Comparison of some algorithms for manufacturing production planning
Comparison of some algorithms for manufacturing production planning
The production planning of a multiproduct multiperiod multistage manufacturing system with deterministic demand is modelled as a network flow optimisation problem with side constraints. The optimisation algorithm determines the optimum production and storage level, the demand supply level, backorder, and raw material allocation during the planning horizon. The performance of some optimisation algorithms ( Simplex, Dantzig-Wolfe and Interior Point Methods) in a serial and parallel environment using Parallel Optimisation Subroutine Library -OSLp are compared for different production scenarios (products and periods).
291-295
Takahashi, Marcia Tomie
fa084efd-dc46-4325-8fc9-d3f6d1f8b5d0
Yamakami, Akebo
8e244cd5-d564-4ddc-99da-ba0f5e92b365
1 July 2000
Takahashi, Marcia Tomie
fa084efd-dc46-4325-8fc9-d3f6d1f8b5d0
Yamakami, Akebo
8e244cd5-d564-4ddc-99da-ba0f5e92b365
Takahashi, Marcia Tomie and Yamakami, Akebo
(2000)
Comparison of some algorithms for manufacturing production planning.
In IFAC Proceedings Volumes (IFAC Papers-Online).
vol. 33,
.
(doi:10.1016/S1474-6670(17)38065-5).
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Conference or Workshop Item
(Paper)
Abstract
The production planning of a multiproduct multiperiod multistage manufacturing system with deterministic demand is modelled as a network flow optimisation problem with side constraints. The optimisation algorithm determines the optimum production and storage level, the demand supply level, backorder, and raw material allocation during the planning horizon. The performance of some optimisation algorithms ( Simplex, Dantzig-Wolfe and Interior Point Methods) in a serial and parallel environment using Parallel Optimisation Subroutine Library -OSLp are compared for different production scenarios (products and periods).
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Published date: 1 July 2000
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Local EPrints ID: 454143
URI: http://eprints.soton.ac.uk/id/eprint/454143
PURE UUID: 4a23b83d-5521-4b88-8c85-fb4077b8e649
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Date deposited: 01 Feb 2022 17:42
Last modified: 17 Mar 2024 04:09
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Author:
Akebo Yamakami
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