A Multiple Criteria Genetic Algorithm Scheduling Tool for Production Scheduling in the Capital Goods Industry

Production planners usually aim to satisfy multiple objectives. This paper describes the development of a genetic algorithm tool that finds optimum trade-offs among delivery performance, resource utilisation, and workin-progress inventory. The tool was specifically developed to meet the requirements...

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Main Authors: Wenbin Xie, Chris Hicks, Pupong Pongcharoen
Format: Article
Language:English
Published: Taiwan Association of Engineering and Technology Innovation 2014-01-01
Series:International Journal of Engineering and Technology Innovation
Subjects:
Online Access:http://ojs.imeti.org/index.php/IJETI/article/view/126
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spelling doaj-78722227bf1e462bac918ecf6d227aeb2020-11-25T00:26:20ZengTaiwan Association of Engineering and Technology InnovationInternational Journal of Engineering and Technology Innovation2223-53292226-809X2014-01-0141A Multiple Criteria Genetic Algorithm Scheduling Tool for Production Scheduling in the Capital Goods IndustryWenbin XieChris HicksPupong PongcharoenProduction planners usually aim to satisfy multiple objectives. This paper describes the development of a genetic algorithm tool that finds optimum trade-offs among delivery performance, resource utilisation, and workin-progress inventory. The tool was specifically developed to meet the requirements of capital goods companies that manufacture products with deep and complex product structures with components that have long and complicated routings. The model takes into account operation and assembly precedence relationships and finite capacity constraints. The tool was tested using various production problems that were obtained from a collaborating company. A series of experiments showed the tool provides a set of non-dominated solutions that enable the planner to choose an optimum trade-off according to their preferences. Previous research had optimised a single objective function. This is the first scheduling tool of its type that has simultaneously optimised delivery performance, resource utilisation and work-in-progress inventory. The quality of the schedules produced was significantly better than the approaches used by the collaborating company.http://ojs.imeti.org/index.php/IJETI/article/view/126genetic algorithmscapital goodsmultiple criteriaproduction scheduling
collection DOAJ
language English
format Article
sources DOAJ
author Wenbin Xie
Chris Hicks
Pupong Pongcharoen
spellingShingle Wenbin Xie
Chris Hicks
Pupong Pongcharoen
A Multiple Criteria Genetic Algorithm Scheduling Tool for Production Scheduling in the Capital Goods Industry
International Journal of Engineering and Technology Innovation
genetic algorithms
capital goods
multiple criteria
production scheduling
author_facet Wenbin Xie
Chris Hicks
Pupong Pongcharoen
author_sort Wenbin Xie
title A Multiple Criteria Genetic Algorithm Scheduling Tool for Production Scheduling in the Capital Goods Industry
title_short A Multiple Criteria Genetic Algorithm Scheduling Tool for Production Scheduling in the Capital Goods Industry
title_full A Multiple Criteria Genetic Algorithm Scheduling Tool for Production Scheduling in the Capital Goods Industry
title_fullStr A Multiple Criteria Genetic Algorithm Scheduling Tool for Production Scheduling in the Capital Goods Industry
title_full_unstemmed A Multiple Criteria Genetic Algorithm Scheduling Tool for Production Scheduling in the Capital Goods Industry
title_sort multiple criteria genetic algorithm scheduling tool for production scheduling in the capital goods industry
publisher Taiwan Association of Engineering and Technology Innovation
series International Journal of Engineering and Technology Innovation
issn 2223-5329
2226-809X
publishDate 2014-01-01
description Production planners usually aim to satisfy multiple objectives. This paper describes the development of a genetic algorithm tool that finds optimum trade-offs among delivery performance, resource utilisation, and workin-progress inventory. The tool was specifically developed to meet the requirements of capital goods companies that manufacture products with deep and complex product structures with components that have long and complicated routings. The model takes into account operation and assembly precedence relationships and finite capacity constraints. The tool was tested using various production problems that were obtained from a collaborating company. A series of experiments showed the tool provides a set of non-dominated solutions that enable the planner to choose an optimum trade-off according to their preferences. Previous research had optimised a single objective function. This is the first scheduling tool of its type that has simultaneously optimised delivery performance, resource utilisation and work-in-progress inventory. The quality of the schedules produced was significantly better than the approaches used by the collaborating company.
topic genetic algorithms
capital goods
multiple criteria
production scheduling
url http://ojs.imeti.org/index.php/IJETI/article/view/126
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