Academic
Publications
An effective genetic algorithm for the flexible job-shop scheduling problem

An effective genetic algorithm for the flexible job-shop scheduling problem,10.1016/j.eswa.2010.08.145,Expert Systems With Applications,Guohui Zhang,L

An effective genetic algorithm for the flexible job-shop scheduling problem   (Citations: 4)
BibTex | RIS | RefWorks Download
In this paper, we proposed an effective genetic algorithm for solving the flexible job-shop scheduling problem (FJSP) to minimize makespan time. In the proposed algorithm, Global Selection (GS) and Local Selection (LS) are designed to generate high-quality initial population in the initialization stage. An improved chromosome representation is used to conveniently represent a solution of the FJSP, and different strategies for crossover and mutation operator are adopted. Various benchmark data taken from literature are tested. Computational results prove the proposed genetic algorithm effective and efficient for solving flexible job-shop scheduling problem.
Journal: Expert Systems With Applications - ESWA , vol. 38, no. 4, pp. 3563-3573, 2011
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
Sort by: