Integration of artificial neural networks and genetic algorithm for job-shop scheduling problem

  • Authors:

  • Fuqing Zhao;Yi Hong;Dongmei Yu;Xuhui Chen;Yahong Yang

  • Affiliations:

  • School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu, China;School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu, China;School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu, China;School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu, China;College of Civil Engineering, Lanzhou University of Techchnology, Lanzhou, Gansu, China

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
  • Year:
  • 2005

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Abstract

Job-shop scheduling is usually a strongly NP-hard problem of combinatorial optimization problems and is one of the most typical production scheduling problem. It is usually very hard to find its optimal solution. In this paper, a new hybrid approach in dealing with this job-shop scheduling problem based on artificial neural network and genetic algorithm (GA) is presented. The GA 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. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency.