Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

Carman K.M. Lee; Danping Lin; William Ho; Zhang Wu

doi:10.1007/s00170-011-3251-4

Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

Carman K.M. Lee, Danping Lin, William Ho, Zhang Wu

Research output: Contribution to journal › Article › peer-review

Abstract

This paper presents a simulated genetic algorithm (GA) model of scheduling the flow shop problem with re-entrant jobs. The objective of this research is to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines in the same order. Furthermore, the re-entrant jobs are stochastic as only some jobs are required to reenter to the flow shop. The tardiness weight is adjusted once the jobs reenter to the shop. The performance of the proposed GA model is verified by a number of numerical experiments where the data come from the case company. The results show the proposed method has a higher order satisfaction rate than the current industrial practices.

Original language	English
Pages (from-to)	1105-1113
Number of pages	9
Journal	International Journal of Advanced Manufacturing Technology
Volume	56
Issue number	9-12
Early online date	5 Apr 2011
DOIs	https://doi.org/10.1007/s00170-011-3251-4
Publication status	Published - 1 Oct 2011

Bibliographical note

Keywords

bi-objective
flow shop
genetic algorithm
re-entrant

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10.1007/s00170-011-3251-4

Design of a genetic algorithm for biobjective flow shop scheduling problems with re-entrant jobs
© Springer-Verlag London Limited 2011. The final publication is available at Springer via http://dx.doi.org/10.1007/s00170-011-3251-4
Accepted author manuscript, 456 KB

Cite this

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title = "Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs",

abstract = "This paper presents a simulated genetic algorithm (GA) model of scheduling the flow shop problem with re-entrant jobs. The objective of this research is to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines in the same order. Furthermore, the re-entrant jobs are stochastic as only some jobs are required to reenter to the flow shop. The tardiness weight is adjusted once the jobs reenter to the shop. The performance of the proposed GA model is verified by a number of numerical experiments where the data come from the case company. The results show the proposed method has a higher order satisfaction rate than the current industrial practices.",

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AB - This paper presents a simulated genetic algorithm (GA) model of scheduling the flow shop problem with re-entrant jobs. The objective of this research is to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines in the same order. Furthermore, the re-entrant jobs are stochastic as only some jobs are required to reenter to the flow shop. The tardiness weight is adjusted once the jobs reenter to the shop. The performance of the proposed GA model is verified by a number of numerical experiments where the data come from the case company. The results show the proposed method has a higher order satisfaction rate than the current industrial practices.

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Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

Abstract

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Keywords

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