Please use this identifier to cite or link to this item:
Title: Job-shop scheduling with an adaptive neural network and local search hybrid approach
Authors: Yang, S
Keywords: Adaptive scheduling;Adaptive systems;Computer science;Constraint optimization;Job production systems;Job shop scheduling;Neural networks;Neurons;Processor scheduling;Sorting
Issue Date: 2006
Publisher: IEEE
Citation: IEEE International Joint Conference on Neural Networks (IJCNN 2006): 2720 - 2727, 16-21 Jul 2006
Abstract: 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.
Description: This article is posted here with permission from IEEE - Copyright @ 2006 IEEE
ISBN: 0-7803-9490-9
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf417.21 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.