Skip to main content
SpringerLink
Log in

Distributed Scheduling in Multiple-factory Production with Machine Maintenance

  • Chapter
Process Planning and Scheduling for Distributed Manufacturing

Part of the book series: Springer Series in Advanced Manufacturing ((SSAM))

Abstract

In general, the distributed scheduling problem focuses on solving two issues simultaneously: (i) allocation of jobs to suitable factories, and (ii) determination of the corresponding production scheduling in each factory. Its objective is to maximise the system efficiency by finding an optimal plan for a better collaboration among various processes. This makes the distributed scheduling problem more complicated than the classical production scheduling ones. With the addition of alternative production routing, the problems are even more complicated. Conventionally, machines are assumed to be available without interruption during the production scheduling. Maintenance is usually not considered. However, in reality, this assumption is not true in most cases. Maintenance policy always directly affects the machine availability. Consequently, it interrupts the production. In this connection, maintenance should be considered with the distributed scheduling problems. In this chapter, a genetic algorithm with dominant genes (GADG) approach is introduced to deal with this problem. The significance and benefits of considering maintenance are demonstrated by simulation runs in an example.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Shen, W. and Norrie, D.H., 1999, “Agent-based systems for intelligent manufacturing: a state-of-the-art survey,” International Journal Knowledge and Information Systems, 1(2), pp. 129–156.

    Google Scholar 

  2. Schniederjans, M.J., 1999, International Facility Acquisition and Location Analysis, Quorum Books, Westport.

    Google Scholar 

  3. Sule, D.R., 2001, Logistics of Facility Location and Allocation, Marcel Dekker, Inc. New York, NY.

    Google Scholar 

  4. Drezner, Z., 1995, Facility Location: A Survey of Applications and Methods, Springer-Verlag Inc., New York.

    Google Scholar 

  5. Jayaraman, V., 1998, “Transportation, facility location and inventory issues in distribution network design,” International Journal of Operations and Production Management, 18(5), pp. 471–494.

    Article  Google Scholar 

  6. Dhaenens-Flipo, G. and Finke, G., 2001, “An integrated model for an industrial production-distribution problem,” IIE Transactions, 33(9), pp. 705–715.

    Article  Google Scholar 

  7. Stankovic, J., 1996, “Strategic directions in real-time and embedded systems,” ACM Computing Surveys, 28(4), pp. 751–763.

    Article  MathSciNet  Google Scholar 

  8. Kim, K.H., Bae, J.W., Song, J.Y. and Lee, H.Y., 1996, “A distributed scheduling and shop floor control method,” Computer Industrial Engineering, 31(3/4), pp. 583–586.

    Google Scholar 

  9. Wang, H.H. and Wu, Z.M., 2003, “The application of Adaptive Genetic Algorithms in FMS dynamic rescheduling,” International Journal of Computer Integrated Manufacturing, 16(6), pp. 382–397.

    Article  Google Scholar 

  10. Vincent, A.C. and Stephen, F.S., 2004, “Wasp-like agents for distributed factory coordination,” Autonomous Agents and Multi-Agent Systems, 8, pp. 237–266.

    Article  Google Scholar 

  11. Sandholm, T.W., 2000, “Automated contracting in distributed manufacturing among independent companies,” Journal of Intelligent Manufacturing”, 11, pp. 271–283.

    Article  Google Scholar 

  12. Barroso, A.M., Leite, J.C.B. and Loques, O.G., 2002, “Treating uncertainty in distributed scheduling,” The Journal of Systems and Software, 63, pp. 129–136.

    Article  Google Scholar 

  13. Jia, H.Z., Nee, A.Y.C., Fuh, J.Y.H. and Zhang, Y.F., 2003, “A modified genetic algorithm for distributed scheduling problems,” Journal of Intelligent Manufacturing, 14, pp. 351–362.

    Article  Google Scholar 

  14. Ballou, R.H., 1998, Business Logistics Management, Prentice Hall, Upper Saddle River, New Jersey.

    Google Scholar 

  15. Garey, M.R. and Johnson, D.S., 1979, Computers and Interactability: A Guide to the Theory of NP-completeness, Freeman and Co., San Francisco, CA.

    MATH  Google Scholar 

  16. Chan, F.T.S., Chung, S.H., and Chan, P.L.Y., 2005, “An adaptive genetic algorithm for distributed production and scheduling problems with alternative production routines,” Expert Systems with Application, 29(2), pp. 261–371.

    Google Scholar 

  17. Cohen, M.A. and Lee, H.L., 1998, “Strategic analysis of integrated production-distribution systems: models and methods,” Operations Research, 36(2), pp. 216–228.

    Google Scholar 

  18. Beamon, B.M., 1999, “Supply chain design and analysis: models and methods,” International Journal of Production Economics, 55(1), pp. 281–294.

    Google Scholar 

  19. Lee, Y.H., Kim, S.H. and Moon, C., 2002, “Production-distribution planning in supply chain using a hybrid approach,” Production Planning and Control, 13(1), pp. 35–46.

