Skip to main content
SpringerLink
Log in

An Artificial Bee Colony with Random Key for Resource-Constrained Project Scheduling

  • Conference paper
Life System Modeling and Intelligent Computing (ICSEE 2010, LSMS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6329))

Abstract

This paper proposes an artificial bee colony (ABC for short) algorithm with random key for resource-constrained project scheduling (RCPSP for short) in real time. Aim at resource saving by the activities, the RCPSP problem attempts to obtain a feasible schedule minimizing the makespan. We modified the artificial bee colony algorithm (named by ABC-RK) for this problem, where the problem representation was based on random key, and a heuristic priority rule to assign activities was also employed. The preliminary experimental results showed the effectiveness of the ABC-RK algorithm.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Computer Engineering Department, Erciyes University, Turkey (2005)

    Google Scholar 

  2. Karaboga, D., Akay, B.: A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation 214, 108–132 (2009)

    Article  MATH  Google Scholar 

  3. Karaboga, D., Akay, B.: Artificial Bee Colony (ABC) Algorithm on training artificial neural networks. In: IEEE 15th Signal Processing and Communications Applications, Inst. of Elec. and Elec. Eng. Computer Society, Eskisehir, Turkey (2007)

    Google Scholar 

  4. Valls, V., Ballestín, F., Quintanilla, S.: Justification and RCPSP: A technique that pays. European Journal of Operational Research 165, 375–386 (2005)

    Article  MATH  Google Scholar 

  5. Valls, V., Ballestín, F., Quintanilla, S.: A Population-Based Approach to the Resource-Constrained Project Scheduling Problem. Annals of Operations Research 131, 305–324 (2004)

    Article  MATH  Google Scholar 

  6. Chen, W., Shi, Y.-j., Teng, H.-f., Lan, X.-p., Hu, L.-c.: An efficient hybrid algorithm for resource-constrained project scheduling. Information Sciences 180, 1031–1039 (2010)

    Google Scholar 

  7. Christofides, N., Alvarez-Valdes, R., Tamarit, J.M.: Project scheduling with resource constraints: A branch and bound approach. European Journal of Operational Research 29, 262–273 (1987)

    Article  MATH  Google Scholar 

  8. Kolisch, R., Hartmann, S.: Heuristic Algorithms for Solving the Resource-Constrained Project Scheduling Problem: Classification and Computational Analysis. In: Weglarz, J. (ed.) Project Scheduling: Recent Models, Algorithms and Applications, pp. 147–178. Kluwer Academic Publishers, Berlin (1999)

    Chapter  Google Scholar 

  9. Hartmann, S., Kolisch, R.: Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem. European Journal of Operational Research 127, 394–407 (2000)

    Article  MATH  Google Scholar 

  10. Debels, D., De Reyck, B., Leus, R., Vanhoucke, M.: A hybrid scatter search/electromagnetism meta-heuristic for project scheduling. European Journal of Operational Research 169, 638–653 (2006)

    Article  MATH  Google Scholar 

  11. Sprecher, A., Kolisch, R., Drexl, A.: Semi-active, active, and non-delay schedules for the resource-constrained project scheduling problem. European Journal of Operational Research 80, 94–102 (1995)

    Article  MATH  Google Scholar 

  12. Kolisch, R., Hartmann, S.: Experimental investigation of heuristics for resource-constrained project scheduling: An update. European Journal of Operational Research 174, 23–37 (2006)

    Article  MATH  Google Scholar 

  13. Kolisch, R., Sprecher, A.: PSPLIB - A project scheduling problem library: OR Software - ORSEP Operations Research Software Exchange Program. European Journal of Operational Research 96, 205–216 (1997)

    Article  MATH  Google Scholar 

  14. Tormos, P., Lova, A.: Integrating heuristics for resource constrained project scheduling: One step forward. Technicalreport, Department of Statistics and Operations Research, Universidad Politecnica de Valencia (2003)

    Google Scholar 

  15. Debels, D., Vanhoucke, M.: A Decomposition-Based Genetic Algorithm for the Resource-Constrained Project-Scheduling Problem. Operations Research 55, 457–469 (2007)

    Article  MATH  Google Scholar 

  16. Hartmann, S.: A competitive genetic algorithm for resource-constrained project scheduling. Naval Research Logistics 45, 733–750 (1998)

    Article  MATH  Google Scholar 

  17. Hartmann, S.: A self-adapting genetic algorithm for project scheduling under resource constraints. Naval Research Logistics 49, 433–448 (2002)

    Article  MATH  Google Scholar 

  18. Kochetov, Y., Stolyar, A.: Evolutionary local search with variable neighborhood for the resource constrained project scheduling problem. In: Proceedings of the 3rd International Workshop of Computer Science and Information Technologies, Russia (2003)

    Google Scholar 

  19. Valls, V., Ballestin, F., Quintanilla, M.S.: A hybrid genetic algorithm for the RCPSP. Department of Statistics and Operations Research, University of Valencia (2003)

    Google Scholar 

  20. Tseng, L.-Y., Chen, S.-C.: A hybrid metaheuristic for the resource-constrained project scheduling problem. European Journal of Operational Research 175, 707–721 (2006)

    Article  MATH  Google Scholar 

  21. Nonobe, K., Ibaraki, T.: Formulation and tabu search algorithm for the resource constrained project scheduling problem. In: Hansen, P. (ed.) Essays and Surveys in Metaheuristics, pp. 557–588. Kluwer Academic Publishers, Dordrecht (2001)

    Google Scholar 

  22. Mendes, J.J.M., Goncalves, J.F., Resende, M.G.C.: A random key based genetic algorithm for the resource constrained project scheduling problem. Computers & Operations Research 36, 92–109 (2009)

    Article  MATH  Google Scholar 

  23. Palpant, M., Artigues, C., Michelon, P.: LSSPER: Solving the Resource-Constrained Project Scheduling Problem with Large Neighbourhood Search. Annals of Operations Research 131, 237–257 (2004)

    Article  MATH  Google Scholar 

  24. Valls, V., Quintanilla, S., Ballestín, F.: Resource-constrained project scheduling: A critical activity reordering heuristic. European Journal of Operational Research 149, 282–301 (2003)

    Article  MATH  Google Scholar 

  25. Artigues, C., Michelon, P., Reusser, S.: Insertion techniques for static and dynamic resource-constrained project scheduling. European Journal of Operational Research 149, 249–267 (2003)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, P.R. China

    Yan-jun Shi & Fu-Zhen Qu

  2. School of Mechanical Engineering, China North Vehicle Research Institute, Beijing, 100072, P.R. China

    Wang Chen & Bo Li

Authors
  1. Yan-jun Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fu-Zhen Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Electrical Engineering and Computer Science, The Queen’s University, Ashby Building, Stranmillis Road, BT9 5AH, Belfast, UK

    Kang Li

  2. School of Mechatronical Engineering and Automation, Shanghai University, P.O.Box 183, 200072, Shanghai, China

    Minrui Fei  & Li Jia  & 

  3. School of Electronics, Electrical Engineering and Computer Science, The Queen’s University, Ashby Building, Stranmillis Road, BT9 5AH, Belfast, UK

    George W. Irwin

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Shi, Yj., Qu, FZ., Chen, W., Li, B. (2010). An Artificial Bee Colony with Random Key for Resource-Constrained Project Scheduling. In: Li, K., Fei, M., Jia, L., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science, vol 6329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15597-0_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15597-0_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15596-3

  • Online ISBN: 978-3-642-15597-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation