An Improved Genetic Algorithm for Multi-Objective Flexible Job-Shop Scheduling Problem

Abstract:

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Flexible job shop scheduling problem (FJSP) is an extended traditional job shop scheduling problem, which more approximates to real scheduling problems. This paper presents a multi-objective genetic algorithm (GA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved multi-objective GA, the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Advanced crossover and mutation operators are proposed to adapt to this special chromosome structure. The proposed algorithm is evaluated on three representative instances and the computational results and comparison with some other approaches show that the proposed multi-objective algorithm is effective and potential.

Info:

Periodical:

Advanced Materials Research (Volumes 97-101)

Edited by:

Zhengyi Jiang and Chunliang Zhang

Pages:

2449-2454

DOI:

10.4028/www.scientific.net/AMR.97-101.2449

Citation:

C. Y. Zhang et al., "An Improved Genetic Algorithm for Multi-Objective Flexible Job-Shop Scheduling Problem ", Advanced Materials Research, Vols. 97-101, pp. 2449-2454, 2010

Online since:

March 2010

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Price:

$35.00

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