Dynamic Resource Constrained Multi-Project Scheduling Problem with Weighted Earliness/Tardiness Costs

  • M. Berke Pamay
  • Kerem Bülbül
  • Gündüz UlusoyEmail author
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 200)


In this study, a conceptual framework is given for the dynamic resource-constrained multi-project scheduling problem with weighted earliness/tardiness costs (DRCMPSPWET), and a mathematical programming formulation of the problem is provided. In DRCMPSPWET, a project arrives on top of an existing project portfolio, and a due date has to be quoted for the new project while minimizing the costs of schedule changes. The objective function consists of the weighted earliness/tardiness costs of the activities of the existing projects in the current baseline schedule plus a term that increases linearly with the anticipated completion time of the new project. An iterated local search (LS)-based approach is developed for large instances of this problem. In order to analyze the performance and behavior of the proposed method, a new multi-project data set is created by controlling the total number of activities, the due date tightness, the due date range, the number of resource types, and the completion time factor in an instance. A series of computational experiments are carried out to test the performance of the LS approach. Exact solutions are provided for small instances. The results indicate that the LS heuristic performs well in terms of both solution quality and solution time.


Local Search Completion Time Activity List Iterate Local Search Project Portfolio 
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.



We gratefully acknowledge the support given by The Scientific and Technological Research Council of Turkey (TUBITAK) through Project Number MAG 109M571.


  1. Adams, J., Balas, E., & Zawack, D. (1988). The shifting bottleneck procedure for job shop scheduling. Management Science, 34, 391–401.Google Scholar
  2. Alvarez-Valdes, R., & Tamarit, J. M. (1989). Heuristic algorithms for resource constrained project scheduling: A review and empirical analysis. In Slowinski and Weglarz (Eds.), Advances in project scheduling (pp. 113–134). The Netherlands: Elsevier.Google Scholar
  3. Artigues, C., & Roubellat, F. (2000). A polynomial activity insertion algorithm in a multi-resource schedule with cumulative constraints and multiple modes. European Journal of Operational Research, 127(2), 297–316.Google Scholar
  4. 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, 157–171.Google Scholar
  5. Ballestin, F., & Trautman, N. (2008). An iterated-local-search heuristic for the resource-constrained weighted earliness-tardiness project scheduling problem. International Journal of Production Research, 46, 6231–6249.Google Scholar
  6. Badiru, A. B., & Pulat, P. S. (1995). Comprehensive project management. New Jersey: Prentice Hall PTR.Google Scholar
  7. Bulbul, K., & Kaminsky, P. (2010). A linear programming-based general method for job shop scheduling. Journal of Scheduling, in press.–4.
  8. Demeulemeester, E., & Herroelen, W. (2002). Project scheduling. A research handbook. Dordrecht: Kluwer.Google Scholar
  9. Demirkol, E., Mehta, S., & Uzsoy, R. (1997). A computational study of shifting bottleneck procedures for shop scheduling problems. Journal of Heuristics, 3(2), 111–137.Google Scholar
  10. El Sakkout, H., & Wallace, M. (2000). Probe backtrack search for minimal perturbation in dynamic scheduling. Constraints, 5(4), 359–388.Google Scholar
  11. Herbots, J., Herroelen, W., & Leus, R. (2007). Dynamic order acceptance and capacity planning on a single bottleneck resource. Naval Research Logistics, 54(8), 874–889.Google Scholar
  12. Herroelen, W., & Leus, R. (2005) Project scheduling under uncertainty: Survey and research potentials. European Journal of Operational Research, 165, 289–306.Google Scholar
  13. Kanet, J., & Sridharan, V. (2000). Scheduling with inserted idle time: problem taxonomy and literature review. Operations Research, 48(1), 99–110.Google Scholar
  14. Kolisch, R. (1996). Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation. European Journal of Operational Research, 90(2), 320–333.Google Scholar
  15. Kolisch, R., Sprecher, A., & Drexl, A. (1995). Characterization and generation of a general class of resource-constrained project scheduling problems. Management Science, 41, 1693–1703.Google Scholar
  16. Lenstra, J., Rinnooy Kan, A., & Brucker, P. (1977). Complexity of machine scheduling problems. Annals of Discrete Mathematics, 1, 343–362.Google Scholar
  17. Mason, S., Fowler, J., & Carlyle, W. (2002). A modified shifting bottleneck heuristic for minimizing total weighted tardiness in complex job shops. Journal of Scheduling, 5(3), 247–262.Google Scholar
  18. Mastor, A. (1970). An experimental investigation and comparative evaluation of production line balancing techniques. Management Science, 16(11), 728–746, 1970.Google Scholar
  19. Nanobe, K., & Ibaraki, T. (2006). A metaheuristic approach to the resource constrained project scheduling with variable activity durations and convex cost functions. In Jozefowska and Weglarz (Eds.), Perspectives in Modern Project Scheduling, International Series in Operations Research & Management Science, 92, Springer, Berlin, pp. 225–248.Google Scholar
  20. Neumann, K., Schwindt, C., & Zimmermann, J. (2003). Project Scheduling with Time Windows and Scarce Resources, 2nd ed.. Berlin: Springer Verlag.Google Scholar
  21. Pamay, M. B. (2011). A linear programming based method for the resource constrained multi-project scheduling problem with weighted earliness/tardiness costs, MSc thesis, Sabanci University, Turkey, 2011.
  22. Payne, J. H. (1995). Management of multiple simultaneous projects: A state-of-the-art review. International Journal of Project Management, 13, 163–168.Google Scholar
  23. Pinedo, M., & Singer, M. A (1999). Shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop. Naval Research Logistics, 46(1), 1–17.Google Scholar
  24. Schwindt, C. (1995). ProGen/max: A new problem generator for different resource-constrained project scheduling problems with minimal and maximal time lags, Technical Report WIOR 449, University of Karlsruhe.Google Scholar
  25. Singer, M. (2001). Decomposition methods for large job shops. Computers & Operations Research, 28(3), 193–207.Google Scholar
  26. Vanhoucke, M., Demeulemeester, E., & Herroelen, W. (1999). An exact procedure for the unconstrained weighted earliness tardiness project scheduling problem, Research Report 9907, Department of Applied Economics, Katholieke Universiteit Leuven, no. 9907.Google Scholar
  27. Vanhoucke, M., Demeulemeester, E., & Herroelen, W. (2001). An exact procedure for the resource-constrained weighted earliness tardiness project scheduling problem. Annals of Operations Research, 102, 179–196.Google Scholar
  28. Vanhoucke, M. (2002). Optimal due date assignment in project scheduling, Working Paper, Ghent University and Vleric Luevent Gent Management School, no. 159.Google Scholar
  29. Vanhoucke, M., Demeulemeester, E., & Herroelen, W. (2003). RanGen: A random network generator for activity-on-the-node networks, Tech. Rep., Department of Applied Economics, Katholieke Universiteit Leuven.Google Scholar
  30. Yang, K. K., & Sum, C. (1997). An evaluation of due date, resource allocation, project release, and activity scheduling rules in a multi-project environment. European Journal of Operational Research, 13, 139–154.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • M. Berke Pamay
    • 1
  • Kerem Bülbül
    • 1
  • Gündüz Ulusoy
    • 1
    Email author
  1. 1.Manufacturing Systems Engineering ProgramSabancı UniversityIstanbulTurkey

Personalised recommendations