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

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 an...

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Bibliographic Details
Main Authors: Ming-yue Feng, Xian-qing Yi, Guo-hui Li, Shao-xun Tang, Jun He
Format: Conference Proceeding
Language:English
Subjects:
Conference management
Costs
Delay effects
Design methodology
Educational institutions
Genetic algorithms
Helium
Information management
Job shop scheduling
Management information systems
multi-objective
Orthogonal Genetic Algorithm
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Summary: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.
ISSN:2157-9555
DOI:10.1109/ICNC.2008.612