Skip to main content
Log in

A discrete-event simulation approach with multiple-comparison procedure for stochastic resource-constrained project scheduling

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Aiming to minimize the average project duration, a discrete-event simulation (DES) approach with multiple-comparison procedure is presented to solve the stochastic resource-constrained project scheduling problem (SRCPSP). The simulation model of SRCPSP is composed of a resource management model and a project process model, where the resource management model is used to administrate resources of the project, and the project process model based on an extended-directed-graph is proposed to describe the precedence constraints and resource constraints in SRCPSP. A simplified simulation strategy based on activity scanning method is used in the simulation model to generate feasible schedules of the problem. A multiple-comparison procedure based on the common random numbers is adopted to compare the multiple scheduling alternatives obtained from the stochastic simulation model and provide more information to select the optimal scheduling alternative. The cases are given to compare with other methods for the same SRCPSP from literature and show that the simulation tool by utilizing DES with a statistical method improves the efficiency of simulation in stochastic project planning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Brucker P, Drexl A, Möhring R, Neumann K, Pesch E (1999) Resource-constrained project scheduling: notation, classification, models, and methods. Eur J Oper Res 112:3–41

    Article  MATH  Google Scholar 

  2. Márkus A, Váncza J, Kis T, Kovács A (2003) Project scheduling approach to production planning. CIRP Ann Manuf Technol 52(1):359–362

    Article  Google Scholar 

  3. Goswami M, Tiwari MK, Mukhopadhyay SK (2008) An integrated approach to solve tool-part grouping, job allocation and scheduling problems in a flexible manufacturing system. Int J Adv Manuf Technol 35(11–12):1145–1155

    Article  Google Scholar 

  4. Nie L, Shao XY, Gao L, Li WD (2010) Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems. Int J Adv Manuf Technol 50:729–747

    Article  Google Scholar 

  5. Alcaraz J, Maroto C (2001) A robust genetic algorithm for resource allocation in project scheduling. Ann Oper Res 102:83–109

    Article  MathSciNet  MATH  Google Scholar 

  6. Kumanan S, Jegan Jose G, Raja K (2006) Multi-project scheduling using an heuristic and a genetic algorithm. Int J Adv Manuf Technol 31:360–366

    Article  Google Scholar 

  7. Agarwal R, Tiwari MK, Mukherjee SK (2007) Artificial immune system based approach for solving resource constraint project scheduling problem. Int J Adv Manuf Technol 34:584–593

    Article  Google Scholar 

  8. Shukla SK, Son YJ, Tiwari MK (2008) Fuzzy-based adaptive sample-sort simulated annealing for resource-constrained project scheduling. Int J Adv Manuf Technol 36:982–995

    Article  Google Scholar 

  9. Ying KC, Lin SW, Lee ZJ (2009) Hybrid-directional planning: improving improvement heuristics for scheduling resource-constrained projects. Int J Adv Manuf Technol 41:358–366

    Article  Google Scholar 

  10. Zhou YM, Guo QS, Gan RW (2009) Improved ACO algorithm for resource-constrained project scheduling problem. 2009 International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, China, pp 358-365

  11. Nagalakshmi MR, Tripathi M, Shukla N, Tiwari MK (2009) Vehicle routing problem with stochastic demand (VRPSD): optimisation by neighbourhood search embedded adaptive ant algorithm (ns-AAA). Int J Comp Aided Engineering Technol 1(3):300–321

    Article  Google Scholar 

  12. Herroelen W, Leus R (2005) Project scheduling under uncertainty: survey and research potentials. Eur J Oper Res 165:289–306

    Article  MATH  Google Scholar 

  13. Ballestín F, Leus R (2009) Resource-constrained project scheduling for timely project completion with stochastic activity durations. Prod Oper Manag 18(4):459–474

    Article  Google Scholar 

  14. Stork F (2000) Branch-and-bound algorithms for stochastic resource-constrained project scheduling. Research Report No. 702/2000, Technische Universität Berlin

  15. Golenko-Ginzburg D, Gonik A (1997) Stochastic network project scheduling with non-consumable limited resources. Int J Prod Econ 48:29–37

    Article  Google Scholar 

  16. Tsai YW, Gemmill DD (1998) Using tabu search to schedule activities of stochastic resource-constrained projects. Eur J Oper Res 111:129–141

