Optimizing apparel production order planning scheduling using genetic algorithms

Z. X. Guo; Wai Keung Wong; S. Y.S. Leung; J. T. Fan; S. F. Chan

doi:10.1533/9780857097842.55

Optimizing apparel production order planning scheduling using genetic algorithms

Z. X. Guo, Wai Keung Wong, S. Y.S. Leung, J. T. Fan, S. F. Chan

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Chapter in an edited book (as author) › Academic research › peer-review

1 Citation (Scopus)

Abstract

In this chapter the order scheduling problem at the factory level is investigated. Various uncertainties are considered and described as random variables. A mathematical model for this order scheduling problem is presented with the objectives of maximizing the total satisfaction level of all orders and minimizing their total throughput time. Uncertain completion time and beginning time of production process are derived from probability theory. A genetic algorithm is developed to seek after the optimal order scheduling solution. Experiments are conducted to validate the proposed algorithm by using real-world production data. The experimental results show the effectiveness of the proposed algorithm.

Original language	English
Title of host publication	Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI)
Subtitle of host publication	From Production to Retail
Publisher	Elsevier Inc.
Pages	55-80
Number of pages	26
ISBN (Print)	9780857097798
DOIs	https://doi.org/10.1533/9780857097842.55
Publication status	Published - 1 Jan 2013

Keywords

Genetic algorithms
Order scheduling
Probability theory
Uncertain processing time

ASJC Scopus subject areas

General Engineering

More information

10.1533/9780857097842.55

Cite this

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abstract = "In this chapter the order scheduling problem at the factory level is investigated. Various uncertainties are considered and described as random variables. A mathematical model for this order scheduling problem is presented with the objectives of maximizing the total satisfaction level of all orders and minimizing their total throughput time. Uncertain completion time and beginning time of production process are derived from probability theory. A genetic algorithm is developed to seek after the optimal order scheduling solution. Experiments are conducted to validate the proposed algorithm by using real-world production data. The experimental results show the effectiveness of the proposed algorithm.",

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Optimizing apparel production order planning scheduling using genetic algorithms. / Guo, Z. X.; Wong, Wai Keung; Leung, S. Y.S. et al.
Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI): From Production to Retail. Elsevier Inc., 2013. p. 55-80.

Research output: Chapter in book / Conference proceeding › Chapter in an edited book (as author) › Academic research › peer-review

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Optimizing apparel production order planning scheduling using genetic algorithms

Abstract

Keywords

ASJC Scopus subject areas

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