Title :
Stochastic neural networks for solving job-shop scheduling. II. architecture and simulations
Author :
Foo, Yoon-Pin Simon ; Takefuji, Yoahiyasu
Author_Institution :
Dept. of Electr. & Comput. Eng., South Carolina Univ., Columbia, SC, USA
Abstract :
For Part I, see ibid., p.275-82. The authors introduce a neural computation architecture based on a stochastic Hopfield neural network model for solving job-shop scheduling. A computation circuit computes the total completion times (costs) of all jobs, and the cost difference is added to the energy function of the stochastic neural network. Using a simulated annealing algorithm, the temperature of the system is slowly decreased according to an annealing schedule until the energy of the system is at a local or global minimum. By choosing an appropriate annealing schedule, near-optimal and optimal solutions to job-shop problems can be found. The architecture of the system is presented at both the functional and circuit levels. Simulation results are presented.<>
Keywords :
computer architecture; neural nets; optimisation; scheduling; stochastic systems; Hopfield neural network; computation circuit; cost difference; energy function; job-shop scheduling; near-optimal solutions; neural computation architecture; optimal solutions; simulated annealing; stochastic neural network; Computer architecture; Neural networks; Optimization methods; Scheduling; Stochastic systems;
Conference_Titel :
Neural Networks, 1988., IEEE International Conference on
Conference_Location :
San Diego, CA, USA
DOI :
10.1109/ICNN.1988.23940