Solving Flow Shop Problems with Bounded Dynamic Programming

  • Joaquín Bautista
  • Alberto Cano
  • Ramon Companys
  • Imma Ribas
Conference paper

Abstract

We present some results attained with the bounded dynamic programming algorithms to solve the Fm|prmu|C max and the Fm|block|C max problems using the well-known Taillard instances as experimental data. We have improved four of the best-known solutions of the Taillard’s instances for the Fm|block|C max problem and we have confirmed the optimality of six solutions for the Fm|prmu|C max case.

Keywords

Completion Time Travel Salesman Problem Travel Salesman Problem Partial Solution Flow Shop 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work is supported by the UPC Nissan Chair and the Spanish Ministerio de Educación y Ciencia under project DPI2010-16759 (PROTHIUS-III) including EDRF fundings.

References

  1. Bautista J (1993) Procedimientos heurísticos y exactos para la secuenciación en sistemas productivos de unidades homogéneas (contexto J.I.T.). Doctoral Thesis, DOE, ETSEIB-UPCGoogle Scholar
  2. Bautista J, Companys R, Corominas A (1996) Heuristics and exact algorithms for solving the Monden problem. Eur J Oper Res 88:101–113CrossRefMATHGoogle Scholar
  3. Bautista J, Cano A (2011) Solving mixed model sequencing problem in assembly lines with serial workstations with work overload minimization and interruption rules. Eur J Oper Res 210:495–513CrossRefMATHGoogle Scholar
  4. Bautista J, Cano A, Companys R, Ribas I (2011) Solving the Fm∣block∣Cmax problem using bounded dynamic programming. Engineering Applications of Artificial Intelligence, Corrected Proof ( DOI: 10.1016/j.engappai.2011.09.001) in press
  5. Garey MR, Johnson DS, Sethi R (1976) Complexity of flowshop and jobshop scheduling. Math Oper Res 1(2):117–129CrossRefMATHMathSciNetGoogle Scholar
  6. Gilmore PC, Lawler EL, Shmoys DB (1985) Well-solved special cases. In: Lawler EL, Lenstra KL, Rinooy Kan AHG, Shmoys DB (eds) The traveling salesman problem: a guided tour of combinatorial optimization, Wiley, New YorkGoogle Scholar
  7. Hall NG, Sriskandarajah C (1996) A survey of machine scheduling problems with blocking and no wait in process. Oper Res 44(3):510–525CrossRefMATHMathSciNetGoogle Scholar
  8. Hejazi RS, Saghafian S (2005) Flowshop-scheduling problems with makespan criterion: a review. Int J Prod Res 43(14):2895–2929CrossRefMATHGoogle Scholar
  9. Johnson SM (1954) Optimal two-and three-stage production schedules with set up times included. Naval Res Logist Quart 1:61–68CrossRefGoogle Scholar
  10. Reddi SS, Ramamoorthy B (1972) On the flow-shop sequencing problem with no wait in process. Oper Res Quart 23(3):323–331CrossRefMATHGoogle Scholar
  11. Reisman A, Kumar A, Motwani J (1994) ‘Flowshop scheduling/sequencing research: A statistical review of the literature, 1952–1994’. IEEE Transact Eng Manag 44(3):316–329CrossRefGoogle Scholar
  12. Ruiz R, Maroto C (2005) A comprehensive review and evaluation of permutation flowshop heuristics. Eur J Oper Res 165(2):479–494CrossRefMATHMathSciNetGoogle Scholar
  13. Taillard E (1993) Benchmarks for basic scheduling problems. Eur J Oper Res 64(2):278–285CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag London Limited  2012

Authors and Affiliations

  • Joaquín Bautista
    • 1
  • Alberto Cano
    • 1
  • Ramon Companys
    • 2
  • Imma Ribas
    • 2
  1. 1.Nissan Chair, Universitat Politècnica de CatalunyaBarcelonaSpain
  2. 2.Departament D’Organització d’Empreses, ETSEIB, Universitat Politècnica de CatalunyaBarcelonaSpain

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