Skip to main content
Log in

Artificial immune system based approach for solving resource constraint project scheduling problem

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

Abstract

In this paper, resource-constrained project scheduling problem (RCPSP) is discussed with an objective of minimizing the makespan of a project. Due to its universality, it has a variety of applications as in manufacturing, production planning, project management and elsewhere. It is a well known computationally complex problem, thus warrants the application of heuristics techniques or AI based optimization tools to achieve optimal or near optimal solution in real time. In this research, the artificial immune system (AIS) approach is proposed to solve the aforementioned problem. It exploits the beauty of learning and memory acquisition to ensure the convergence with faster rate. During extensive computational experiment, it is found that the performance of the AIS algorithm on a well known data set of resource-constrained project scheduling problem is superior as compared to GA, fuzzy-GA, LFT, GRU, SIO, MINSLK, RSM, RAN, and MJP based approaches.

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. Kim KW, Gen M, Yamajaki G (2003) Hybrid genetic algorithm with fuzzy logic for resource constrained project scheduling. Appl Soft Comput 2/3F:174–188

    Article  Google Scholar 

  2. Davis EW (1966) Resource allocation in project network models - a survey. J Ind Eng 17:177–188

    Google Scholar 

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

    Article  MATH  Google Scholar 

  4. Ozdamar L, Ulusoy G (1995) A survey on the resource-constrained project scheduling problem. IIE Trans 27:574–586

    Article  Google Scholar 

  5. Boctor FF (1993) Heuristics for scheduling projects with resource restrictions and several resource-duration modes. Int J Prod Res 31:2547–2558

    Article  Google Scholar 

  6. Chang YL, Matsuo H,Sullivan RS (1989) A bottleneck-based beam search for job scheduling in a flexible manufacturing system. Int J Prod Res 27:1949–1961

    Article  Google Scholar 

  7. Demeulemeester EL, Herroelen WS (1996) Modelling setup times, process batches and transfer batches using activity network logic. Eur J OperRes 89:355–365

    Article  MATH  Google Scholar 

  8. Drexl A, Salewski F(1997) Distribution requirements and compactness constraints in school time tabling. Eur J Oper Res 102:193–214

    Article  MATH  Google Scholar 

  9. Schrage L (1970) Solving resource-constrained network problems by implicit enumeration - Non preemptivecase. Oper Res 18:263–278

    Article  MATH  MathSciNet  Google Scholar 

  10. Sprecher A (1994) Resource-constrained project scheduling: exact methods for the multi-mode case. Number 409 in lecture notes in economics and mathematical systems. Springer, Berlin Heidelberg NewYork

    Google Scholar 

  11. Sprecher A (1999) A competitive exact algorithm for assembly line balancing. Int J Prod Res 37:1787–1816

    Article  MATH  Google Scholar 

  12. de Reyck B, Herroelen WS (1995) Assembly line balancing by resource-constrained project scheduling- acritical appraisal. Technical Report 9505, Katholieke Universiteit Leuven, Belgium

  13. PSPLIB (2000)

  14. De Castero LN, Von ZubenFJ (2002) Learning and optimization using the clonal selection principle. IEEETrans Evolutionary computation, Special Issue on Artif Immune Syst 6(3):239–251

    Google Scholar 

  15. Khoo LP, Situmdrang TD(2003) Solving the assembly configuration problem for modular products using an immune algorithm approach. Int J Prod Res 41(15):3419–3434

    Article  MATH  Google Scholar 

  16. Anderson EJ, Ferris MC (1994) Genetic algorithm for combinatorial optimisation: assembly line balancing problem. ORSA J Comput 6:161–173

    MATH  Google Scholar 

  17. BaarT, Brucker P, Knust S (1999) Tabu-search algorithms and lower bounds for the resource constrained project scheduling problem. In: Voss et al. (eds.) Meta-heuristics, Kluwer, Dordrecht, pages 1–8

    Google Scholar 

  18. Bouleimen K, Lecocq H (1998) A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem. Technical report, Universit´ e de Li` ege, Belgium

  19. Hartmann S (1998) A competitive genetic algorithm for resource-constrained project scheduling. NavRes Logist 45:733–750

    Article  MATH  MathSciNet  Google Scholar 

  20. Leon VJ, Ramamoorthy B (1995) Strength and adaptability of problem-space based neighborhoods for resource-constrained scheduling. OR Spektrum 17:173–182

    Article  MATH  Google Scholar 

  21. Kolisch R, Drexl A (1996) Adaptive search for solving hard project scheduling problems. Nav Res Logist 43:23–40

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. K. Tiwari.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Agarwal, R., Tiwari, M.K. & Mukherjee, S.K. Artificial immune system based approach for solving resource constraint project scheduling problem. Int J Adv Manuf Technol 34, 584–593 (2007). https://doi.org/10.1007/s00170-006-0631-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-006-0631-2

Keywords

Navigation