A Hybrid Approach for Multi-Objective Expanded Job-Shop Scheduling Problem Based on Artificial Neural Network and Particle Swarm Optimization

摘要

In this paper, a new hybrid approach in dealing with the multi-objective expanded job-shop scheduling problem based on artificial neural network and particle swarm optimization (PSO) is presented. The PSO is used for optimization of sequence and neural network (NN) is used for optimization of operation start times with a fixed sequence. New type of neurons which can represent processing restrictions and resolve constraint conflict are defined to construct a constraint neural network (CNN). CNN with a gradient search algorithm is applied to the optimization of operation start times with a fixed processing sequence. A hybrid approach based on CNN with gradient search algorithm and PSO for sequence optimization is put forward. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency.