By Topic

Genetic Algorithm and Particle Swarm Optimization approaches to solve combinatorial job shop scheduling problems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Surekha, P. ; EEE Dept, PSG Coll. of Technol., Coimbatore, India ; Raajan, P.M. ; Sumathi, S.

In this paper an eminent approach based on the paradigms of evolutionary computation for solving job shop scheduling problem is proposed. The solution to the problem is alienated into three phases; planning, scheduling and optimization. Initially, fuzzy logic is applied for planning and then scheduling is optimized using evolutionary computing algorithms such as Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The well known Adams, Balas, and Zawack 10 × 10 instance (ABZ10) problem is selected as the experimental benchmark problem and simulated using MATLAB R2008b. The results of the optimization techniques are compared with the parameters like makespan, waiting time, completion time and elapse time. The performance evaluation of optimization techniques are analysed and the superior evolutionary technique for solving job shop scheduling problem is determined.

Published in:

Computational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference on

Date of Conference:

28-29 Dec. 2010