Skip to main content

Advertisement

Log in

Scheduling jobs with truncated exponential learning functions

  • Original Paper
  • Published:
Optimization Letters Aims and scope Submit manuscript

Abstract

In this paper we consider the single machine scheduling problem with truncated exponential learning functions. By the truncated exponential learning functions, we mean that the actual job processing time is a function which depends not only on the total normal processing times of the jobs already processed but also on a control parameter. The use of the truncated function is to model the phenomenon that the learning of a human activity is limited. We show that even with the introduction of the proposed model to job processing times, several single machine problems remain polynomially solvable. For the following three objective functions, the total weighted completion time, the discounted total weighted completion time, the maximum lateness, we present heuristic algorithms according to the corresponding problems without truncated exponential learning functions. We also analyse the worst-case bound of our heuristic algorithms.

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

Access this article

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. Badiru A.B.: Computational survey of univariate and multivariate learning curve models. IEEE Trans. Eng. Manag. 39, 176–188 (1992)

    Article  Google Scholar 

  2. Biskup D.: Single-machine scheduling with learning considerations. Eur. J. Oper. Res. 115, 173–178 (1999)

    Article  MATH  Google Scholar 

  3. Biskup D.: A state-of-the-art review on scheduling with learning effects. Eur. J. Oper. Res. 188, 315–329 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  4. Cheng T.C.E., Cheng S.-R., Wu W.-H., Hsu P.-H., Wu C.-C.: A two-agent single-machine scheduling problem with truncated sum-of-processing-times-based learning considerations. Comput. Industr. Eng. 60, 534–541 (2011)

    Article  Google Scholar 

  5. Cheng T.C.E., Lai P.-J., Wu C.-C., Lee W.-C.: Single-machine scheduling with sum-of-logarithm-processing-times-based learning considerations. Inform. Sci. 179, 3127–3135 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  6. Cheng T.C.E., Wang G.: Single machine scheduling with learning effect considerations. Ann. Oper. Res. 98, 273–290 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  7. Cheng T.C.E., Wu C.-C., Lee W.-C.: Some scheduling problems with deteriorating jobs and learning effects. Comput. Industr. Eng. 54, 972–982 (2008)

    Article  Google Scholar 

  8. Cheng T.C.E., Wu C.-C., Lee W.-C.: Some scheduling problems with sum-of-processing-times-based and job-position-based learning effects. Inform. Sci. 178, 2476–2487 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  9. Floudas C.A., Pardalos P.M.: Encyclopedia of Optimization, 2nd edn. Springer, Berlin (2009)

    Book  MATH  Google Scholar 

  10. Graham R.L., Lawler E.L., Lenstra J.K., Rinnooy Kan A.H.G.: Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann. Discret. Math. 5, 287–326 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  11. Hadda H.: A (\({\frac{4}{3}}\))-approximation algorithm for a special case of the two machine flow shop problem with several availability constraints. Optim. Lett. 3, 583–592 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  12. Lai P.J., Lee W.C.: Single-machine scheduling with general sum-of-processing-time-based and position-based learning effects. Omega Int. J. Manag. Sci. 39, 467–471 (2011)

    Article  MathSciNet  Google Scholar 

  13. Lee W.C.: Scheduling with general position-based learning curves. Inform. Sci. 181, 5515–5522 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  14. Lee W.-C., Wu C.-C.: Some single-machine and m-machine flowshop scheduling problems with learning considerations. Inform. Sci. 179, 3885–3892 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  15. Lee W.-C., Wu C.-C.: A note on single-machine group scheduling problems with position-based learning effect. Appl. Math. Model. 33, 2159–2163 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  16. Lee C.-Y., Yu G.: Parallel-machine scheduling under potential disruption. Optim. Lett. 2, 27–37 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  17. Mosheiov G.: Minimizing total absolute deviation of job completion times: extensions to position-dependent processing times and parallel identical machines. J. Oper. Res. Soc. 59, 1422–1424 (2008)

    Article  MATH  Google Scholar 

  18. Pardalos P.M., Resende M.G.C.: Handbook of Applied Optimization. Oxford Univ Press, Oxford (2002)

    Book  MATH  Google Scholar 

  19. Pardalos P.M.: Complexity in Numerical Optimization. World Scientific, Singapore (1993)

    Book  MATH  Google Scholar 

  20. Setamaa-Karkkainen A., Miettinen K., Vuori J.: Heuristic for a new multiobjective scheduling problem. Optim. Lett. 1, 213–225 (2007)

    Article  MathSciNet  Google Scholar 

  21. Pinedo M.: Scheduling: Theory, Algorithms, and Systems. Prentice-Hall, Upper Saddle River (2002)

    Google Scholar 

  22. Smith W.E.: Various optimizers for single state production. Naval Res. Log. Quart. 3, 59–66 (1956)

    Article  Google Scholar 

  23. Townsend W.: The single machine problem with quadratic penalty function of completion times: a branch-and-bound solution. Manag. Sci. 24, 530–534 (1978)

    Article  MATH  Google Scholar 

  24. Wang J.-B., Li J.-X.: Single machine past-sequence-dependent setup times scheduling with general position-dependent and time-dependent learning effects. Appl. Math. Model. 35, 1388–1395 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  25. Wang J.-B., Sun L.-H., Sun L.-Y.: Single machine scheduling with a learning effect and discounted costs. Int. J. Adv. Manufact. Technol. 49, 1141–1149 (2010)

    Article  Google Scholar 

  26. Wang J.-B., Wang M.-Z.: Single machine multiple common due dates scheduling with general job-dependent learning curves. Comput. Math. Appl. 60, 2998–3002 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  27. Wang J.-B., Wang M.-Z.: A revision of machine scheduling problems with a general learning effect. Math. Comput. Model. 53, 330–336 (2011)

    Article  MATH  Google Scholar 

  28. Wang J.-B., Wang M.-Z.: Worst-case behavior of simple sequencing rules in flow shop scheduling with general position-dependent learning effects. Ann. Oper. Res. 191, 155–169 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  29. Wang, J.-B., Wang, M.-Z.: Single-machine scheduling with nonlinear deterioration. Optim. Lett. doi:10.1007/s11590-010-0253-3

  30. Wang, J.-B., Ji, P., Cheng, T.C.E., Wang, D.: Minimizing makespan in a two-machine flow shop with effects of deterioration and learning. Optim. Lett. doi:10.1007/s11590-011-0334-y

  31. Wang J.-J., Wang J.-B., Wang X.-P.: Scheduling problems with exponential learning functions. Int. J. Innovat. Comput. Inform. Control 6, 3265–3274 (2010)

    Google Scholar 

  32. Wang J.-B., Wang D., Wang L.-Y., Lin L., Yin N., Wang W.-W.: Single machine scheduling with exponential time-dependent learning effect and past-sequence-dependent setup times. Comput. Math. Appl. 57, 9–16 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  33. Wright T.P.: Factors affecting the cost of airplanes. J. Aeronaut. Sci. 3, 122–128 (1936)

    Article  Google Scholar 

  34. Wu C.-C., Lee W.-C.: Single-machine and flowshop scheduling with a general learning effect model. Comput. Industr. Eng. 56, 1553–1558 (2009)

    Article  Google Scholar 

  35. Wu C.-C., Yin Y., Cheng S.-R.: Some single-machine scheduling problems with a truncation learning effect. Comput. Industr. Eng. 60, 790–795 (2011)

    Article  Google Scholar 

  36. Yang D.-L., Kuo W.-H.: Single-machine scheduling with both deterioration and learning effects. Ann. Oper. Res. 172, 315–327 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  37. Yang D.-L., Kuo W.-H.: Some scheduling problems with deteriorating jobs and learning effects. Comput. Industr. Eng. 58, 25–28 (2010)

    Article  Google Scholar 

  38. Yin Y., Xu D., Sun K., Li H.: Some scheduling problems with general position-dependent and time-dependent learning effects. Inform. Sci. 179, 2416–2425 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  39. Zhao C.-L., Tang H.-Y.: A note on two-machine no-wait flow shop scheduling with deteriorating jobs and machine availability constraints. Optim. Lett. 5, 183–190 (2011)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ji-Bo Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, JB., Wang, XY., Sun, LH. et al. Scheduling jobs with truncated exponential learning functions. Optim Lett 7, 1857–1873 (2013). https://doi.org/10.1007/s11590-011-0433-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11590-011-0433-9

Keywords

Navigation