    Article  MATH  Google Scholar 

  17. Li HT (2009) Constraint programming based approximate dynamic programming for deterministic and stochastic resource-constrained project scheduling. Project Final Report, 2009. University of Missouri, St. Louis

    Google Scholar 

  18. Ballestín F (2007) When it is worthwhile to work with the stochastic RCPSP? J Sched 10:153–166

    Article  MathSciNet  MATH  Google Scholar 

  19. Ashtiani B, Leus R, Aryanezhad MB (2011) New competitive results for the stochastic resource-constrained project scheduling problem: exploring the benefits of pre-processing. J Sched 14(2):157–171

    Article  MathSciNet  MATH  Google Scholar 

  20. Badiru AB (1991) A simulation approach to PERT network analysis. Simulation 57:245–255

    Article  Google Scholar 

  21. Reddy JP, Kumanan S, Chetty OVK (2001) Application of Petri nets and a genetic algorithm to multi-mode multi-resource constrained project scheduling. Int J Adv Manuf Technol 17:305–314

    Article  Google Scholar 

  22. Zhang H, Li H (2004) Simulation-based optimization for dynamic resource allocation. Autom Constr 13:409–420

    Article  Google Scholar 

  23. Zhang H, Tam CM, Li H, Shi JJ (2006) Particle swarm optimization-supported simulation for construction operations. J Constr Eng Manag 132(12):1267–1274

    Article  Google Scholar 

  24. Zhang H, Li H, Tam CM (2004) Fuzzy discrete-event simulation for modeling uncertain activity duration. Eng Constr Archit Manag 11(6):426–437

    Article  Google Scholar 

  25. Swisher JR, Jacobson SH (1999) A survey of ranking, selection, and multiple comparison procedures for discrete-event simulation. Proceedings of the 1999 Winter Simulation Conference P. A. Farrington, H. B. Nembhard, D. T. Sturrock, and G. W. Evans, eds, pp 492–501

  26. Rinott Y (1978) On two-stage selection procedures and related probability-inequalities. Comm Stat-Theory and Methods 7(8):799–811

    Article  MathSciNet  Google Scholar 

  27. Dudewicz EJ, Dalal SR (1975) Allocation of observations in ranking and selection with unequal variances. Indian J Stat 37B(1):28–78

    MathSciNet  Google Scholar 

  28. Clark GM, Yang WN (1986) A Bonferroni selection procedure when using common random numbers with unknown variances. ACM, New York, pp 313–315, Proceedings of the 1986 Winter Simulation Conference

    Google Scholar 

  29. Nelson BL, Matejcik FJ (1995) Using common random numbers for indifference-zone selection and multiple comparisons in simulation. Manag Sci 41(12):1935–1945

    Article  MATH  Google Scholar 

  30. Heikes RG, Montgomery DC, Rardin RL (1976) Using common random numbers in simulation experiments—an approach to statistical analysis. Simulation 27:81–85

    Article  Google Scholar 

  31. Nelson BL, Swann J, Goldsman D, Song W (2001) Simple procedures for selecting the best simulated system when the number of alternatives is large. Oper Res 49(6):950–963

    Article  Google Scholar 

  32. Chick SE, Inoue K (2001) New procedures to select the best simulated system using common random numbers. Manag Sci 47(8):1133–1149

    Article  Google Scholar 

  33. Fu MC, Hu JQ, Chen CH, Xiong XP (2007) Simulation allocation for determining the best design in the presence of correlated sampling. INFORMS J Comput 19(1):101–111

    Article  MathSciNet  MATH  Google Scholar 

  34. Hooper JW (1986) Strategy-related characteristics of discrete-event languages and models. Simulation 46:153–159

    Article  Google Scholar 

  35. Martinez JC, Ioannou PG (1999) General-purpose systems for effective construction simulation. J Constr Eng Manag 125(4):265–276

    Article  Google Scholar 

  36. Davis EW, Patterson JH (1975) A comparison of heuristic and optimum solutions in resource-constrained project scheduling. Manag Sci 21(8):944–955

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Junfeng Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, S., Jia, Y. & Wang, J. A discrete-event simulation approach with multiple-comparison procedure for stochastic resource-constrained project scheduling. Int J Adv Manuf Technol 63, 65–76 (2012). https://doi.org/10.1007/s00170-011-3885-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-011-3885-2

Keywords

Navigation