Energy-Aware Multiple State Machine Scheduling for Multiobjective Optimization

  • Angelo Oddi
  • Riccardo Rasconi
  • Miguel A. González
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11298)


Optimising the energy consumption is one of the most important issues in scheduling nowadays. In this work we consider a multi-objective optimisation for the well-known job-shop scheduling problem. In particular, we minimise the makespan and the energy consumption at the same time. We consider a realistic energy model where each machine can be in Off, Stand-by, Idle or Working state. We design an effective constraint-programming approach to optimise both the energy consumption and the makespan of the solutions. Experimental results illustrate the potential of the proposed method, outperforming the results of the current state of the art in this problem.


Constraint-programming Job-shop scheduling Energy considerations Multi-objective optimisation 


  1. 1.
    Apt, K.: Principles of Constraint Programming. Cambridge University Press, New York (2003)CrossRefGoogle Scholar
  2. 2.
    Baker, K.: Introduction to Sequencing and Scheduling. Wiley, London (1974)Google Scholar
  3. 3.
    Fisher, H., Thomson, G.L.: Probabilistic learning combinations of local job-shop scheduling rules. In: Muth, J.F., Thomson, G.L. (eds.) Industrial Scheduling, pp. 225–251. Prentice Hall, Englewood Cliffs (1963)Google Scholar
  4. 4.
    González, M.A., Oddi, A., Rasconi, R.: Multi-objective optimization in a job shop with energy costs through hybrid evolutionary techniques. In: Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling (ICAPS-2017), pp. 140–148. AAAI Press, Pittsburgh (2017)Google Scholar
  5. 5.
    Laborie, P.: Algorithms for propagating resource constraints in AI planning and scheduling: existing approaches and new results. Artif. Intell. 143(2), 151–188 (2003)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Le Pape, C., Baptiste, P., Nuijten, W.: Constraint-Based Scheduling: Applying Constraint Programming to Scheduling Problems. Springer, New York (2001). Scholar
  7. 7.
    Liu, Y., Dong, H., Lohse, N., Petrovic, S., Gindy, N.: An investigation into minimising total energy consumption and total weighted tardiness in job shops. J. Clean. Prod. 65, 87–96 (2014)CrossRefGoogle Scholar
  8. 8.
    May, G., Stahl, B., Taisch, M., Prabhu, V.: Multi-objective genetic algorithm for energy-efficient job shop scheduling. Int. J. Prod. Res. 53(23), 7071–7089 (2015)CrossRefGoogle Scholar
  9. 9.
    Miettinen, K.: Nonlinear Multiobjective Optimization. International Series in Operations Research & Management Science. Springer, New York (2012). Scholar
  10. 10.
    Oddi, A., Rasconi, R., González, M.: A constraint programming approach for the energy-efficient job shop scheduling problem. In: Gunawan, A., Kendall, G., Soon, L., McCollum, B., Seow, H.V. (eds.) Proceedings of the 8th Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2017), 05–08 December 2017, Kuala Lumpur, Malaysia, pp. 158–172 (2017)Google Scholar
  11. 11.
    Vilím, P., Barták, R., Čepek, O.: Unary resource constraint with optional activities. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 62–76. Springer, Heidelberg (2004). Scholar
  12. 12.
    Zhang, R., Chiong, R.: Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption. J. Clean. Prod. 112, 3361–3375 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Angelo Oddi
    • 1
  • Riccardo Rasconi
    • 1
  • Miguel A. González
    • 2
  1. 1.Institute of Cognitive Sciences and Technologies, ISTC-CNRRomeItaly
  2. 2.Department of ComputingUniversity of OviedoOviedoSpain

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