Skip to main content
Log in

Solving the Multi-Mode Resource Availability Cost Problem in Project Scheduling Based on Modified Particle Swarm Optimization

  • Research Article - Systems Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

This paper proposes a model for multi-mode resource availability cost problem (MMRACP) in project scheduling which minimizes the resource availability cost required to finish all activities in a project at a given project deadline. Precedence relations exist among the activities of the project in the model. Furthermore, renewable and nonrenewable resources are both considered. MMRACP is a nondeterministic polynomial time hard (NP-hard) problem, as a result it is very difficult to use an exact method to solve it. For solving MMRACP, we developed a modified particle swarm optimization method combined with path relinking procedure and designed a heuristic algorithm to improve the fitness of the solution. At the end, a computational experiment including 180 instances was designed to test the performance of the modified particle swarm optimization. Comparative computational results show that the modified particle swarm optimization is very effective in solving MMRACP.

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. Möhring R.H.: Minimizing costs of resource requirements in project networks subject to a fix completion time. Oper. Res. 32(1), 89–120 (1984)

    Article  MATH  Google Scholar 

  2. Yamashita D.S., Armentano V.A., Laguna M.: Scatter search for project scheduling with resource availability cost. Eur. J. Oper. Res. 169(2), 623–637 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  3. Yamashita D.S., Armentano V.A., Laguna M.: Robust optimization models for project scheduling with resource availability cost. J. Sched. 10(1), 67–76 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  4. Shadrokh S., Kianfar F.: A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty. Eur. J. Oper. Res. 181(1), 86–101 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  5. Ranjbar M., Kianfar F., Shadrokh S.: Solving the resource availability cost problem in project scheduling by path relinking and genetic algorithm. Appl. Math. Comput. 196(2), 879–888 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  6. Rodrigues S.B., Yamashita D.S.: An exact algorithm for minimizing resource availability costs in project scheduling. Eur. J. Oper. Res. 206(3), 562–568 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  7. Sprecher A., Hartmann S., Drexl A.: An exact algorithm for project scheduling with multiple modes. OR Spektrum 19(3), 195–203 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  8. Damak N., Jarboui B., Siarry P., Loukil T.: Differential evolution for solving multi-mode resource-constrained project scheduling problems. Comput. Oper. Res. 36(9), 2653–2659 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  9. Barrios A., Ballestín F., Valls V.: A double genetic algorithm for the MRCPSP/max. Comput. Oper. Res. 38(1), 22–43 (2011)

    Article  Google Scholar 

  10. Deblaere F., Demeulemeester E., Herroel W.: Reactive scheduling in the multi-mode RCPSP. Comput. Oper. Res. 38(1), 63–74 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  11. Wang L., Fang C.: An effective shuffled frog-leaping algorithm for multi-mode resource- constrained project scheduling problem. Inf. Sci. 181(20), 4804–4822 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  12. Wang L., Fang C.: An effective estimation of distribution algorithm for the multi-mode resource-constrained project scheduling problem. Comput. Oper. Res. 39(2), 449–460 (2012)

    Article  MathSciNet  Google Scholar 

  13. Speranza M.G., Vercellis C.: Hierarchical models for multi-project planning and scheduling. Eur. J. Oper. Res. 64(2), 312–325 (1993)

    Article  MATH  Google Scholar 

  14. Kennedy, J.; Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Nature Networks, pp. 1942–1948 (1995)

  15. Deng Y.M., Zheng D., Lu X.J.: Injection moulding optimisation of multi-class design variables using a PSO algorithm. Int. J. Adv. Manuf. Technol. 39(7–8), 690–698 (2008)

    Article  Google Scholar 

  16. Biswas S., Mahapatra S.S.: Modified particle swarm optimization for solving machine-loading problems in flexible manufacturing systems. Int. J. Adv. Manuf. Technol. 39(9–10), 931–942 (2008)

    Article  Google Scholar 

  17. Chen R.M.: Particle swarm optimization with justification and designed mechanisms for resource-constrained project scheduling problem. Expert Syst. Appl. 38(6), 7102–7111 (2011)

    Article  Google Scholar 

  18. Clerc M., Kennedy J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)

    Article  Google Scholar 

  19. Parsopoulos K.E., Vrahatis M.N.: On the computation of all global minimizers through particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 211–224 (2004)

    Article  MathSciNet  Google Scholar 

  20. Orosz J.E., Jacobson S.H.: Analysis of static simulated annealing algorithms. J. Optim. Theory Appl. 115(1), 165–182 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  21. Triki E., Collette Y., Siarry P.: A theoretical study on the behavior of simulated annealing leading to a new cooling schedule. Eur. J. Oper. Res. 166(1), 77–92 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  22. Shukla S.K., Son Y.J., Tiwari M.K.: Fuzzy-based adaptive sample-sort simulated annealing for resource-constrained project scheduling. Int. J. Adv. Manuf. Technol. 36(9–10), 982–995 (2008)

    Article  Google Scholar 

  23. Khorasani J.: A new heuristic approach for unit commitment problem using particle swarm optimization. Arab. J. Sci. Eng. 37(4), 1033–1042 (2012)

    Article  MathSciNet  Google Scholar 

  24. Maghsoudi M.J., Ibrahim Z., Buyamin S., Rahmat M.F.A.: Data clustering for the DNA computing readout method implemented on lightcycler and based on particle swarm optimization. Arab. J. Sci. Eng. 37(3), 697–707 (2012)

    Article  Google Scholar 

  25. Chen, S.P.; Vargas, Y.N.: Improving the performance of particle swarms through dimension reductions—a case study with locust swarms. In: 2010 IEEE Congress on Evolutionary Computation. IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)

  26. Debels D., De Reyck B., Leus R., Vanhoucke M.: A hybrid scatter search/electromagnetism meta-heuristic for project scheduling. Eur. J. Oper. Res. 169(2), 638–653 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  27. Tormos P., Lova A.: An efficient multi-pass heuristic for project scheduling with constrained resources. Int. J. Prod. Res. 41(5), 1071–1086 (2003)

    Article  MATH  Google Scholar 

  28. Project Scheduling Problem Library-PSPLIB. http://www.om-db.wi.tum.de/psplib/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jian-Jun Qi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qi, JJ., Liu, YJ., Lei, HT. et al. Solving the Multi-Mode Resource Availability Cost Problem in Project Scheduling Based on Modified Particle Swarm Optimization. Arab J Sci Eng 39, 5279–5288 (2014). https://doi.org/10.1007/s13369-014-1162-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-014-1162-z

Keywords

Navigation