    Article  Google Scholar 

  20. Thomas, D.J. and Griffin, P.M., 1996, “Coordinated supply chain management,” European Journal of Operational Research, 94(1), pp. 1–15.

    Article  MATH  Google Scholar 

  21. Chan, F.T.S. and Chung, S.H., 2005, “Multi-criteria genetic optimization for due date assigned distribution network problems,” Decision Support Systems, 39, pp. 661–675.

    Article  Google Scholar 

  22. Willis, A.K., 1996, “Customer Delight and Demand Management: Can they be integrated?” Hospital Material Management Quarterly, 18(2), pp. 58–65.

    Google Scholar 

  23. Wu, S.H.., Fuh, J.Y.H. and Nee, A.Y.C., 2002, “Concurrent process planning and scheduling in distributed virtual manufacturing,” IIE Transactions, 34(1), pp. 77–89.

    Article  Google Scholar 

  24. Siwamogsatham, T. and Saygin, C., 2004, “Auction-based distributed scheduling and control scheme for flexible manufacturing systems,” International Journal of Production Research, 42(3), pp. 542–572.

    Article  Google Scholar 

  25. Byrne, M.D. and Chutima, P., 1997, “Real-time operational control of an FMS with full routing flexibility,” International Journal of Production Economics, 51(1–2), pp. 109–113.

    Article  Google Scholar 

  26. Abdinnour-Helm, S., 1999, “Network design in supply chain management,” International Journal of Agile Management Systems, 1(2), pp. 99–106.

    Article  Google Scholar 

  27. Glover, F., 1986, “Future paths for integer programming links to artificial intelligence,” Computers and Operations Research, 13(5), pp. 533–589.

    Article  MATH  MathSciNet  Google Scholar 

  28. Glover, F., 1989, “Tabu search part I”, ORSA Journal on Computing, 1(3), pp. 190–206.

    MATH  Google Scholar 

  29. Goldberg, D.E., 1989, Genetic Algorithms in Search, Optimization and Machine Learning, Reading, MA: Addison-Wesley.

    MATH  Google Scholar 

  30. Vignaux, G.A. and Michalewicz, Z., 1991, “A genetic algorithm for the linear transportation problem,” IEEE Transactions on Systems, Man, and Cybernetics, 21(2), pp. 445–452.

    Article  MATH  MathSciNet  Google Scholar 

  31. González, E.L. and Fernández, M.A.R., 2000, “Genetic optimization of a fuzzy distribution model,” International Journal of Physical Distribution and Logistics Management, 30(7/8), pp. 681–696.

    Article  Google Scholar 

  32. Al-Hakin, L., 2001, “An analogue genetic algorithm for solving job shop scheduling problems,” International Journal of Production Research, 39(7), pp. 1537–1548.

    Article  Google Scholar 

  33. Aytug, H., Khouja, M. and Vergara, F.E., 2003, “Use of genetic algorithms to solve production and operations management problems: a review,” International Journal of Production Research, 41(17), pp. 3955–4009.

    Article  Google Scholar 

  34. Cheung, R., Gen, M. and Tsujimura, Y., 1996, “A tutorial survey of job-shop scheduling problems using genetic algorithms — I,” Computers and Industrial Engineering, 30(4), pp. 983–997.

    Article  Google Scholar 

  35. Cheung, R., Gen, M. and Tsujimura, Y., 1999, “A tutorial survey of job-shop scheduling problems using genetic algorithms — II,” Computers and Industrial Engineering, 37(1), pp. 51–55.

    Article  Google Scholar 

  36. Jain, A.K. and ElMaraghy, H.A., 1997, “Single process plan scheduling with genetic algorithm,” Production Planning and Control, 8(4), pp. 363–376.

    Article  Google Scholar 

  37. Cavalieri, S. and Gaiardelli, P., 1998, “Hybrid genetic algorithms for a multipleobjective scheduling problem,” Journal of Intelligent Manufacturing, 9, pp. 361–367.

    Article  Google Scholar 

  38. Sakawa, M., 2002, Genetic Algorithms and Fuzzy Multiobjective Optimization, Kluwer Academic Publishers, pp. 188–222.

    Google Scholar 

  39. Wadhwa, S. and Chopra, A., 2000, “A Genetic Algorithm application: dynamic reconfiguration in agile manufacturing systems,” Studies in Informatics and Control, 9(4), “http://www.ici.ro/ici/revista/sic2000_4/index.html”.

    Google Scholar 

  40. Mori, M. and Tseng, C.C., 1997, “Genetic algorithms for multi-mode resource constrained project scheduling problem,” European Journal of Operational Research, 100(1), pp. 134–141.

    Article  MATH  MathSciNet  Google Scholar 

  41. Ghedjati, F., 1999, “Genetic algorithms for the job-shop scheduling problem with unrelated parallel constraints: Heuristics mixing method machines and precedence,” Computers and Industrial Engineering, 73, pp. 39–42.

    Article  Google Scholar 

  42. Grefenstette, J.J., 1986, “Optimization of control parameters for Genetic Algorithms”, IEEE Transactions on Systems, Man, and Cybernetics, 16(1), pp. 122–128.

