Skip to main content
SpringerLink
Log in

An improved particle swarm optimization for the resource-constrained project scheduling problem

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In this paper, an improved particle swarm optimization (PSO) algorithm is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The algorithm treats the solutions of RCPSP as particle swarms and employs a double justification skill and a move operator for the particles, in association with rank-priority-based representation, greedy random search, and serial scheduling scheme, to execute the intelligent updating process of the swarms to search for better solutions. The integration combines and overhauls the characteristics of both PSO and RCPSP, resulting in enhanced performance. The computational experiments are subsequently conducted to set the adequate parameters and compare the proposed algorithm with other approaches. The results suggest that the proposed PSO algorithm augments the performance by 9.26, 16.17, and 10.45 % for the J30, J60, and J120 instances against the best lower bound-based PSO currently available, respectively. Moreover, the proposed algorithms demonstrate obvious advantage over other proposals in exploring solutions for large-scale RCPSP problems such as the J60 and J120 instances.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Herroelen W, Demeulemeester E, De Reyck B (1999) An integrated classification scheme for resource scheduling. Research Report 9905, Department of Applied Economics, K.U. Leuven

    Google Scholar 

  2. 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 

  3. Zamani R (2010) A parallel complete anytime procedure for project scheduling under multiple resource constraints. Int J Adv Manuf Technol 50:353–362

    Article  Google Scholar 

  4. Pritsker AAB, Watters LJ, Wolfe PM (1969) Multiproject scheduling with limited resources: a zero-one programming approach. Manag Sci 16(1):93–108

    Article  Google Scholar 

  5. Kaplan L (1996) Resource-constrained project scheduling with preemption of jobs. Dissertation, University of Michigan

  6. Klein R (2000) Scheduling of resource-constrained projects. Kluwer, Boston

    Book  MATH  Google Scholar 

  7. Alcaraz J, Maroto C, Ruiz R (2003) Solving the multi-mode resource-constrained project scheduling problem with genetic algorithms. J Oper Res Soc 54(6):614–626

    Article  MATH  Google Scholar 

  8. Mingozzi A, Maniezzo V, Ricciardelli S, Bianco L (1998) An exact algorithm for the resource-constrained project scheduling problem based on a new mathematical formulation. Manag Sci 44(5):714–729

    Article  MATH  Google Scholar 

  9. Kone O, Artigues C, Lopez P, Mongeau M (2011) Event-based MILP models for resource-constrained project scheduling problems. Comput Oper Res 38(1):3–13

    Article  MathSciNet  MATH  Google Scholar 

  10. Brucker P, Knust S, Schoo A, Thiele O (1998) A branch and bound algorithm for the resource-constrained project scheduling problem1. Eur J Oper Res 107(2):272–288

    Article  MATH  Google Scholar 

  11. Demeulemeester E, Herroelen W (1995) New benchmark results for the resource-constrained project scheduling problem. In: Proceedings of the INFORMS Singapore international meeting, Singapore

  12. Heilmann R (2003) A branch-and-bound procedure for the multi-mode resource-constrained project scheduling problem with minimum and maximum time lags. Eur J Oper Res 144(2):348–365

    Article  MathSciNet  MATH  Google Scholar 

  13. Blazewicz J, Lenstra JK, Rinooy Kan AHG (1983) Scheduling subject to resource constraints: classification and complexity. Discrete Appl Math 5:11–24

    Article  MathSciNet  MATH  Google Scholar 

  14. Icmeli O, Erenguc SS, Zappe CJ (1993) Project scheduling problems: a survey. Int J Oper Prod Manag 13(11):80–91

    Article  Google Scholar 

  15. Özdamar L, Ulusoy G (1995) A survey on the resourceconstrained project scheduling problem. IIE Trans 27:574– 586

    Article  Google Scholar 

  16. Herroelen W, Demeulemeester E, De Reyck B (1998) Resource-constrained project scheduling—a survey of recent developments. Comput Oper Res 25(4):279–302

    Article  MathSciNet  MATH  Google Scholar 

  17. Kolisch R, Padman R (1997) An integrated survey of deterministic project scheduling. Technical Report 463, Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel

  18. 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(1):3–41

    Article  MATH  Google Scholar 

  19. Kolisch R, Hartmann S (1999) Heuristic algorithms for the resource-constrained project scheduling problem: classification and computational analysis. In: Weglarz J (ed) Project scheduling-recent models, algorithms and applications. Kluwer Academic, Boston, pp 147–178

    Google Scholar 

  20. Kolisch R, Hartmann S (2006) Experimental investigation of heuristics for resource-constrained project scheduling: an update. Eur J Oper Res 174(1):23–37

