A hybrid genetic algorithm for the job scheduling problems

A hybrid genetic algorithm for the job scheduling problems

0.00 Avg rating0 Votes
Article ID: iaor20042033
Country: Netherlands
Volume: 45
Issue: 4
Start Page Number: 597
End Page Number: 613
Publication Date: Dec 2003
Journal: CIE
Authors: , ,
Keywords: job shop, genetic algorithms
Abstract:

The Job Shop Scheduling Problem (JSSP) is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on genetic algorithm to address JSSP. We design a scheduling method based on Single Genetic Algorithm (SGA) and Parallel Genetic Algorithm (PGA). In the scheduling method, the representation, which encodes the job number, is made to be always feasible, the initial population is generated through integrating representation and G&T algorithm, the new genetic operators and selection method are designed to better transmit the temporal relationships in the chromosome, and island model PGA are proposed. The scheduling methods based on genetic algorithm are tested on five standard benchmark JSSP. The results are compared with other proposed approaches. Compared to traditional genetic algorithm, the proposed yields significant improvement in solution quality. The superior results indicate the successful incorporation of a method to generate initial population into the genetic operators.

Reviews

Required fields are marked *. Your email address will not be published.