Skip to main content

Advertisement

Log in

Evaluation of optimality in the fuzzy single machine scheduling problem including discounted costs

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

Abstract

The single machine scheduling problem has been often regarded as a simplified representation that contains many polynomial solvable cases. However, in real-world applications, the imprecision of data at the level of each job can be critical for the implementation of scheduling strategies. Therefore, the single machine scheduling problem with the weighted discounted sum of completion times is treated in this paper, where we assume that the processing times, weighting coefficients and discount factor are all described using trapezoidal fuzzy numbers. Our aim in this study is to elaborate adequate measures in the context of possibility theory for the assessment of the optimality of a fixed schedule. Two optimization approaches namely genetic algorithm and pattern search are proposed as computational tools for the validation of the obtained properties and results. The proposed approaches are experimented on the benchmark problem instances and a sensitivity analysis with respect to some configuration parameters is conducted. Modeling and resolution frameworks considered in this research offer promise to deal with optimality in the wide class of fuzzy scheduling problems, which is recognized to be a difficult task by both researchers and practitioners.

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.

Similar content being viewed by others

References

  1. Ahmadizar F, Hosseini L (2011) Single-machine scheduling with a position-based learning effect and fuzzy processing times. Int J Adv Manuf Tech 56(5-8):693–698

    Article  Google Scholar 

  2. 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(1-4):581–587

    Article  Google Scholar 

  3. Altomare C, Guglielmann R, Riboldi M, Bellazzi R, Baroni G (2014) Optimal marker placement in hadrontherapy: intelligent optimization strategies with augmented Lagrangian pattern search. J Biomed Inform. 10.1016/j.jbi.2014.09.001

  4. Artigues C, Demassey S, Nron E (2008) Resource-constrained project scheduling models, algorithms, extensions and applications. Wiley, New Jersey

    Book  Google Scholar 

  5. Baker KR, Trietsch D (2009) Principles of sequencing and scheduling. Wiley, New Jersey

    Book  MATH  Google Scholar 

  6. BłaŻewicz J, Ecker KH, Pesch E, Schmidt G, Weglarz J (2007) Handbook on scheduling from theory to applications. Springer, Berlin

    MATH  Google Scholar 

  7. Cao C, Gu X, Xin Z (2009) Chance constrained programming models for refinery short-term crude oil scheduling problem. Appl Math Model 33(3):1696–1707

    Article  MathSciNet  MATH  Google Scholar 

  8. Castillo O, Melin P (2009) Soft computing models for intelligent control of non-linear dynamical systems. In: Mitkowski W, Kacprzyk J (eds) Modelling dynamics in processes and systems. Springer, Berlin, pp 43–70

  9. 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(3):377–386

    Article  Google Scholar 

  10. Chanas S, Kasperski A (2004) Possible and necessary optimality of solutions in the single machine scheduling problem with fuzzy parameters. Fuzzy Set Syst 142(3):359–371

    Article  MathSciNet  MATH  Google Scholar 

  11. Chryssolouris G (2006) Manufacturing systems: theory and practice, 2nd edn. Springer, New York

    Google Scholar 

  12. Chuang TN (2004) The EDD rule for fuzzy job time. J Inform Optim S 25(1):1–20

    MathSciNet  MATH  Google Scholar 

  13. Coello Coello CA (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Method Appl M 191(11-12):1245–1287

    Article  MathSciNet  MATH  Google Scholar 

  14. Conn AR, Gould N, Toint PL (1997) A globally convergent Lagrangian barrier algorithm for optimization with general inequality constraints and simple bounds. Math Comput 66(217):261–288

    Article  MathSciNet  MATH  Google Scholar 

  15. Costa L, Esprito Santo IACP, Fernandes EMGP (2012) A hybrid genetic pattern search augmented Lagrangian method for constrained global optimization. Appl Math Comput 218(18):9415–9426

    Article  MathSciNet  MATH  Google Scholar 

  16. Dahal KP, Tan KC, Cowling PI (2007) Evolutionary scheduling. Springer, Berlin

    Book  MATH  Google Scholar 

  17. Deb K, Srivastava S (2012) A genetic algorithm based augmented Lagrangian method for constrained optimization. Comput Optim Appl 53(3):869–902

