

  35. Atlan L, Bonnet J, Naillon M (1994) Learning distributed reactive strategies by genetic programming for the general job shop problem. In: Dankel D, Stewan J (eds) Proceedings of The 7th annual Florida artificial intelligence research symposium, 5-6 May 1994 Pensacola Beach, Florida, USA. IEEE, Piscataway

    Google Scholar 

  36. Miyashita K (2000) Job-shop scheduling with genetic programming. In: Whitley LD, Goldberg DE et al (eds) Proceedings of Genetic and Evolutionary Computation Conference (GECCO-2000), 8-12 July 2000 Las Vegas, Nevada, USA. Kaufmann, San Francisco, pp 505–512

    Google Scholar 

  37. Ho NB, Tay JC (2003) Evolving dispatching rules for solving the flexible job shop problem. In: Corne D (ed) Proceedings of the 2005 IEEE congress on evolutionary computation, 2–4 September 2005 Edinburgh, UK. IEEE, Piscataway, pp 2848–2855

    Google Scholar 

  38. Tay JC, Ho NB (2007) Designing dispatching rules to minimize total tardiness. Stud Comput Intel 49:101–124

    Article  Google Scholar 

  39. Tay JC, Ho NB (2008) Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problem. Comput Ind Eng 54(3):453–473

    Article  Google Scholar 

  40. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    MATH  Google Scholar 

  41. Hardy Y, Steeb WH (2002) Gene expression programming and one-dimensional chaotic maps. Int J Mod Phys C 13(1):13–24

    Article  MATH  MathSciNet  Google Scholar 

  42. Jayamohan MS, Rajendran C (2000) New dispatching rules for shop scheduling: a step forward. Int J Prod Res 38:563–586

    Article  MATH  Google Scholar 

  43. Montagne ER (1969) Sequencing with time delay costs. Industrial Engineering Research Bulletin, Arizona State University 5

  44. Oliver H, Chandrasekharan R (1997) Efficient dispatching rules for scheduling in a job shop. Int J Prod Econ 48(1):87–105

    Article  Google Scholar 

  45. Kanet JJ, Li XM (2004) A weighted modified due date rule for sequencing to minimize weighted tardiness. J Sched 7(4):261–276

    Article  MATH  MathSciNet  Google Scholar 

  46. Bhaskaran K, Pinedo M (1992) Dispatching. In: Salvendy G (ed) Handbook of industrial engineering. Wiley, New York, pp 2184–2198

    Google Scholar 

  47. Vepsalainen APJ, Morton TE (1987) Priority rules for job shops with weighted tardiness costs. Manage Sci 33:1035–1047

    Article  Google Scholar 

  48. Cramer NL (1985) A representation for the adaptive generation of simple sequential programs. In: Grefenstette JJ (ed) Proceedings of The First International Conference on Genetic Algorithms and their Applications, 24-26 July 1985 Pittsburgh, USA. Erlbaum, Hillsdale, pp 183–187

    Google Scholar 

  49. Koza JR (1994) Genetic programming II: automatic discovery of reusable programs. MIT, Cambridge

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, People’s Republic of China

    Li Nie, Xinyu Shao & Liang Gao

  2. Faculty of Engineering and Computing, Coventry University, Coventry, UK

    Weidong Li

Authors
  1. Li Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinyu Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weidong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liang Gao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nie, L., Shao, X., Gao, L. et al. Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems. Int J Adv Manuf Technol 50, 729–747 (2010). https://doi.org/10.1007/s00170-010-2518-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-010-2518-5

Keywords

Search

Navigation