Norhashimah, Morad and Zalzala, A.M.S. (1997) Integrated Production Planning and Scheduling in Cellular Manufacturing Using Genetic Algorithms. Research Report. ACSE Research Report 667 . Department of Automatic Control and Systems Engineering
Abstract
This paper describes the integration of production planning and scheduling in order to find better schedules. The conventional method which seeks the planning of production before going in scheduling could cause some schedules to be less optimal. In cellular manufacturing, most processes can be done on more than one machines which have different processing times. In the proposed method based on genetic algorithms, the processing capabilities of the machines which includes the processing cost as well as number of rejects produced in alternative machines are considered simultaneously with the scheduling of the jobs. The formulation is based on weighted-sums multi-objective which are to minimize total rejects produced and to minimize the total cost of production.
Metadata
Item Type: | Monograph |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances. |
Keywords: | Multi-objective genetic algorithms; process planning and scheduling. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) > ACSE Research Reports |
Depositing User: | MRS ALISON THERESA BARNETT |
Date Deposited: | 16 Oct 2014 10:40 |
Last Modified: | 26 Oct 2016 08:31 |
Status: | Published |
Publisher: | Department of Automatic Control and Systems Engineering |
Series Name: | ACSE Research Report 667 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:81079 |