Advertisement

Journal of Scheduling

, Volume 21, Issue 3, pp 349–365 | Cite as

New strategies for stochastic resource-constrained project scheduling

  • Salim Rostami
  • Stefan Creemers
  • Roel Leus
Article

Abstract

We study the stochastic resource-constrained project scheduling problem or SRCPSP, where project activities have stochastic durations. A solution is a scheduling policy, and we propose a new class of policies that is a generalization of most of the classes described in the literature. A policy in this new class makes a number of a priori decisions in a preprocessing phase, while the remaining scheduling decisions are made online. A two-phase local search algorithm is proposed to optimize within the class. Our computational results show that the algorithm has been efficiently tuned toward finding high-quality solutions and that it outperforms all existing algorithms for large instances. The results also indicate that the optimality gap even within the larger class of elementary policies is very small.

Keywords

Project scheduling Uncertainty Stochastic activity durations Scheduling policies 

References

  1. Al-Bahar, J. F., & Crandall, K. C. (1990). Systematic risk management approach for construction projects. Journal of Construction Engineering and Management, 116, 533–546.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. (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 it is 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. Bendavid, I., & Golany, B. (2011). Predetermined intervals for start times of activities in the stochastic project scheduling problem. Annals of Operations Research, 186, 429–442.CrossRefGoogle Scholar
  7. Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J. (2009). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2), 239–287.CrossRefGoogle Scholar
  8. Blazewicz, J., Lenstra, J. K., & Rinnooy Kan, A. H. G. (1983). Scheduling subject to resource constraints. Discrete Applied Mathematics, 5, 11–24.CrossRefGoogle Scholar
  9. Bruni, M. E., Beraldi, P., Guerriero, F., & Pinto, E. (2011). A heuristic approach for resource constrained project scheduling with uncertain activity durations. Computers & Operations Research, 38, 1305–1318.CrossRefGoogle Scholar
  10. Buss, A. H., & Rosenblatt, M. J. (1997). Activity delay in stochastic project networks. Operations Research, 45(1), 126–139.CrossRefGoogle Scholar
  11. Chapman, C., & Ward, S. (2000). Estimation and evaluation of uncertainty: A minimalist first pass approach. International Journal of Project Management, 18, 369–383.CrossRefGoogle Scholar
  12. Chtourou, H., & Haouari, M. (2008). A two-stage-priority-rule-based algorithm for robust resource-constrained project scheduling. Computers & Industrial Engineering, 55, 183–194.CrossRefGoogle Scholar
  13. 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
  14. Creemers, S., Leus, R., & Lambrecht, M. (2010). Scheduling Markovian PERT networks to maximize the net present value. Operations Research Letters, 38(1), 51–56.CrossRefGoogle Scholar
  15. Dawood, N. (1998). Estimating project and activity duration: A risk management approach using network analysis. Construction Management and Economics, 16, 41–48.CrossRefGoogle Scholar
  16. Deblaere, F. (2010). Resource constrained project scheduling under uncertainty. Ph.D. thesis, Department of Applied Economics, KU Leuven, Belgium.Google Scholar
  17. Deblaere, F., Demeulemeester, E., & Herroelen, W. (2011). Proactive policies for the stochastic resource-constrained project scheduling problem. European Journal of Operational Research, 214(2), 308–316.CrossRefGoogle Scholar
  18. Demeulemeester, E., & Herroelen, W. (2002). Project scheduling: A research handbook. Boston: Kluwer Academic Publishers.Google Scholar
  19. Escudero, L. F., Kamesam, P. V., King, A. J., & Wets, R. J. B. (1993). Production planning via scenario modelling. Annals of Operations Research, 43, 311–335.Google Scholar
  20. 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
  21. Feo, T. A., & Resende, M. G. C. (1995). Greedy randomized adaptive search procedures. Journal of Global Optimization, 6(2), 109–133.CrossRefGoogle Scholar
  22. Fernandez, A. A., Armacost, R. L., & Pet-Edwards, J. (1996). The role of the non-anticipativity constraint in commercial software for stochastic project scheduling. Computers and Industrial Engineering, 31, 233–236.CrossRefGoogle Scholar
  23. Fernandez, A. A., Armacost, R. L., & Pet-Edwards, J. (1998). Understanding simulation solutions to resource constrained project scheduling problems with stochastic task durations. Engineering Management Journal, 10, 5–13.CrossRefGoogle Scholar
  24. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.Google Scholar
  25. Graham, R. L. (1966). Bounds on multiprocessing timing anomalies. Bell System Technical Journal, 45, 1563–1581.CrossRefGoogle Scholar
  26. Hartmann, S., & Kolisch, R. (2000). Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem. European Journal of Operational Research, 127, 394–407.CrossRefGoogle Scholar
  27. Holland, H. J. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.Google Scholar
  28. Igelmund, G., & Radermacher, F. J. (1983). Preselective strategies for the optimization of stochastic project networks under resource constraints. Networks, 13, 1–28.CrossRefGoogle Scholar
  29. Kolisch, R. (1996a). Efficient priority rules for the resource-constrained project scheduling problem. Journal of Operations Management, 14, 172–192.CrossRefGoogle Scholar
