Skip to main content
SpringerLink
Log in

A shifting bottleneck procedure with multiple objectives in a complex manufacturing environment

  • Production Management
  • Published:
Production Engineering Aims and scope Submit manuscript

Abstract

Scheduling problems have been studied extensively over the years. Broad ranges of focus areas exist, from optimization to heuristics, production planning to production sequencing, customized algorithms to general-purpose algorithms, and from simple machines to complex environments. In this research, a heuristic approach has been proposed to overcome the scheduling problem on a complex job shop found at a manufacturer of commercial building products. The research is aimed at sequencing production orders in near-real-time, primarily to minimize total tardiness, but also to reduce total setup time. A layered Shifting Bottleneck Procedure is employed, with the top layer determining release dates and due dates for individual jobs, and the bottom layer applying algorithms to individual work centers. The outcome of this research is a better production schedule than current methods with minimal computation cost. The proposed framework performs well and could be applied to other production areas.

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

Fig. 1

Similar content being viewed by others

References

  1. Adams J, Balas E, Zawack D (1988) The shifting bottleneck procedure for job shop scheduling. Manage Sci 34:391–401

    MathSciNet  MATH  Google Scholar 

  2. Askin RG, Standridge CR (1993) Modeling and analysis of manufacturing systems. Wiley, New York

    MATH  Google Scholar 

  3. Balas E, Lancia G, Serafini P, Vazacopoulos A (1998) Job shop scheduling with deadlines. J Comb Optim 1:329–353

    MathSciNet  MATH  Google Scholar 

  4. Balas E, Simonetti N (2001) Linear time dynamic-programming algorithms for new classes of restricted TSPs: a computational study. INFORMS J Comput 13(1):56–75

    MathSciNet  MATH  Google Scholar 

  5. Balas E, Simonetti N, Vazacopoulos A (2008) Job shop scheduling with setup times, deadlines, and precedence constraints. J Sched 11:253–262

    MathSciNet  MATH  Google Scholar 

  6. Balas E, Toth P (1983) Branch and bound methods for the traveling salesman problem. Management Science Research Report, No MSRR 488. Carnegie-Mellon University, Pittsburgh

  7. Balas E, Vazacopoulos A (1998) Guided local search with shifting bottleneck for job shop scheduling. Manag Sci 44:262–275

    MATH  Google Scholar 

  8. Bean JC (1994) Genetic algorithms and random keys for sequencing and optimization. ORSA J Comput 6(2):154–160

    MATH  Google Scholar 

  9. Blackstone J (2013) APICS dictionary, Fourteenth edn. APICS, Chicago

    Google Scholar 

  10. Carpeneto G, Toth P (1980) Some new branching and bounding criteria for the asymmetric travelling salesman problem. Manag Sci 26(7):735–743

    MathSciNet  Google Scholar 

  11. Chiang TC (2013) Enhancing rule-based scheduling in wafer fabrication facilities by evolutionary algorithms: Review and opportunity. Comput Ind Eng 64:524–535

    Google Scholar 

  12. Chiang TC, Cheng HC, Fu LC (2010) A memetic algorithm for minimizing total weighted tardiness on parallel batch machines with incompatible job families and dynamic job arrival. Comput Oper Res 37:2257–2269

    MathSciNet  MATH  Google Scholar 

  13. Edmonds J, Karp RM (1972) Theoretical improvements in algorithmic efficiency for network flow problems. J Assoc Comput Mach 19(2):248–264

    MATH  Google Scholar 

  14. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New York

    MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  16. Graves S (1981) A review of production scheduling. Oper Res 29:646–675

    MathSciNet  MATH  Google Scholar 

  17. Hall LA, Shmoys DB (1990) Jackson’s rule for single-machine scheduling: making a good heuristic better. Math Oper Res 17(1):22–35

    MathSciNet  MATH  Google Scholar 

  18. Hanzálek Z, Šůcha P (2017) Time symmetry of resource constrained project scheduling with general temporal constraints and take-give resources. Ann Oper Res 248:209–237

    MathSciNet  MATH  Google Scholar 

  19. Johnson DS, Gutin G, McGeoch LA, Yeo A, Zhang W, Zverovitch A (2002) The traveling salesman problem and its variations. Kluwer Academic Publishers, Dordrecht

    MATH  Google Scholar 

  20. Kanellakis PC, Papadimitriou CH (1980) Local search for the asymmetric traveling salesman problem. Oper Res 28(5):1086–1099

    MathSciNet  MATH  Google Scholar 

  21. Karp RM (1979) A patching algorithm for the nonsymmetric traveling-salesman problem. SIAM J Comput 8(4):561–573

    MathSciNet  MATH  Google Scholar 

  22. Karp RM, Steele JM (1985) Probabilistic analysis of heuristics. In: The traveling salesman problem. Wiley, New York, pp 181–205

  23. Kashan AH, Karimi B, Jenabi M (2008) A hybrid genetic heuristic for scheduling parallel batch processing machines with arbitrary job sizes. Comput Oper Res 35:1084–1098

    MATH  Google Scholar 

  24. Kim YD, Joo BJ, Choi SY (2010) Scheduling wafer lots on diffusion machines in a semiconductor wafer fabrication facility. IEEE Trans Semicond Manuf 23(2):246–254

    Google Scholar 

  25. Koh SG, Koo PH, Kim DC, Hur WS (2005) Scheduling a single batch processing machine with arbitrary job sizes and incompatible job families. Int J Prod Econ 98:81–96

