Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research
INFORMS Journal on Computing
A coupled gradient network approach for static and temporal mixed-integer optimization
IEEE Transactions on Neural Networks
Minimizing the normalized sum of square for workload deviations on m parallel processors
Computers and Industrial Engineering
Heuristic algorithms for assigning and scheduling flight missions in a military aviation unit
Computers and Industrial Engineering
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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.