Production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming.
In this article two multi-stage stochastic linear programming models are developed, one applying the stochastic programming solver integrated by Lingo 17.0 optimization software that utilizes an approximation using an identical conditional sampling and Latin-hyper-square techniques to reduce the sam...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0252801 |
| _version_ | 1818400866386837504 |
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| author | José Emmanuel Gómez-Rocha Eva Selene Hernández-Gress Héctor Rivera-Gómez |
| author_facet | José Emmanuel Gómez-Rocha Eva Selene Hernández-Gress Héctor Rivera-Gómez |
| author_sort | José Emmanuel Gómez-Rocha |
| collection | DOAJ |
| description | In this article two multi-stage stochastic linear programming models are developed, one applying the stochastic programming solver integrated by Lingo 17.0 optimization software that utilizes an approximation using an identical conditional sampling and Latin-hyper-square techniques to reduce the sample variance, associating the probability distributions to normal distributions with defined mean and standard deviation; and a second proposed model with a discrete distribution with 3 values and their respective probabilities of occurrence. In both cases, a scenario tree is generated. The models developed are applied to an aggregate production plan (APP) for a furniture manufacturing company located in the state of Hidalgo, Mexico, which has important clients throughout the country. Production capacity and demand are defined as random variables of the model. The main purpose of this research is to determine a feasible solution to the aggregate production plan in a reasonable computational time. The developed models were compared and analyzed. Moreover, this work was complemented with a sensitivity analysis; varying the percentage of service level, also, varying the stochastic parameters (mean and standard deviation) to test how these variations impact in the solution and decision variables. |
| first_indexed | 2024-12-14T07:43:23Z |
| format | Article |
| id | doaj.art-4a9a6aa71af941dfb24c5410fc328ee9 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T07:43:23Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-4a9a6aa71af941dfb24c5410fc328ee92022-12-21T23:10:58ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01166e025280110.1371/journal.pone.0252801Production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming.José Emmanuel Gómez-RochaEva Selene Hernández-GressHéctor Rivera-GómezIn this article two multi-stage stochastic linear programming models are developed, one applying the stochastic programming solver integrated by Lingo 17.0 optimization software that utilizes an approximation using an identical conditional sampling and Latin-hyper-square techniques to reduce the sample variance, associating the probability distributions to normal distributions with defined mean and standard deviation; and a second proposed model with a discrete distribution with 3 values and their respective probabilities of occurrence. In both cases, a scenario tree is generated. The models developed are applied to an aggregate production plan (APP) for a furniture manufacturing company located in the state of Hidalgo, Mexico, which has important clients throughout the country. Production capacity and demand are defined as random variables of the model. The main purpose of this research is to determine a feasible solution to the aggregate production plan in a reasonable computational time. The developed models were compared and analyzed. Moreover, this work was complemented with a sensitivity analysis; varying the percentage of service level, also, varying the stochastic parameters (mean and standard deviation) to test how these variations impact in the solution and decision variables.https://doi.org/10.1371/journal.pone.0252801 |
| spellingShingle | José Emmanuel Gómez-Rocha Eva Selene Hernández-Gress Héctor Rivera-Gómez Production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming. PLoS ONE |
| title | Production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming. |
| title_full | Production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming. |
| title_fullStr | Production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming. |
| title_full_unstemmed | Production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming. |
| title_short | Production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming. |
| title_sort | production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming |
| url | https://doi.org/10.1371/journal.pone.0252801 |
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