  30. Kolisch, R. (1996b). Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation. European Journal of Operational Research, 90, 320–333.CrossRefGoogle Scholar
  31. Kolisch, R., & Sprecher, A. (1996). PSPLIB—A project scheduling problem library. European Journal of Operational Research, 96, 205–216.CrossRefGoogle Scholar
  32. Kulkarni, V. G., & Adlakha, V. G. (1986). Markov and Markov-regenerative PERT networks. Operations Research, 34(5), 769–781.CrossRefGoogle Scholar
  33. Lambrechts, O. (2007). Robust project scheduling subject to resource breakdowns. Ph.D. thesis, KU Leuven, Belgium.Google Scholar
  34. Leus, R. (2003). The generation of stable project plans. Ph.D. thesis, Department of Applied Economics, KU Leuven, Belgium.Google Scholar
  35. Leus, R., & Herroelen, W. (2004). Stability and resource allocation in project planning. IIE Transactions, 36(7), 667–682.CrossRefGoogle Scholar
  36. 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
  37. Li, K. Y., & Willis, R. J. (1992). An iterative scheduling technique for resource-constrained project scheduling. European Journal of Operational Research, 56, 370–379.CrossRefGoogle Scholar
  38. Malcolm, D. G., Rosenbloom, J. M., Clark, C. E., & Fazar, W. (1959). Application of a technique for research and development program evaluation. Operations Research, 7, 646–669.CrossRefGoogle Scholar
  39. Möhring, R. H. (2000). Scheduling under uncertainty: Optimizing against a randomizing adversary. In Lecture Notes in Computer Science (Vol. 1913/2000), pp. 651–670.Google Scholar
  40. Möhring, R. H., & Radermacher, F. J. (1989). The order-theoretic approach to scheduling: The stochastic case. In R. Slowinski, J. Weglarz (Eds.), Advances in Project Scheduling, chapter III.4. Elsevier.Google Scholar
  41. Möhring, R., Radermacher, F., & Weiss, G. (1984). Stochastic scheduling problems I—General strategies. ZOR: Zeitschrift für Operations Research, 28, 193–260.Google Scholar
  42. Neumann, K., Schwindt, C., & Zimmermann, J. (2006). Project scheduling with time windows. Berlin: Springer.Google Scholar
  43. Özdamar, L., & Ulusoy, G. (1996). A note on an iterative forward/backward scheduling technique with reference to a procedure by Li and Willis. European Journal of Operational Research, 89, 400–407.CrossRefGoogle Scholar
  44. Pinedo, M. L. (2008). Scheduling: Theory, algorithms, and systems. Berlin: Springer.Google Scholar
  45. Project Management Institute. (2013). A guide to the project management body of knowledge (PMBOK \(^{\textregistered }\) Guide). Project Management Institute Inc.Google Scholar
  46. Radermacher, F. J. (1981). Cost-dependent essential systems of ES-strategies for stochastic scheduling problems. Methods of Operations Research, 42, 17–31.Google Scholar
  47. Radermacher, F. J. (1985). Scheduling of project networks. Annals of Operations Research, 4, 227–252.CrossRefGoogle Scholar
  48. Radermacher, F. J. (1986). Analytical vs. combinatorial characterizations of well-behaved strategies in stochastic scheduling. Methods of Operations Research, 53, 467–475.Google Scholar
  49. Rockafellar, R. T., & Wets, R. J. B. (1991). Scenarios and policy aggregation in optimization under uncertainty. Mathematics of Operations Research, 16, 119–147.CrossRefGoogle Scholar
  50. Saliby, E. (1990). Descriptive sampling: A better approach to Monte Carlo simulation. Journal of the Operational Research Society, 41, 1133–1142.CrossRefGoogle Scholar
  51. Schatteman, D., Herroelen, W., Van de Vonder, S., & Boone, A. (2008). A methodology for integrated risk management and proactive scheduling of construction projects. Journal of Construction Engineering and Management, 134, 885–893.Google Scholar
  52. Shtub, A., Bard, J. F., & Globerson, S. (2005). Project management: Processes, methodologies, and economics. New Jersey: Pearson Prentice Hall.Google Scholar
  53. Sprecher, A. (2000). Scheduling resource-constrained projects competitively at modest memory requirements. Management Science, 46, 710–723.CrossRefGoogle Scholar
  54. Stork, F. (2001). Stochastic resource-constrained project scheduling. Ph.D. thesis, Technische Universität Berlin.Google Scholar
  55. Valls, V., Ballestín, F., & Quintanilla, S. (2005). Justification and RCPSP: A technique that pays. European Journal of Operational Research, 165, 375–386.CrossRefGoogle Scholar
  56. 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
  57. Van de Vonder, S., Demeulemeester, E., Herroelen, W., & Leus, R. (2005). The use of buffers in project management: The trade-off between stability and makespan. International Journal of Production Economics, 97, 227–240.CrossRefGoogle Scholar
  58. Wang, J. (2004). A fuzzy robust scheduling approach for product development projects. European Journal of Operational Research, 152, 180–194.CrossRefGoogle Scholar
  59. Wets, R. J. B. (1989). The aggregation principle in scenario analysis and stochastic optimization, volume F51 of Nato ASI Series (pp. 91–113). Springer.Google Scholar
  60. Yu, G., & Qi, X. (2004). Disruption management—Framework, models and applications. New Jersey: World Scientific.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.IESEG School of ManagementLilleFrance
  2. 2.ORSTATKU LeuvenLeuvenBelgium
  3. 3.Research Centre for Operations ManagementKU LeuvenLeuvenBelgium

Personalised recommendations