An Orthogonal Genetic Algorithm for Job Shop Scheduling Problems with Multiple Objectives

Author

Feng, Ming-yue ; Yi, Xian-qing ; LI, Guo-hui ; Tang, Shao-xun ; He, Jun

Author_Institution

Coll. of Inf. Syst. & Manage., Nat. Univ. of Defense Technol., Changsha

Volume

1

fYear

2008

fDate

18-20 Oct. 2008

Firstpage

546

Lastpage

550

Abstract

The job shop scheduling problem with multiple objectives is a research hotspot. In this paper, a multi-objective orthogonal genetic algorithm(MOOGA) was proposed to solve this problem. MOOGA integrated the orthogonal design method into the crossover operator, which could generate both outstanding and evenly distributed filial individuals, and improve the efficiency of the algorithm. A fitness calculating method was designed to help MOOGA move towards the Pareto front. Numerical results verify effectiveness and efficiency of the algorithm.

Keywords

Pareto optimisation; genetic algorithms; job shop scheduling; mathematical operators; Pareto front; crossover operator; fitness calculating method; job shop scheduling; multiobjective orthogonal genetic algorithm; Conference management; Costs; Delay effects; Design methodology; Educational institutions; Genetic algorithms; Helium; Information management; Job shop scheduling; Management information systems; Job Shop Scheduling; Orthogonal Genetic Algorithm; multi-objective;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2008. ICNC '08. Fourth International Conference on

Conference_Location

Jinan

Print_ISBN

978-0-7695-3304-9

Type

conf

DOI

10.1109/ICNC.2008.612

Filename

4666905