    Article  MATH  Google Scholar 

  21. Hartmann S, Kolisch R (2000) Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem. Eur J Oper Res 127(2):394–407

    Article  MATH  Google Scholar 

  22. Hartmann S (2002) A self-adapting genetic algorithm for project scheduling under resource constraints. Nav Res Logist 49:433–448

    Article  MATH  Google Scholar 

  23. Alcaraz J, Maroto C, Ruiz R (2003) Solving the multi-mode resource-constrained project scheduling problem with genetic algorithms. J Oper Res Soc 54(6):614–626

    Article  MATH  Google Scholar 

  24. Valls V, Ballestin F, Quintanilla S (2004) A population-based approach to the resource-constrained project scheduling problem. Ann Oper Res 131:305–324

    Article  MathSciNet  MATH  Google Scholar 

  25. Valls V, Ballestin F, Quintanilla S (2008) A hybrid genetic algorithm for the resource-constrained project scheduling problem. Eur J Oper Res 185(2):495–508

    Article  MathSciNet  MATH  Google Scholar 

  26. Nonobe K, Ibaraki T (2002) Formulation and tabu search algorithm for the resource constrained project scheduling problem. In: Ribeiro CC, Hansen P (eds) Essays and surveys in metaheuristics. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp 557–588

    Google Scholar 

  27. Bouleimen K, Lecocq H (2003) A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. Eur J Oper Res 149(2):268–281

    Article  MathSciNet  MATH  Google Scholar 

  28. Zamani R (2004) An efficient time-windowing procedure for scheduling projects under multiple resource constraints. OR Spectrum 26:423–444

    Article  MathSciNet  MATH  Google Scholar 

  29. Chen RM, Wu CL, Wang CM, Lo ST (2010) Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB. Expert Syst Appl 37(3):1899–1910

    Article  Google Scholar 

  30. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks. Perth, Australia, pp 1942–1948

    Book  Google Scholar 

  31. Zhang H, Li XD, Li H, Huang FL (2005) Particle swarm optimization-based schemes for resource-constrained project scheduling. Autom Constr 14(3):393–404

    Article  Google Scholar 

  32. Zhang H, Li H, Tam CM (2006) Particle swarm optimization for resource-constrained project scheduling. Int J Proj Manag 24:83–92

    Article  Google Scholar 

  33. Jarboui B, Damak N, Siarry P, Rebai A (2008) A combinatorial particle swarm optimization for solving multi-mode resource-constrained project scheduling. Appl Math Comput 105(1):299–308

    Article  MathSciNet  Google Scholar 

  34. Lu M, Lam HC, Dai F (2008) Resource-constrained critical path analysis based on discrete event simulation and particle swarm optimization. Autom Constr 17(6):670–681

    Article  Google Scholar 

  35. Demeulemeester EL, Herroelen WS (2002) Project scheduling: a research handbook. Kluwer Academic, Boston

    MATH  Google Scholar 

  36. Kennedy J, Eberhart RC, Shi YH (2001) Swarm intelligence: collective, adaptive. Morgan Kaufmann, San Francisco

    Google Scholar 

  37. Trelea IC (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85(6):317–325

    Article  MathSciNet  MATH  Google Scholar 

  38. Kolisch R (1996) Serial and parallel resource-constrained project scheduling methods revisited: theory and computation. Eur J Oper Res 90(2):320–333

    Article  MATH  Google Scholar 

  39. Valls V, Ballestin F, Quintanilla MS (2005) Justification and RCPSP: a technique that pays. Eur J Oper Res 165:375–386

    Article  MATH  Google Scholar 

  40. Kolisch R, Sprecher A, Drexl A (1995) Characterization and generation of a general class of resource-constrained project scheduling problems. Manag Sci 41:1693–1703

    Article  MATH  Google Scholar 

  41. Chen F, Wand L (2011) An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem. Comput Oper Res 39(5):890–901

    Google Scholar 

  42. Schirmer A (2000) Case-based reasoning and improved adaptive search for project scheduling. Nav Res Logist 47:201–222

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Management Engineering, Korea University, Anam-dong, Seoul, 136-713, South Korea

    Qiong Jia & Yoonho Seo

Authors
  1. Qiong Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoonho Seo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yoonho Seo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jia, Q., Seo, Y. An improved particle swarm optimization for the resource-constrained project scheduling problem. Int J Adv Manuf Technol 67, 2627–2638 (2013). https://doi.org/10.1007/s00170-012-4679-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-012-4679-x

Keywords

Search

Navigation