A Multiobjective Hybrid Genetic Algorithm for TFT-LCD Module Assembly Scheduling

The thin-film transistor-liquid crystal display (TFT-LCD) module assembly production is a flexible job-shop scheduling problem that is critical to satisfy the customer demands on time. On the module assembly shop floor, each workstation has identical and non-identical parallel machines that access t...

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Bibliographic Details
Published in:IEEE transactions on automation science and engineering Vol. 11; no. 3; pp. 692 - 705
Main Authors: Che-Wei Chou, Chen-Fu Chien, Gen, Mitsuo
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Assembly
Assembly lines
Crystals
Genetic algorithms
Job shop scheduling
Job shops
LCDs
Liquid crystal displays
Machine shops
Modules
Molybdenum
Multiobjective genetic algorithm
Parallel machines
Scheduling
Setup times
Thin film transistors
Thin films
thin-film transistor-liquid crystal display (TFT-LCD)
TOPSIS
variable neighborhood descent
Work stations
Workstations
ISSN:1545-5955, 1558-3783
Online Access:Get full text
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Summary:The thin-film transistor-liquid crystal display (TFT-LCD) module assembly production is a flexible job-shop scheduling problem that is critical to satisfy the customer demands on time. On the module assembly shop floor, each workstation has identical and non-identical parallel machines that access the jobs at various processing velocities depending on the product families. To satisfy the various jobs, the machines need to be set up as the numerous tools to conduct consecutive products. This study aims to propose a novel approach to address the TFT-LCD module assembly scheduling problem by simultaneously considering the following multiple and often conflicting objectives such as the makespan, the weighted number of tardy jobs, and the total machine setup time, subject to the constraints of product families, non-identical parallel machines, and sequence-dependent setup times. In particular, we developed a multiobjective hybrid genetic algorithm (MO-HGA) that hybridizes with the variable neighborhood descent (VND) algorithm as a local search and TOPSIS evaluation technique to derive the best compromised solution. To estimate the validity of the proposed MO-HGA, experiments based on empirical data were conducted to compare the results with conventional approaches. The results have shown the validity of this approach. This study concludes with a discussion of future research directions.
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ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2014.2316193