Advertisement

Local Search Methods for the MRCPSP-Energy

  • André Renato Villela da Silva
  • Luiz Satoru Ochi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11299)

Abstract

The Multi-Mode Resource-Constrained Project Scheduling Problem with energy saving (MRCPSP-energy) is a variant of the classical Resource-Constrained Project Scheduling Problem (RCPSP). In this variant, the execution of each job must take into account the job duration and the energy spent to execute that job, which are conflicting. The objective is to minimize both makespan and total energy consumption. This work proposes two local search methods to improve a large dataset of inputs. One of them is a restricted version of a Mixed-Integer Programming formulation and the other one is a heuristic local search called H. The computational experiments showed that the hybrid method with the H algorithm obtained better solutions and is competitive with the literature results.

Keywords

MRCPSP MRCPSP-energy Heuristics Local search procedures Hybrid heuristics 

References

  1. 1.
    Blazewicz, J., Lenstra, J., Rinnooy Kan, A.: Scheduling subject to resource constraints: classification and complexity. Disc. App. Math. 5, 11–24 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Boctor, F.F.: Some efficient multi-heuristic procedures for resource constrained project scheduling. Eur. J. Oper. Res. 49, 3–13 (1990)zbMATHCrossRefGoogle Scholar
  3. 3.
    Bouleimen, K., Lecocq, H.: A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. Eur. J. Oper. Res. 149, 268–281 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Brucker, P., Drexl, A., Möhring, R., Neumann, K., Pesch, E.: Resource-constrained project scheduling: notation, classification, models, and methods. Eur. J. Oper. Res. 112, 3–41 (1999)zbMATHCrossRefGoogle Scholar
  5. 5.
    Demeulemeester, E., Herroelen, W.: A branch-and-bound procedure for multiple resource-constrained project scheduling problem. Manag. Sci. 38, 1803–1818 (1992)zbMATHCrossRefGoogle Scholar
  6. 6.
    Gonçalves, J.F., Mendes, J.J.M., Resende, M.G.C.: A random key based genetic algorithm for the resource constrained project scheduling problems. Int. J. Prod. Res. 36, 92–109 (2009)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Hartmann, S., Briskorn, D.: A survey of variants and extensions of the resource-constrained project scheduling problem. Eur. J. Oper. Res. 207(1), 1–14 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Kolisch, R., Hartmann, S.: Experimental investigation of heuristics for resource-constrained project scheduling: an update. Eur. J. Oper. Res. 174(1), 23–37 (2006)zbMATHCrossRefGoogle Scholar
  9. 9.
    Lu, Y., Benlic, U., Wu, Q.: A hybrid dynamic programming and memetic algorithm to the traveling salesman problem with hotel selection. Comput. Oper. Res. 90, 193–207 (2018).  https://doi.org/10.1016/j.cor.2017.09.008MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Myszkowski, P.B., Skowroński, M.E., Olech, Ł.P., Oślizło, K.: Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem. Soft Comput. 19(12), 3599–3619 (2015)CrossRefGoogle Scholar
  11. 11.
    Morillo, D.: PSPLIB-ENERGY: a PSPLIB extension for evaluating energy optimization in MRCPSP. http://gps.webs.upv.es/psplib-energy/. Accessed 28 Nov 2017
  12. 12.
    Morillo, D., Barber, F., Salido, M.A.: Mode-based versus activity-based search for a nonredundant resolution of the multimode resource-constrained project scheduling problem. Mathem. Probl. Eng. 2017, 15 (2017).  https://doi.org/10.1155/2017/4627856. Article ID 4627856MathSciNetCrossRefGoogle Scholar
  13. 13.
    Prais, M., Ribeiro, C.C.: Reactive GRASP: an application to a matrix decomposition problem in TDMA Traffic assignment. INFORMS J. Comput. 12(3), 163–255 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Torres, D.M., Barber, F., Salido, M.A.: MRCPSP-ENERGY, un enfoque metaheurístico para problemas de programación de actividades basados en el uso de energía. In: Proceedings of XVIII Latin Ibero-American Conference on Operations Research (CLAIOXVIII), Santiago-Chile, October 2016Google Scholar
  15. 15.
    Villela da Silva, A.R.: Techniques to solve a multi-mode resource constrained project scheduling problem with energy saving. In: Proceedings of XIII Brazilian Congress on Computational Intelligence (XIII CBIC), Niterói-Brazil, October 2017Google Scholar
  16. 16.
    Tseng, L.-Y., Chen, S.-C.: A hybrid metaheuristic for the resource-constrained project scheduling problem. Eur. J. Oper. Res. 175(2), 707–721 (2006)zbMATHCrossRefGoogle Scholar
  17. 17.
    Valls, V., Ballestin, F., Quintanilla, M.S.: A hybrid genetic algorithm for the resource-constrained project scheduling problem. Eur. J. Oper. Res. 185(2), 495–508 (2008)zbMATHCrossRefGoogle Scholar
  18. 18.
    Zhu, G., Bard, J., Tu, G.: A branch-and-cut procedure for the multimode resource-constrained project-scheduling problem. J. Comput. 18(3), 377–390 (2006)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • André Renato Villela da Silva
    • 1
  • Luiz Satoru Ochi
    • 2
  1. 1.Institute of Science and TechnologyFederal Fluminense UniversityRio das OstrasBrazil
  2. 2.Institute of ComputingFederal Fluminense UniversityNiteróiBrazil

Personalised recommendations