A Job Shop Scheduling Algorithm Using Chaotic Neural Network

The job shop scheduling problem is a complicated combination optimization question. The problem allocates limited resources to tasks over time and determines the sequence of operations so that the constraints of the system are met and the performance criteria are optimized. This paper describes a generalized job-shop scheduling algorithm using chaotic neural network based on simulated annealing method. The neural network which introducing the chaos mechanism and simulated annealing strategy in Hopfield neural network can avoid putting into local search. An improved permutation matrix and an improved energy function with objective function of job-shop scheduling problem are given. Simulation results show that the convergence speed and accuracy of solution of this algorithm are better than Hopfield neural network based on other strategy.