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Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Co-Evolutionary Algorithm solving Multi-Skill Resource-Constrained Project Scheduling Problem

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DOI: http://dx.doi.org/10.15439/2017F318

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 75–82 ()

Full text

Abstract. This paper presents methods solving MS--RCPSP as main task--resource--time assignment optimization problem. In paper are presented four variants of Evolutionary Algorithm applied to MS--RCPSP problem: concern of prioritizing the tasks (or resources), combined task--resources prioritizing approach and co--evolution based approach that effectively solves problem dividing it to two subproblems. All approaches are examined using benchmark MS--RCPSP iMOPSE dataset and results show that problem decomposition is effective. All experiments are described, statistically verified and summarized. Conclusions and promising areas of future work are presented.

References

  1. Bianco L., Dell Olmo P., Speranza M.G.; Heuristics for multimode scheduling problems with dedicated resources, Eu. J. of Oper. Research (107), pp. 260–271, 1998.
  2. Błazewicz J., Lenstra J.K., Rinnooy Kan A. H. G.; Scheduling subject to resource constraints: Classification and complexity, Discr. App. Math. (5), pp. 11–24, 1983.
  3. Hartmann S., Briskorn D.; A survey of variants and extensions of the resource–constrained project scheduling problem, Eur. J. of Oper. Res. (207), pp. 1–14, 2010.
  4. Hartmann S., Kolisch R.; Experimental investigation of heuristics for resource–constrained project scheduling: An update, Eur. J. of Oper. Res. (174), pp. 23–37, 2006.
  5. Myszkowski P. B., Laszczyk M., Nikulin I. and Skowroński M. E. "iMOPSE: a library for bicriteria optimization in Multi–Skill Resource–Constrained Project Scheduling Problem", in review process, Soft Com- puting Journal.
  6. Myszkowski P. B., Olech L. P., Laszczyk M. and Skowroński M. E. "Hybrid Differential Evolution and Greedy (DEGR) for Solving Multi–Skill Resource–Constrained Project Scheduling Problem", in review process, Applied Soft Computing Journal.
  7. Myszkowski P. B., Siemieński J. J., "GRASP applied to Multi-Skill Resource-Constrained Project Scheduling Problem", Computational Collective Intelligence, Volume 9875 of the series Lecture Notes in Computer Science pp.402-411, 2016.
  8. Myszkowski P. B., Skowroński M., Sikora K., "A new benchmark dataset for Multi-Skill Resource-Constrained Project Scheduling Problem", Proc. of FedCSIS Conference (2015), M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 5, pages 129–138 (2015)
  9. Myszkowski P.B., Skowroński M., Olech L., Oślizło K. "Hybrid Ant Colony Optimization in solving Multi–Skill Resource–Constrained Project Scheduling Problem", Soft Computing Journal, 2015, Volume 19, Issue 12, pp.3599–3619.
  10. Myszkowski P.B., Skowroński M., "Specialized genetic operators for Multi–Skill Resource–Constrained Project Scheduling Problem", 19th Inter. Conference on Soft Computing – Mendel 2013, pp. 57-62, 2013.
  11. Skowroński M., Myszkowski P.B., Kwiatek P., Adamski M., "Tabu Search approach for Multi–Skill Resource–Constrained Project Scheduling Problem", Annals of Computer Science and Information Systems Volume 1, Proc. of FedCSIS Conference (2013), pp. 153-158, 2013.
  12. Skowroński M., Myszkowski P.B., Podlodowski L., "Novel heuristic solutions for Multi–Skill Resource–Constrained Project Scheduling Problem", Annals of Computer Science and Information Systems Volume 1, Proc. of FedCSIS Conference (2013), pp. 159-166, 2013.
  13. Zheng, H., Wang, L. and Zheng, X., Teaching–learning-based optimization algorithm for multi–skill resource constrained project scheduling problem, Soft Comput (2017) 21: 1537.
  14. Yan R., Li. W., Jiang P., Zhou Y., Wu G.; A Modified Differential Evolution Algorithm for Resource Constrained Multi-project Scheduling Problem, Journal of Computers, Vol. 9, No. 8, pp. 1922–1927, 2014.
  15. H. y. Zheng, L. Wang and S. y. Wang, "A co-evolutionary teaching-learning-based optimization algorithm for stochastic RCPSP," 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, 2014, pp. 587-594.