    Google Scholar 

  26. Lawler EL, Lenstra JK, Rinnooy Kan AHG, Shmoys D (1985) The traveling salesman problem: a guided tour to combinatorial optimization. Wiley, New York

    MATH  Google Scholar 

  27. Liu SQ, Kozan E (2012) A hybrid shifting bottleneck procedure algorithm for the parallel-machine job-shop scheduling problem. J Oper Res Soc 63:168–182

    Google Scholar 

  28. López-Ibáñez M, Blum C, Ohlmann JW, Thomas BW (2013) The travelling salesman problem with time windows: adapting algorithms from travel-time to makespan optimization. Appl Soft Comput 13:3806–3815

    Google Scholar 

  29. Malve S, Uzsoy R (2007) A genetic algorithm for minimizing maximum lateness on parallel identical batch processing machines with dynamic job arrivals and incompatible job families. Comput Oper Res 34:3016–3028

    MathSciNet  MATH  Google Scholar 

  30. Méndez CA, Cerdá J, Grossmann IE, Harjunkoski I, Fahl M (2006) State-of-the-art review of optimization methods for short-term scheduling of batch processes. Comput Chem Eng 30:913–946

    Google Scholar 

  31. Mladenović N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24(11):1097–1100

    MathSciNet  MATH  Google Scholar 

  32. Mönch L (2012) Design and implementation of a service-based scheduling component for complex manufacturing systems. In: Proceedings of the 14th international conference on enterprise information systems, pp 284–290

  33. Mönch L, Drießel R (2005) A distributed shifting bottleneck heuristic for complex job shops. Comput Ind Eng 49:363–380

    Google Scholar 

  34. Mönch L, Fowler JW, Dauzère-Pérès S, Mason SJ, Rose O (2011) A survey of problems, solutions techniques, and future challenges in semiconductor manufacturing operations. J Sched 14:583–599

    MathSciNet  Google Scholar 

  35. Phanden RK (2016) Multi agents approach for job shop scheduling problem using genetic algorithm and variable neighborhood search method. In: Proceedings of the 20th world multi-conference on systemics, cybernetics, and informatics, pp 275–278

  36. Potts CN (1980) Analysis of a heuristic for one machine sequencing with release dates and delivery times. Oper Res 28(6):1436–1441

    MathSciNet  MATH  Google Scholar 

  37. Ramacher R, Mönch L (2016) An automated negotiation approach to solve single machine scheduling problems with interfering job sets. Comput Ind Eng 99:318–329

    Google Scholar 

  38. Schutten JMJ, van de Velde SL, Zijm WHM (1996) Single-machine scheduling with release dates, due dates and family setup times. Manag Sci 42(8):1165–1174

    MATH  Google Scholar 

  39. Shen L, Mönch L, Buscher U (2014) A simultaneous and iterative approach for parallel machine scheduling with sequence-dependent family setups. J Sched 17:471–487

    MathSciNet  MATH  Google Scholar 

  40. Sobeyko O, Mönch L (2015) Grouping genetic algorithms for solving single machine multiple orders per job scheduling problems. Ann Oper Res 235:709–739

    MathSciNet  MATH  Google Scholar 

  41. Sobeyko O, Mönch L (2015) Heuristic approaches for scheduling jobs in large-scale flexible job shops. Comput Oper Res 68:97–109

    MathSciNet  MATH  Google Scholar 

  42. Sourirajan K, Uzsoy R (2007) Hybrid decomposition heuristics for solving large-scale scheduling problems in semiconductor wafer fabrication. J Sched 10:41–65

    MATH  Google Scholar 

  43. Tan Y, Mönch L, Fowler JW (2014) A decomposition heuristic for a two-machine flow shop with batch processing. In: Proceedings of the 2014 winter simulation conference, pp. 2490–2501

  44. Uzsoy R (1995) Scheduling batch processing machines with incompatible job families. Int J Prod Res 33(10):2685–2708

    MATH  Google Scholar 

  45. Wang CS, Uzsoy R (2002) A genetic algorithm to minimize maximum lateness on a batch processing machine. Comput Oper Res 29:1621–1640

    MathSciNet  Google Scholar 

  46. Wright S (1932) The roles of mutation, inbreeding, crossbreeding, and selection in evolution. In: Proceedings of the sixth international congress on genetics, pp 356–366

  47. Waschneck B, Altenmüller T, Bauernhansl T, Kyek A (2017) Production scheduling in complex job shops from an industry 4.0 perspective: a review and challenges in the semiconductor industry. CEUR Workshop Proceedings 1793

  48. Yusof R, Khalid M, Hui GT, Yusof SM, Othman MF (2011) Solving job shop scheduling problem using a hybrid parallel micro genetic algorithm. Appl Soft Comput 11:5782–5792

    Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge and thank Dr. Abhijit Gosavi from the Department of Engineering Management and Systems Engineering at Missouri University of Science and Technology for his valuable review and feedback.

Author information

Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Southern Illinois University, Box 1805, Edwardsville, IL, USA

    Paul Cayo & Sinan Onal

Authors
  1. Paul Cayo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sinan Onal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sinan Onal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cayo, P., Onal, S. A shifting bottleneck procedure with multiple objectives in a complex manufacturing environment. Prod. Eng. Res. Devel. 14, 177–190 (2020). https://doi.org/10.1007/s11740-019-00947-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11740-019-00947-7

Keywords

Search

Navigation