Fuzzy Modeling of Single Machine Scheduling Problems Including the Learning Effect

  • Chapter
Metaheuristics for Production Systems

Part of the book series: Operations Research/Computer Science Interfaces Series ((ORCS,volume 60))

Abstract

In this chapter, we consider the single machine scheduling problem including uncertain parameters and position based learning effect with the aim to minimize the weighted sum of jobs completion times. Due to the ill-known quantities within the model, the determination procedures of optimal solutions in the conventional way is not an affordable task and more elaborated frameworks should be developed. In this context, we introduce two solution approaches for the proposed fuzzy scheduling problem in order to obtain an exact or a satisfactory near optimal solution. The first approach is based on the extension of the well-known Smith’s rule resulting in a polynomial algorithm with a complexity O(n l o g(n)). However, a severe constraint on jobs (fuzzy agreeability concept) should be satisfied in this case. The second approach based on optimization methods is built upon the assumption of unequal fuzzy release dates in addition to the absence of fuzzy agreeability constraint. Three trajectory based metaheuristics (Simulated annealing, taboo search and kangaroo search) are implemented and applied to solve the resulting problem. For the proposed methods throughout the chapter, several numerical experimentations jointly with statistical deductions are provided.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (France)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 85.59
Price includes VAT (France)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 105.49
Price includes VAT (France)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 105.49
Price includes VAT (France)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Pinedo ML (2012) Scheduling theory, algorithms, and systems, 4th edn. Springer, New York

    Google Scholar 

  2. Glover F, Kochenberger GA (2003) Handbook of metaheurstics. Kluwer, Dordrecht

    Google Scholar 

  3. Wright TP (1936) Factors affecting the cost of airplanes. J Aeronaut Sci 3:122–128

    Article  Google Scholar 

  4. Yelle LE (1979) The learning curves: historical review and comprehensive survey. Decision Sci 10:302–328

    Article  Google Scholar 

  5. Woods JR, Saywell RM, Nyhuis AW et al (1992) The learning curve and the cost of heart transplantation. Health Serv Res 27:219–238

    Google Scholar 

  6. Meilijson I, Tamir A (1984) Minimizing flow time on parallel identical processors with variable unit processing time. Oper Res 32:440–448

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Mosheiov G (2001) Scheduling problems with a learning effect. Eur J Oper Res 132:687–692

    Article  Google Scholar 

  9. Bachman A, Janiak A (2004) Scheduling jobs with position-dependent processing times. J Oper Res Soc 55:257–264

    Article  Google Scholar 

  10. Rudek R (2013) A note on proving the strong NP-hardness of a scheduling problem with position dependent job processing times. Opt Lett 7:613–616

    Article  Google Scholar 

  11. Kuo WH, Yang DL (2006) Minimizing the total completion time in a single-machine scheduling problem with a time-dependent learning effect. Eur J Oper Res 174:1184–1190

    Article  Google Scholar 

  12. Wu CC, Lee WC (2008) Single-machine scheduling problems with a learning effect. Appl Math Model 32:1191–1197

    Article  Google Scholar 

  13. Janiak A, Rudek R (2010) A note on a makespan minimization problem with a multi-ability learning effect. Omega-Int J Manage S 38:213–217

    Article  Google Scholar 

  14. Yin Y, Liu M, Hao J et al (2012) Single-machine scheduling with job-position-dependent learning and time-dependent deterioration. IEEE T Syst Man Cybern A 42:192–200

    Article  Google Scholar 

  15. Lai PJ, Lee WC (2013) Single-machine scheduling with learning and forgetting effects. Appl Math Model 37:4509–4516

    Article  Google Scholar 

  16. Eren T (2009) Minimizing the total weighted completion time on a single machine scheduling with release dates and a learning effect. Appl Math Comput 208:355–358

    Article  Google Scholar 

  17. Wu CC, Hsu PH, Chen JC et al (2011) Genetic algorithm for minimizing the total weighted completion time scheduling problem with learning and release times. Comput Oper Res 38:1025–1034

    Article  Google Scholar 

  18. Yin Y, Wu WH, Wu WH et al (2014) A branch-and-bound algorithm for a single machine sequencing to minimize the total tardiness with arbitrary release dates and position-dependent learning effects. Inf Sci 256:91–108

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Schultmann F, Fröhling M, Rentz O (2006) Fuzzy approach for production planning and detailed scheduling in paints manufacturing. Int J Prod Res 44:1589–1612

    Article  Google Scholar 

  21. Prade H (1979) Using fuzzy set theory in a scheduling problem: a case study. Fuzzy Set Syst 2:153–165

    Article  Google Scholar 

  22. Liao LM, Liao CJ (1998) Single machine scheduling problem with fuzzy due date and processing time. J Chin Inst Eng 21:189–196.

    Article  Google Scholar 

  23. Chanas S, Kasperski A (2001) Minimizing maximum lateness in a single machine scheduling problem with fuzzy processing times and fuzzy due dates. Eng Appl Artif Intel 14:377–386

    Article  Google Scholar 

  24. Wang C, Wang D, Ip WH et al (2002) The single machine ready time scheduling problem with fuzzy processing times. Fuzzy Set Syst 127:117–129

    Article  Google Scholar 

  25. Dong Y (2003) One machine fuzzy scheduling to minimize total weighted tardiness, earliness, and recourse cost. Int J Smart Eng Syst Des 5:135–147

    Article  Google Scholar 

  26. Chanas S, Kasperski A (2004) Possible and necessary optimality of solutions in the single machine scheduling problem with fuzzy parameters. Fuzzy Set Syst 142:359–371

    Article  Google Scholar 

  27. Li J, Sun K, Xu D et al (2010) Single machine due date assignment scheduling problem with customer service level in fuzzy environment. Appl Soft Comput 10:894–858

    Google Scholar 

  28. Wu HC (2010) Solving the fuzzy earliness and tardiness in scheduling problems by using genetic algorithms. Expert Syst Appl 37:4860–4866

    Article  Google Scholar 

  29. Kasperski A, Zieliński P (2011) Possibilistic minmax regret sequencing problems with fuzzy parameters. IEEE T Fuzzy Syst 19:1072–1082

    Article  Google Scholar 

  30. Ahmadizar F, Hosseini L (2011) Single-machine scheduling with a position-based learning effect and fuzzy processing times. Int J Adv Manuf Tech 56:693–698

    Article  Google Scholar 

  31. Ahmadizar F, Hosseini L (2013) Minimizing makespan in a single-machine scheduling problem with a learning effect and fuzzy processing times. Int J Adv Manuf Tech 65:581–587

    Article  Google Scholar 

  32. Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic theory and applications. Prentice Hall, New Jersey

    Google Scholar 

  33. Ross TJ (2004) Fuzzy logic with engineering applications. John Wiley, West Sussex

    Google Scholar 

  34. Hanss M (2005) Applied fuzzy arithmetic an introduction with engineering applications. Springer, Berlin

    Google Scholar 

  35. Sivanandam SN, Sumathi S, Deepa SN (2007) Introduction to fuzzy logic using MATLAB. Springer, Berlin

    Book  Google Scholar 

  36. Zadeh LA (1971) Similarity relations and fuzzy orderings. Inf Sci 3:177–200

    Article  Google Scholar 

  37. Matarazzo B, Munda G (2001) New approaches for the comparison of L-R fuzzy numbers: a theoretical and operational analysis. Fuzzy Set Syst 118:407–418

    Article  Google Scholar 

  38. Jain R (1976) Decision-making in the presence of fuzzy variables. IEEE T Syst Man Cybern 6:698–703

    Article  Google Scholar 

  39. Özelkan EC, Duckstein L (1999) Optimal fuzzy counterparts of scheduling rules. Eur J Oper Res 113:593–609

    Article  Google Scholar 

  40. Abbasbandy S, Hajjari T (2009) A new approach for ranking of trapezoidal fuzzy numbers. Comput Math Appl 57:413–419

    Article  Google Scholar 

  41. Yager RR (1981) A procedure for ordering fuzzy subsets of the unit interval. Inf Sci 24:143–161

    Article  Google Scholar 

  42. Lee CY, Lei L, Pinedo M (1997) Current trends in deterministic scheduling. Ann Oper Res 70:1–41

    Article  Google Scholar 

  43. Glover F (1989) Tabu search-Part I. ORSA J Comput 1:190–206

    Article  Google Scholar 

  44. Glover F (1990) Tabu search-Part II. ORSA J Comput 2:4–32

    Article  Google Scholar 

  45. Al-Turki U, Fedjki C, Andijani A (2001) Tabu search for a class of single-machine scheduling problems. Comput Oper Res 28:1223–1230

    Article  Google Scholar 

  46. Salhi S (2002) Defining tabu list size and aspiration criterion within tabu search methods. Comput Oper Res 29:67–86

    Article  Google Scholar 

  47. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680

    Article  Google Scholar 

  48. van Laarhoven PJM, Aarts EHL (1987) Simulated annealing: theory and applications. Springer, Dordrecht

    Book  Google Scholar 

  49. Nourani Y, Andresen B (1989) A comparison of simulated annealing cooling strategies. J Phys A 31:8373–8385

    Article  Google Scholar 

  50. Fleury G (1993) Méthodes stochastiques et deterministes pour les problemes NP-difficiles. Ph.D. dissertation, University of Clermont Ferrand 2

    Google Scholar 

  51. Duta L (2006) Contribution a l’étude de la conduite des systemes de desassemblage. Ph.D. dissertation, University of Franche-Comte

    Google Scholar 

  52. Koç E (2010) The bees algorithm theory, improvements and applications. Ph.D. dissertation, University of Wales

    Google Scholar 

  53. Graham RL, Lawler EL, Lenstra JK et al (1979) Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann Discrete Math 5:287–326

    Article  Google Scholar 

  54. Smith WE (1956) Various optimizers for single stage production. Nav Res Log 3:59–66

    Article  Google Scholar 

  55. Zhao CL, Zhang QL, Tang HY (2004) Machine scheduling problems with learning effects. Dyn Cont Dis Ser A 11:741–750

    Google Scholar 

  56. Lenstra JK, Rinnooy Kan AHG, Brucker P (1977) Complexity of machine scheduling problems. Ann Discrete Math 1:343–362

    Article  Google Scholar 

  57. Wonga BK, Lai VS (2011) A survey of the application of fuzzy set theory in production and operations management: 1998–2009. Int J Prod Econ 129:157–168

    Article  Google Scholar 

  58. Allahverdi A, Al-Anzi F (2006) A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application. Comput Oper Res 33:1056–1080

    Article  Google Scholar 

  59. Lehmann EL, Romano JP (2005) Testing statistical hypotheses. Springer, New York

    Google Scholar 

  60. Gibbons JD, Chakraborti S (2003) Non parametric statistical inference. Marcel Dekker Inference, New York

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Toufik Bentrcia .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Bentrcia, T., Mouss, LH. (2016). Fuzzy Modeling of Single Machine Scheduling Problems Including the Learning Effect. In: Talbi, EG., Yalaoui, F., Amodeo, L. (eds) Metaheuristics for Production Systems. Operations Research/Computer Science Interfaces Series, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-319-23350-5_14

Download citation

Publish with us

Policies and ethics

Navigation