Minimizing Makespan on Identical Parallel Machines Using Neural Networks

  • Derya Eren Akyol
  • G. Mirac Bayhan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


This paper deals with the problem of minimizing the maximum completion time (makespan) of jobs on identical parallel machines. A Hopfield type dynamical neural network is proposed for solving the problem which is known to be NP-hard even for the case of two machines. A penalty function approach is employed to construct the energy function of the network and time evolving penalty coefficients are proposed to be used during simulation experiments to overcome the tradeoff problem. The results of proposed approach tested on a scheduling problem across 3 different datasets for 5 different initial conditions show that the proposed network converges to feasible solutions for all initialization schemes and outperforms the LPT (longest processing time) rule.


Schedule Problem Energy Function Travelling Salesman Problem Penalty Parameter Initialization Scheme 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Derya Eren Akyol
    • 1
  • G. Mirac Bayhan
    • 1
  1. 1.Department of Industrial EngineeringDokuz Eylul UniversityBornova-IzmirTurkey

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