Advertisement

Annals of Operations Research

, Volume 274, Issue 1–2, pp 187–210 | Cite as

Important classes of reactions for the proactive and reactive resource-constrained project scheduling problem

  • Morteza DavariEmail author
  • Erik Demeulemeester
Original Research

Abstract

The proactive and reactive resource-constrained project scheduling problem (PR-RCPSP), that has been introduced recently (Davari and Demeulemeester, 2017), deals with activity duration uncertainty in a very unique way. The optimal solution to an instance of the PR-RCPSP is a proactive and reactive policy (PR-policy) that is a combination of a baseline schedule and a set of required transitions (reactions). In this research, we introduce two interesting classes of reactions, namely the class of selection-based reactions and the class of buffer-based reactions, the latter in fact being a subset of the class of selection-based reactions. We also discuss the theoretical relevance of these two classes of reactions. We run some computational results and report the contributions of the selection-based reactions and the buffer-based reactions in the optimal solution. The results suggest that although both selection-based reactions and buffer-based reactions contribute largely in the construction of the optimal PR-policy, the contribution of the buffer-based reactions is of much greater importance. These results also indicate that the contributions of non-selection-based reactions (reactions that are not selection-based) and selection-but-not-buffer-based reactions (selection-based reactions that are not buffer-based) are very limited.

Keywords

Proactive and reactive RCPSP Stochastic durations Buffer-based reactions Proactive and reactive policies 

References

  1. Alvarez-Valdes, R., & Tamarit, J. (1993). The project scheduling polyhedron: Dimension, facets and lifting theorems. European Journal of Operational Research, 67(2), 204–220.CrossRefGoogle Scholar
  2. Artigues, C., Leus, R., & Talla Nobibon, F. (2013). Robust optimization for resource-constrained project scheduling with uncertain activity durations. Flexible Services and Manufacturing Journal, 25(1–2), 175–205.CrossRefGoogle Scholar
  3. Ashtiani, B., Leus, R., & Aryanezhad, M.-B. (2011). New competitive results for the stochastic resource-constrained project scheduling problem: Exploring the benefits of pre-processing. Journal of Scheduling, 14(2), 157–171.CrossRefGoogle Scholar
  4. Ballestín, F. (2007). When is it worthwhile to work with the stochastic RCPSP? Journal of Scheduling, 10(3), 153–166.CrossRefGoogle Scholar
  5. Ballestín, F., & Leus, R. (2009). Resource-constrained project scheduling for timely project completion with stochastic activity durations. Production and Operations Management, 18, 459–474.CrossRefGoogle Scholar
  6. Bertsekas, D . P. (2007). Dynamic programming and optimal control. Belmont, MA: Athena Scientific.Google Scholar
  7. Choi, J., Realff, M. J., & Lee, J. H. (2004). Dynamic programming in a heuristically confined state space: A stochastic resource-constrained project scheduling application. Computers & Chemical Engineering, 28(6), 1039–1058.CrossRefGoogle Scholar
  8. Cong, J. (1993). Computing maximum weighted k-families and k-cofamilies in partially ordered sets. Technical report, University of CaliforniaGoogle Scholar
  9. Creemers, S. (2015). Minimizing the expected makespan of a project with stochastic activity durations under resource constraints. Journal of Scheduling, 18(3), 263–273.CrossRefGoogle Scholar
  10. Davari, M. (2017). Contributions to complex project and machine scheduling problems. Ph.D. thesis, Faculty of Economics and Business, KU LeuvenGoogle Scholar
  11. Davari, M. & Demeulemeester, E. (2016). A novel branch-and-bound algorithm for the chance-constrained RCPSP. Technical Report KBI_1620, KU LeuvenGoogle Scholar
  12. Davari, M., & Demeulemeester, E. (2017). The proactive and reactive resource-constrained project scheduling problem. Journal of Scheduling.  https://doi.org/10.1007/s10951-017-0553-x.Google Scholar
  13. Fang, C., Kolisch, R., Wang, L., & Mu, C. (2015). An estimation of distribution algorithm and new computational results for the stochastic resource-constrained project scheduling problem. Flexible Services and Manufacturing Journal, 27(4), 585–605.CrossRefGoogle Scholar
  14. Golumbic, M . C. (1980). Chapter 5: Comparability graphs. In M . C. Golumbic (Ed.), Algorithmic graph theory and perfect graphs (pp. 105–148). Cambridge, MA: Academic Press.CrossRefGoogle Scholar
  15. Grötschel, M., Lovász, L., & Schrijver, A. (1984). Polynomial algorithms for perfect graphs. In C. Berge & V. Chvátal (Eds.), Topics on perfect graphs, volume 88 of North-Holland Mathematics Studies (pp. 325–356). Amsterdam: North-Holland.Google Scholar
  16. Igelmund, G., & Radermacher, F. J. (1983). Preselective strategies for the optimization of stochastic project networks under resource constraints. Networks, 13(1), 1–28.CrossRefGoogle Scholar
  17. Kaerkes, R., & Leipholz, B. (1977). Generalized network functions in flow networks. Methods of Operations Research, 27, 441–465.Google Scholar
  18. Kolisch, R., & Sprecher, A. (1997). PSPLIB: a project scheduling problem library. European Journal of Operational Research, 96(1), 205–216.CrossRefGoogle Scholar
  19. Lambrechts, O., Demeulemeester, E., & Herroelen, W. (2008). Proactive and reactive strategies for resource-constrained project scheduling with uncertain resource availabilities. Journal of Scheduling, 11(2), 121–136.CrossRefGoogle Scholar
  20. Leus, R. (2003). The generation of stable project plans. Ph.D. thesis, Department of applied economics, Katholieke Universiteit Leuven, Belgium.Google Scholar
  21. Leus, R. (2011a). Resource allocation by means of project networks: Complexity results. Networks, 58(1), 59–67.CrossRefGoogle Scholar
  22. Leus, R. (2011b). Resource allocation by means of project networks: Dominance results. Networks, 58(1), 50–58.CrossRefGoogle Scholar
  23. Li, H., & Womer, N. K. (2015). Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming. European Journal of Operational Research, 246(1), 20–33.CrossRefGoogle Scholar
  24. Mehta, S., & Uzsoy, R. (1998). Predictive scheduling of a job shop subject to breakdowns. IEEE Transactions on Robotics and Automation, 14, 365–378.CrossRefGoogle Scholar
  25. Möhring, R., Radermacher, F., & Weiss, G. (1984). Stochastic scheduling problems I: Set strategies. Mathematical Methods of Operations Research, 28, 193–260.CrossRefGoogle Scholar
  26. Möhring, R., Radermacher, F., & Weiss, G. (1985). Stochastic scheduling problems II: set strategies. Mathematical Methods of Operations Research, 29(3), 65–104.CrossRefGoogle Scholar
  27. Neumann, K., Schwindt, C., & Zimmermann, J. (2003). Project scheduling with time windows and scarce resources. Berlin: Springer.CrossRefGoogle Scholar
  28. Radermacher, F. (1981). Cost-dependent essential systems of ES-strategies for stochastic scheduling problems. Methods of Operations Research, 42, 17–31.Google Scholar
  29. Rostami, S., Creemers, S., & Leus, R. (2017). New benchmark results for the stochastic resource-constrained project scheduling problem. Journal of Scheduling.  https://doi.org/10.1007/s10951-016-0505-x.Google Scholar
  30. Schwindt, C. (2005). Resource allocation in project management. Berlin: Springer.Google Scholar
  31. Stork, F. (2001). Stochastic resource-constrained project scheduling. Ph.D. thesis, Technical University of Berlin, School of Mathematics and Natural Sciences.Google Scholar
  32. Van de Vonder, S., Ballestín, F., Demeulemeester, E., & Herroelen, W. (2007). Heuristic procedures for reactive project scheduling. Computers & Industrial Engineering, 52(1), 11–28.CrossRefGoogle Scholar
  33. Van de Vonder, S., Demeulemeester, E., & Herroelen, W. (2008). Proactive heuristic procedures for robust project scheduling: An experimental analysis. European Journal of Operational Research, 189(3), 723–733.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.CODeS, Department of Computer Science & imec-ITECKU Leuven KULAKKortrijkBelgium
  2. 2.Department of Decision Sciences and Information Management, Research Center for Operations Management, Faculty of Business and EconomicsKU LeuvenLouvainBelgium

Personalised recommendations