Advertisement

RCPS with Variable Intensity Activities and Feeding Precedence Constraints

  • Tamás Kis
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 92)

Abstract

This paper presents a branch-and-cut based exact solution algorithm for scheduling of projects with variable intensity activities connected by feeding precedence constraints the objective being to minimize the violation of resource constraints. Feeding precedence constraints allow some overlap in the execution of the connected activities and capture the flow of material or information between them. New polyhedral results are obtained and computational results are summarized.

Keywords

variable intensity activities branch-and-cut 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. De Boer, R. (1998). Resource-Constrained Multi-Project Management-A Hierarchical Decision Support System, Ph.D. thesis, Twente University Press, The Netherlands.Google Scholar
  2. Ford, L. R., and Fulkerson, D. R. (1956). Maximal flow through a network, Canadian Journal of Mathematics 8:399–404.MathSciNetGoogle Scholar
  3. Gademann, N., and Schutten, M. (2005), Linear programming based heuristics for project capacity planning, HE Transactions, 37(2): 153–165.Google Scholar
  4. Hans, E. W. (2001). Resource loading by branch-and-price techniques, Ph.D. thesis, Twente University Press, The Netherlands.Google Scholar
  5. Jünger, M., Reinelt, G., and Thienel, S. (1995). Practical problem solving with cutting plane algorithms in combinatorial optimization, DIMACS Ser. in Discr. Math. and Theor. Comput. Sci. 20:111–152.Google Scholar
  6. Kis, T. (2004). A branch-and-cut algorithm for scheduling of projects with variable intensity activities, Math. Prog., in press.Google Scholar
  7. Leachman, R. C. (1983). Multiple resource leveling in construction systems through variation of activity intensities, Naval research logistics quarterly 30(3):187–198.Google Scholar
  8. Leachman, R. C., Dincerler, A., and Kim, S. (1990). Resource-constrained scheduling of projects with variable-intensity activities, HE Transactions 22(l):31–39.Google Scholar
  9. Márkus, A., Váncza, J., Kis, T., and Kovács, A. (2003). Project oriented view on production planning, CIRP Annals of Manufacturing Technology 52(1):359–362.Google Scholar
  10. Nemhauser, G. L., and Wolsey, L. A. (1988) Integer and Combinatorial Optimization, John Wiley & Sons, New York.zbMATHGoogle Scholar
  11. Padberg, M. W., Van Roy, T. J., and Wolsey, L. A. (1985), Valid linear inequalities for fixed charge problems, Operations Research 33(4):842–861.MathSciNetCrossRefGoogle Scholar
  12. Padberg, M. W., and Rinaldi, G. (1987). Optimization of a 532 city symmetric traveling salesman problem by branch and cut, Open Res. Lett. 6:1–7.CrossRefMathSciNetGoogle Scholar
  13. Tavares, L. V. (1998). Advanced models for project management, Kluwer Academic Publishers, pp. 177–216.Google Scholar
  14. Tavares, L. V. (2002). A review of the contribution of Operational Research to Project Management, Eur. Jour. Ops. Res. 136:1–18.CrossRefGoogle Scholar
  15. Weglarz, J. (1976). Time-optimal control of resource allocation in a complex of operations framework, IEEE Trans. Systems, Man and Cybernetics 6:783–788.MathSciNetCrossRefGoogle Scholar
  16. Weglarz, J. (1979). Project scheduling with discrete and continuous resources, IEEE Trans. Systems, Man and Cybernetics 9:644–650.Google Scholar
  17. Weglarz, J. (1981). Project scheduling with continuously-divisible doubly constrained resources, Management Science 27(9): 1040–1053.Google Scholar
  18. Wullink, G. (2005). Resource loading under uncertainty, Ph.D. Thesis, Twente University Press, The Netherlands.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Tamás Kis
    • 1
  1. 1.Computer and Automation InstituteHungarian Academy of SciencesHungary

Personalised recommendations