Construction of Scheduling Models

  • Jose M. Framinan
  • Rainer Leisten
  • Rubén Ruiz García
Chapter

Abstract

In this chapter, we present fundamental issues concerning the construction of scheduling models.

Keywords

Schedule Problem Flow Shop Schedule Model Complexity Reduction Aggregation Approach 
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.

References

  1. Błażewicz, J., Ecker, K. H., Pesch, E., Schmidt, G., and Weglarz, J. (2007). Handbook on scheduling: from theory to applications. Springer, Berlin/Heidelberg/New York.Google Scholar
  2. Bowman, E. H. (1959). The schedule-sequencing problem. Operations Research, 7(5):621–624.Google Scholar
  3. Burdett, R. L. and Kozan, E. (2003). Resource aggregation issues and effects in mixed model assembly. In Kozan, E., Beard, R., and Chattopadhyay, G., editors, Proceedings 5th Operations Research Conference of the Australian Society for Operations Research Queensland Branch on Operations research into the 21st century, pages 35–53, Brisbane, Queensland - Australia. Queensland University of Technology.Google Scholar
  4. Dantzig, G. B. (1960). A machine-job scheduling model. Managemant Science, 6(2):191–196.Google Scholar
  5. Dubois, D., Fargier, H., and Fortemps, P. (2003). Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge. European Journal of Operational Research, 147(2):231–252.Google Scholar
  6. Dubois, D., Fargier, H., and Prade, H. (1996). Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty. Applied Intelligence, 6(4):287–310.Google Scholar
  7. Dubois, D. and Prade, H. (1988). Possibility theory. Plenum Press, New York.Google Scholar
  8. Fayad, C. and Petrovic, S. (2005). A fuzzy genetic algorithm for real-world job shop scheduling. Innovations in Applied Artificial Intelligence: 18th Int. Conf. on Industrial and Engineering Application of Artificial Intelligence and Expert Systems, IEA/AIE 2005. Bari, Italy, 22–24 June 2005, 3533:524–533.Google Scholar
  9. Gibson, J. P. (2001). Formal requirements models: simulation, validation and verification. Technical, Report NUIM-CS-2001-TR-02.Google Scholar
  10. Greenberg, H. H. (1968). A branch-bound solution to the general scheduling problem. Operations Research, 16(2):353–361.Google Scholar
  11. Kuroda, M. and Wang, Z. (1996). Fuzzy job shop scheduling. International Journal of Production Economics, 44(1–2):45–51.Google Scholar
  12. Le Pape, C. (1994). Implementation of resource constraints in ILOG schedule: A library for the development of constraint-based scheduling systems. Intelligent Systems Engineering, 3(2):55–66.Google Scholar
  13. Macal, C. M. (2005). Model verification and validation. Workshop on "Threat Anticipation: Social Science Methods and Models".Google Scholar
  14. Manne, A. S. (1960). On the job-shop scheduling problem. Operations Research, 8(2):219–223.Google Scholar
  15. Nagar, A., Heragu, S. S., and Haddock, J. (1995). A branch-and-bound approach for a two-machine flowshop scheduling problem. Journal of the Operational Research Society, 46(6):721–734.Google Scholar
  16. Pinedo, M. L. (2012). Scheduling: Theory, Algorithms, and Systems. Springer, New York, fourth edition.Google Scholar
  17. Sargent, R. G. (2007). Verification and validation of simulation models. In Henderson, S. G., Biller, B., Hsieh, M.-H., Shortle, J., Tew, J. D., and Barton, R. R., editors, Proceedings of the 2007 Winter Simulation Conference, pages 124–137, Piscataway, NJ. IEEE Operations Center.Google Scholar
  18. Schmidt, G. (1992). A decision support system for production scheduling. Revue des Systemes de decision, 1(2–3):243–260.Google Scholar
  19. Schmidt, G. (1996). Modelling production scheduling systems. International Journal of Production Economics, 46–47:109–118.Google Scholar
  20. Shen, W. and Norrie, D. (1998). An agent-based approach for dynamic manufacturing scheduling. Working Notes of the Agent-Based Manufacturing Workshop, Minneapolis, MN.Google Scholar
  21. Slowinski, R. and Hapke, M. (2000). Foreword. In Slowinski, R. and Hapke, M., editors, Scheduling under Fuzziness, Heidelberg, New York. Physica-Verlag.Google Scholar
  22. Vancza, J., Kis, T., and Kovacs, A. (2004). Aggregation: the key to integrating production planning and scheduling. CIRP Annals of Manufacturing Technology, 3(1):377–380.Google Scholar
  23. Vlach, M. (2000). Single machine scheduling under fuzziness. In Slowinski, R. and Hapke, M., editors, Scheduling under Fuzziness, pages 223–245, Heidelberg, New York. Physica-Verlag.Google Scholar
  24. Wagner, H. M. (1959). An integer linear-programming model for machine scheduling. Naval Research Logistics Quarterly, 6(2):131–140.Google Scholar
  25. Williams, H. P. (2013). Model building in mathematical programming. Wiley, Chichester, 5th ed.Google Scholar
  26. Zadeh, L. A. (1975). Calculus of fuzzy restrictions. In Zadeh, L. A., Fu, K. S., Tanaka, K., and Shimura, M., editors, Fuzzy sets and their applications cognitive and decision processes, pages 1–39, New York. Academic Press.Google Scholar
  27. Zadeh, L. A. (1999). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, 100:9–34.Google Scholar
  28. Zhou, M. C. and Venkatesh, K. (2000). Modelling, simulation and control of flexible manufacturing systems: A petri net approach. World Scientific, Singapore a.o.Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Jose M. Framinan
    • 1
  • Rainer Leisten
    • 2
  • Rubén Ruiz García
    • 3
  1. 1.Departamento Organización Industrial y Gestión de EmpresasUniversidad de Sevilla Escuela Superior de IngenierosIsla de la CartujaSpain
  2. 2.Fakultät für Ingenieurwissenschaften Allgemeine Betriebswirtschaftslehre und Operations ManagementUniversität Duisburg-EssenDuisburgGermany
  3. 3.Grupo de Sistemas de Optimización Aplicada, Instituto Tecnológico de InformáticaUniversitat Politècnica de ValènciaValenciaSpain

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