Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 38, Issue 8, Pages 9248-9255Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.01.136
Keywords
Adaptive annealing genetic algorithm; Genetic algorithm; Job-shop planning; Scheduling
Categories
Funding
- NSFC [61034004, 61073090]
- Guangdong Science and Technology Department of China [2009GJE00026, 2009B090300429]
- SMST Commission [09510701300]
- SRF for ROCS of SEM
Ask authors/readers for more resources
The genetic algorithm, the simulated annealing algorithm and the optimum individual protecting algorithm are based on the order of nature, and there exist some application limitations on global astringency, population precocity and convergence rapidity. An adaptive annealing genetic algorithm is proposed to deal with the job-shop planning and scheduling problem for the single-piece, small-batch, custom production mode. In the AAGA, the adaptive mutation probability is included to improve upon the convergence rapidity of the genetic algorithm, and to avoid local optimization, the Boltzmann probability selection mechanism from the simulated annealing algorithm, which solves the population precocity and the local convergence problems, is applied to select the crossover parents. Finally, the AAGA-based job-shop planning and scheduling problem is discussed, and the computing results of AAGA and GA are depicted and compared. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available