Skip to main content
Article thumbnail
Location of Repository

Job-Shop Scheduling with an Adaptive Neural Network and Local Search Hybrid Approach

By Shengxiang Yang


Job-shop scheduling is one of the most difficult production scheduling problems in industry. This paper proposes an adaptive neural network and local search hybrid approach for the job-shop scheduling problem. The adaptive neural network is constructed based on constraint satisfactions of job-shop scheduling and can adapt its structure and neuron connections during the solving process. The neural network is used to solve feasible schedules for the job-shop scheduling problem while the local search scheme aims to improve the performance by searching the neighbourhood of a given feasible schedule. The experimental study validates the proposed hybrid approach for job-shop scheduling regarding the quality of solutions and the computing speed

Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Year: 2006
DOI identifier: 10.1109/IJCNN.2006.247176
OAI identifier:

Suggested articles

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.