    Article  MathSciNet  MATH  Google Scholar 

  18. Dong Y (2003) One machine fuzzy scheduling to minimize total weighted tardiness, earliness, and recourse cost. Int J Smart Eng Sys Des 5(3):135–147

    Article  Google Scholar 

  19. Dubois D, Prade H (1988) Possibility theory an approach to computerized processing of uncertainty. Plenum Press, New York

    MATH  Google Scholar 

  20. Duenas A, Petrovic D (2008) Multi-objective genetic algorithm for single machine scheduling problem under fuzziness. Fuzzy Optim Decis Ma 7(1):87–104

    Article  MathSciNet  MATH  Google Scholar 

  21. Gawiejnowicz S (2008) Time-dependent scheduling. Springer, Berlin

    MATH  Google Scholar 

  22. Georgescu I (2012) Possibility theory and the risk. Springer, Berlin

    Book  MATH  Google Scholar 

  23. Glover F, Kochenberger GA (2003) Handbook of metaheurustics. Kluwer, Dordrecht

    Google Scholar 

  24. Gupta SK, Kyparisis J (1987) Single machine scheduling research. OMEGA-Int J Manage S15(3):207–227

    Article  Google Scholar 

  25. Han S, Ishii H, Fuji S (1994) One machine scheduling problem with fuzzy due dates. Eur J Oper Res 79(1):1–12

    Article  Google Scholar 

  26. Harikrishnan KK, Ishii H (2005) Single machine batch scheduling problem with resource dependent setup and processing time in the presence of fuzzy due date. Fuzzy Optim Decis Ma 4(2):141–147

    Article  MathSciNet  MATH  Google Scholar 

  27. Jamison KD (1998) Modeling uncertainty using probabilistic based possibility theory with applications to optimization. PhD Dissertation, University of Colorado, Denver

  28. Kasperski A (2005) A possibilistic approach to sequencing problems with fuzzy parameters. Fuzzy Set Syst 150(1):77–86

    Article  MathSciNet  MATH  Google Scholar 

  29. Kasperski A, Ziełiński P (2011) Possibilistic minmax regret sequencing problems with fuzzy parameters. IEEE T Fuzzy Syst 19(6):1072–1082

    Article  Google Scholar 

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

    MATH  Google Scholar 

  31. Kroll A, Schulte H (2014) Benchmark problems for nonlinear system identification and control using soft computing methods: need and overview. Appl Soft Comput 25:496–513

    Article  Google Scholar 

  32. Leung JYT (2004) Handbook of scheduling algorithms, models, and performance analysis. CRC, Florida

    MATH  Google Scholar 

  33. Lewis RM, Torczon (1999) Pattern search algorithms for bound constrained minimization. SIAM J Optimiz 9(4):1082–1099

    Article  MathSciNet  MATH  Google Scholar 

  34. Lewis RM, Torczon V, Trosset MW (2000) Direct search methods: then and now. J Comput Appl Math 124(1-2):191–207

    Article  MathSciNet  MATH  Google Scholar 

  35. Li J, Yuan X, Lee ES, Xu D (2011) Setting due dates to minimize the total weighted possibilistic mean value of the weighted earliness-tardiness costs on a single machine. Comput Math Appl 62(11):4126–4139

    Article  MathSciNet  MATH  Google Scholar 

  36. Liao LM, Liao CJ (1998) Single machine scheduling problem with fuzzy due date and processing time. J Chinese Inst Eng 21(2):189–196

    Article  MathSciNet  Google Scholar 

  37. Liu HC, Yih Y (2013) A fuzzy-based approach to the liquid crystal injection scheduling problem in a TFT-LCD fab. Int J Prod Res 51(20):6163–6181

    Article  Google Scholar 

  38. Lodwick WA, Kacprzyk J (2010) Fuzzy optimization recent advances and applications. Springer, Berlin

    MATH  Google Scholar 

  39. Lopez P, Roubellat F (2001) Ordonnancement de la production. Herms

  40. Mehrabad MS, Pahlavani A (2009) A fuzzy multi-objective programming for scheduling of weighted jobs on a single machine. Int J Adv Manuf Tech 45(1-2):122–139

    Article  Google Scholar 

  41. Nguyen HT (1978) A note on the extension principle for fuzzy sets. J Math Anal Appl 64(2):369–380

    Article  MathSciNet  MATH  Google Scholar 

  42. Nikulin Y, Drexl A (2010) Theoretical aspects of multicriteria flight gate scheduling: deterministic and fuzzy models. J Sched D 13(3):261–280

    Article  MathSciNet  MATH  Google Scholar 

  43. Nocedal J, Wrigh SJ (1999) Numerical optimization. Springer, New York

    Book  Google Scholar 

  44. Olaru D, Smith B (2005) Modelling behavioural rules for daily activity scheduling using fuzzy logic. Transportation 32(4):423–441

    Article  Google Scholar 

  45. Özelkan EC, Duckstein L (1999) Optimal fuzzy counterparts of scheduling rules. Eur J Oper Res 113 (3):593–609

    Article  MATH  Google Scholar 

  46. Petrovic S, Petrovic D, Burke E (2011) Fuzzy logic-based production scheduling and rescheduling in the presence of uncertainty. In: Kempf KG (ed) Planning production and inventories in the extended enterprise. Springer, Berlin, pp 531–562

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

    MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  49. Rahim MA, Khalid HM, Khoukhi A (2012) Nonlinear constrained optimal control problem: a PSO-GA-based discrete augmented Lagrangian approach. Int J Adv Manuf Tech 62(1-4):183–203

    Article  Google Scholar 

  50. Rocha AMAC, Martins TFMC, Fernandes EMGP (2011) An augmented Lagrangian fish swarm based method for global optimization. J Comput Appl Math 235(16):4611–4620

    Article  MathSciNet  MATH  Google Scholar 

  51. Schultmann F, Fröhling M, Rentz O (2006) Fuzzy approach for production planning and detailed scheduling in paints manufacturing. Int J Prod Res 44(8):1589–1612

    Article  MATH  Google Scholar 

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

    Book  MATH  Google Scholar 

  53. Srivastava S, Deb K (2010) A genetic algorithm based augmented Lagrangian method for computationally fast constrained optimization. In: Panigrahi BK (ed) Swarm, evolutionary, and memetic computing. Springer, Berlin, pp 330–337

  54. Stanfield PM, King RE, Joines JA (1996) Scheduling arrivals to a production system in a fuzzy environment. Eur J Oper Res 93(1):75–87

    Article  MATH  Google Scholar 

  55. Talbi EG (2013) Combining metaheuristics with mathematical programming, constraint programming and machine learning. 4OR-Q J Oper Res 11(2):101–150

    Article  MATH  Google Scholar 

  56. Torczon V (1997) On the convergence of pattern search algorithms. SIAM J Optimiz 7(1):1–25

    Article  MathSciNet  MATH  Google Scholar 

  57. Zadeh LA (1965) Fuzzy sets. Inform Control 3(3):338–353

    Article  MathSciNet  MATH  Google Scholar 

  58. Zadeh LA (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Set Syst 1:3–28

    Article  MathSciNet  MATH  Google Scholar 

  59. Zimmermann HJ (1985) Fuzzy sets theory and applications. Kluwer, Dorrecht

    Book  Google Scholar 

  60. Wang C, Wang D, Ip WH, Yuen DW (2002) The single machine ready time scheduling problem with fuzzy processing times. Fuzzy Set Syst 127(2):117–129

    Article  MathSciNet  MATH  Google Scholar 

  61. 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(1):157–168

    Article  Google Scholar 

  62. Wu CC, Lee WC (2005) A single-machine group schedule with fuzzy setup and processing times. J Inform Optim S 26(3):683–691

    MathSciNet  MATH  Google Scholar 

  63. Wu HC (2010) Solving the fuzzy earliness and tardiness in scheduling problems by using genetic algorithms. Expert Syst Appl 37(7):4860–4866

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Toufik Bentrcia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bentrcia, T., Mouss, LH., Mouss, NK. et al. Evaluation of optimality in the fuzzy single machine scheduling problem including discounted costs. Int J Adv Manuf Technol 80, 1369–1385 (2015). https://doi.org/10.1007/s00170-015-7100-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-015-7100-8

Keywords

Navigation