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Datasets on mathematical modeling of multi-product multi-stage production to analyze the relationship between production yield, demand, and costs

The data presented in this article are related to the research article “Optimal procurement and production planning for multi-product multi-stage production under yield uncertainty” (Talay and Özdemir-Akyıldırım, in press) [1]. The data includes: 1) the input parameters (production yield, demand, an...

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Detalles Bibliográficos
Autores principales: Talay, Işılay, Özdemir-Akyıldırım, Öznur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6355965/
https://www.ncbi.nlm.nih.gov/pubmed/30740488
http://dx.doi.org/10.1016/j.dib.2019.01.028
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author Talay, Işılay
Özdemir-Akyıldırım, Öznur
author_facet Talay, Işılay
Özdemir-Akyıldırım, Öznur
author_sort Talay, Işılay
collection PubMed
description The data presented in this article are related to the research article “Optimal procurement and production planning for multi-product multi-stage production under yield uncertainty” (Talay and Özdemir-Akyıldırım, in press) [1]. The data includes: 1) the input parameters (production yield, demand, and costs) collected through comprehensive review of the literature and diversified further to enrich the analytical results, and 2) results from mathematical modeling and analysis on the optimal procurement and semi-processed material allocation decisions for different parameter sets. The dataset is particularly constructed for a production system with two stages and three final products.
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spelling pubmed-63559652019-02-08 Datasets on mathematical modeling of multi-product multi-stage production to analyze the relationship between production yield, demand, and costs Talay, Işılay Özdemir-Akyıldırım, Öznur Data Brief Decision Science The data presented in this article are related to the research article “Optimal procurement and production planning for multi-product multi-stage production under yield uncertainty” (Talay and Özdemir-Akyıldırım, in press) [1]. The data includes: 1) the input parameters (production yield, demand, and costs) collected through comprehensive review of the literature and diversified further to enrich the analytical results, and 2) results from mathematical modeling and analysis on the optimal procurement and semi-processed material allocation decisions for different parameter sets. The dataset is particularly constructed for a production system with two stages and three final products. Elsevier 2019-01-18 /pmc/articles/PMC6355965/ /pubmed/30740488 http://dx.doi.org/10.1016/j.dib.2019.01.028 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Decision Science
Talay, Işılay
Özdemir-Akyıldırım, Öznur
Datasets on mathematical modeling of multi-product multi-stage production to analyze the relationship between production yield, demand, and costs
title Datasets on mathematical modeling of multi-product multi-stage production to analyze the relationship between production yield, demand, and costs
title_full Datasets on mathematical modeling of multi-product multi-stage production to analyze the relationship between production yield, demand, and costs
title_fullStr Datasets on mathematical modeling of multi-product multi-stage production to analyze the relationship between production yield, demand, and costs
title_full_unstemmed Datasets on mathematical modeling of multi-product multi-stage production to analyze the relationship between production yield, demand, and costs
title_short Datasets on mathematical modeling of multi-product multi-stage production to analyze the relationship between production yield, demand, and costs
title_sort datasets on mathematical modeling of multi-product multi-stage production to analyze the relationship between production yield, demand, and costs
topic Decision Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6355965/
https://www.ncbi.nlm.nih.gov/pubmed/30740488
http://dx.doi.org/10.1016/j.dib.2019.01.028
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