    Article  Google Scholar 

  43. Michalewicz, Z., 1996, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Berlin, Heidelberg, New York.

    MATH  Google Scholar 

  44. DiNatale, M. and Stankovic, J.A., 1995, “Applicability of simulated annealing methods to real-time scheduling and jitter control,” In 16th IEEE Real-time Systems Symposium, Pisa, Italy.

    Google Scholar 

  45. Barroso, A.M., Torreao, J.R.A., Leite, J.C.B., Loques, O.G., and Fraga, J.S., 1997, “A new technique for task allocation in real-time distributed systems,” In: Proceedings of the 7th Brazilian Symposium of Fault Tolerant Computers, Campina Grande, Brazil, pp. 269–278.

    Google Scholar 

  46. Santos, J., Ferro, E., Orozco, J. and Cayssials, R., 1997, “A heuristic approach to the multi-task-multiprocessor assignment problem using the empty-slots method and rate-monotonic scheduling,” Journal of Real-time Systems, 13, pp. 167–199.

    Article  Google Scholar 

  47. Tindell, K.W., Burns, A. and Wellings, A.J., 1992, “Allocating hard realtime tasks: an NP-hard problem made easy,” Journal of Real-time Systems, 4, pp. 145–165.

    Article  Google Scholar 

  48. Jia, H.Z., Fuh, J.Y.H., Nee, A.Y.C. and Zheng, Y.F., 2002, “Web-based multifunctional scheduling system for a distributed manufacturing environment,” Concurrent Engineering: Research and Applications, 10(1), pp. 27–39.

    Google Scholar 

  49. Erschler, J., Roubellat, F. and Thuriot, C., 1985, “Steady state scheduling of a flexible manufacturing system with periodic releasing and flow time constraints,” Annals of Operations Research, 3, pp. 333–353.

    Article  Google Scholar 

  50. Shriskandarajah, C. and Ladet, P., 1986, “Some no-wait shops scheduling problems,” European Journal of Operational Research, 24, pp. 424–445.

    Article  MathSciNet  Google Scholar 

  51. Langston, M.A., 1987, “Interstage transportation planning in the deterministic flowshop environment,” Operations Research, 35(4), pp. 556–564.

    Article  Google Scholar 

  52. Wittrock, R.J., 1998, “An adaptable scheduling algorithm for flexible flow lines,” Operations Research, 36, pp. 445–453.

    Google Scholar 

  53. Shriskandarajah, C. and Sethi, S.P., 1989, “Scheduling algorithms for flexible flow shop: worst and average case performance,” European Journal of Operational Research, 43, pp. 143–160.

    Article  MathSciNet  Google Scholar 

  54. Ghosh, S. and Gaimon, C., 1992, “Routing flexibility and production scheduling in a flexible manufacturing environment,” European Journal of Operational Research, 60, pp. 344–364.

    Article  MATH  Google Scholar 

  55. Stecke, K.E., 1992, “Planning and scheduling approaches to operate a particular FMS,” European Journal of Operational Research, 61, pp. 273–291.

    Article  Google Scholar 

  56. Caprihan, R. and Wadhwa, C.S., 1997, “Impact of routing flexibility on the performance of an FMS — a simulation study,” International Journal of Flexible Manufacturing Systems, 9, pp. 273–298.

    Article  Google Scholar 

  57. Lee, C.Y. and Vairktarakis, G., 1998, “Performance comparison of some classes of flexible flow shops and job shops,” International Journal of Flexible Manufacturing Systems, 10, pp. 379–405.

    Article  Google Scholar 

  58. Jawahar, N., Aravindan, P. and Ponnambalam, S.G., 1998, “A genetic algorithm for scheduling flexible manufacturing systems,” International Journal of Advanced Manufacturing Technology, 14, pp. 588–607.

    Article  Google Scholar 

  59. Chan, F.T.S., Chung, S.H. and Chan, P.L.Y., 2006, “Application of genetic algorithms with dominant genes in distributed scheduling problem in FMS,” International Journal of Production Research, 44(3), pp. 523–543.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China

    Felix Tung Sun Chan & Sai Ho Chung

Authors
  1. Felix Tung Sun Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sai Ho Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Integrated Manufacturing Technologies Institute, National Research Council of Canada, London, Ontario, N6G 4X8, Canada

    Lihui Wang PhD, PEng (Senior Research Officer) & Weiming Shen PhD, PEng (Senior Research Officer) (Senior Research Officer) &  (Senior Research Officer)

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag London Limited

About this chapter

Cite this chapter

Chan, F.T.S., Chung, S.H. (2007). Distributed Scheduling in Multiple-factory Production with Machine Maintenance. In: Wang, L., Shen, W. (eds) Process Planning and Scheduling for Distributed Manufacturing. Springer Series in Advanced Manufacturing. Springer, London. https://doi.org/10.1007/978-1-84628-752-7_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-84628-752-7_10

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-751-0

  • Online ISBN: 978-1-84628-752-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation