Application of a production planning model based on linear programming and machine learning techniques
The absence of efficient optimization methods combined with Artificial Intelligence concepts has led to inefficiencies and high costs in the production planning of organizations. Thus, this study aims to optimize production planning in an electronic equipment company, using Linear Programming and M...
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| Format: | Article |
| Language: | English |
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Institute of Technology and Education Galileo da Amazônia
2024-02-01
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| Series: | ITEGAM-JETIA |
| Online Access: | https://itegam-jetia.org/journal/index.php/jetia/article/view/920 |
| _version_ | 1797290186170695680 |
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| author | Lucas Vianna Vaz Marcelo Carneiro Gonçalves Izamara Cristina Palheta Dias Elpídio Oscar Benitez Nara |
| author_facet | Lucas Vianna Vaz Marcelo Carneiro Gonçalves Izamara Cristina Palheta Dias Elpídio Oscar Benitez Nara |
| author_sort | Lucas Vianna Vaz |
| collection | DOAJ |
| description |
The absence of efficient optimization methods combined with Artificial Intelligence concepts has led to inefficiencies and high costs in the production planning of organizations. Thus, this study aims to optimize production planning in an electronic equipment company, using Linear Programming and Machine Learning to support assertive and efficient decisions. The methodological process comprises seven stages: Literature review; Collection and analysis of production data; Application of Machine Learning methods for modelling; Selection of the best model; Development and application of the Linear Programming model; Analysis of results; Validation with stakeholders. The approach resulted in optimized production planning, capable of reducing operating costs and assisting in the daily decision-making of the organization. The Machine Learning forecasting technique achieved an average error of 9%, demonstrating its accuracy in forecasting future demand. This study evidences a robust and promising approach to improve efficiency and effectiveness in production planning operations. In this context, the union between Operations Research and Machine Learning emerges as a response to existing gaps and a driving direction for continuously optimizing these crucial processes.
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| first_indexed | 2024-03-07T19:16:53Z |
| format | Article |
| id | doaj.art-2fe760f5aa9143249100663a003be3b9 |
| institution | Directory Open Access Journal |
| issn | 2447-0228 |
| language | English |
| last_indexed | 2024-03-07T19:16:53Z |
| publishDate | 2024-02-01 |
| publisher | Institute of Technology and Education Galileo da Amazônia |
| record_format | Article |
| series | ITEGAM-JETIA |
| spelling | doaj.art-2fe760f5aa9143249100663a003be3b92024-02-29T12:56:06ZengInstitute of Technology and Education Galileo da AmazôniaITEGAM-JETIA2447-02282024-02-01104510.5935/jetia.v10i45.920Application of a production planning model based on linear programming and machine learning techniquesLucas Vianna Vaz0Marcelo Carneiro Gonçalves1Izamara Cristina Palheta Dias2Elpídio Oscar Benitez Nara3Industrial Engineering, Pontifical Catholic University of Paraná - PUCPR, Curitiba, Paraná, BrazilIndustrial Engineering, Pontifical Catholic University of Paraná - PUCPR, Curitiba, Paraná, BrazilIndustrial Engineering, Pontifical Catholic University of Paraná - PUCPR, Curitiba, Paraná, BrazilIndustrial Engineering, Pontifical Catholic University of Paraná - PUCPR, Curitiba, Paraná, Brazil The absence of efficient optimization methods combined with Artificial Intelligence concepts has led to inefficiencies and high costs in the production planning of organizations. Thus, this study aims to optimize production planning in an electronic equipment company, using Linear Programming and Machine Learning to support assertive and efficient decisions. The methodological process comprises seven stages: Literature review; Collection and analysis of production data; Application of Machine Learning methods for modelling; Selection of the best model; Development and application of the Linear Programming model; Analysis of results; Validation with stakeholders. The approach resulted in optimized production planning, capable of reducing operating costs and assisting in the daily decision-making of the organization. The Machine Learning forecasting technique achieved an average error of 9%, demonstrating its accuracy in forecasting future demand. This study evidences a robust and promising approach to improve efficiency and effectiveness in production planning operations. In this context, the union between Operations Research and Machine Learning emerges as a response to existing gaps and a driving direction for continuously optimizing these crucial processes. https://itegam-jetia.org/journal/index.php/jetia/article/view/920 |
| spellingShingle | Lucas Vianna Vaz Marcelo Carneiro Gonçalves Izamara Cristina Palheta Dias Elpídio Oscar Benitez Nara Application of a production planning model based on linear programming and machine learning techniques ITEGAM-JETIA |
| title | Application of a production planning model based on linear programming and machine learning techniques |
| title_full | Application of a production planning model based on linear programming and machine learning techniques |
| title_fullStr | Application of a production planning model based on linear programming and machine learning techniques |
| title_full_unstemmed | Application of a production planning model based on linear programming and machine learning techniques |
| title_short | Application of a production planning model based on linear programming and machine learning techniques |
| title_sort | application of a production planning model based on linear programming and machine learning techniques |
| url | https://itegam-jetia.org/journal/index.php/jetia/article/view/920 |
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