Machine Learning Applied to Logistics Decision Making: Improvements to the Soybean Seed Classification Process
Soybean seed classification is a relevant and time-consuming process for Brazilian agribusiness cooperatives. This activity can generate queues and waiting times that directly affect logistics costs. This is the reason why it is so important to properly allocate resources, considering the most relev...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/19/10904 |
| _version_ | 1797576165803687936 |
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| author | Djonathan Luiz de Oliveira Quadras Ian Cavalcante Mirko Kück Lúcio Galvão Mendes Enzo Morosini Frazzon |
| author_facet | Djonathan Luiz de Oliveira Quadras Ian Cavalcante Mirko Kück Lúcio Galvão Mendes Enzo Morosini Frazzon |
| author_sort | Djonathan Luiz de Oliveira Quadras |
| collection | DOAJ |
| description | Soybean seed classification is a relevant and time-consuming process for Brazilian agribusiness cooperatives. This activity can generate queues and waiting times that directly affect logistics costs. This is the reason why it is so important to properly allocate resources, considering the most relevant factors that can influence their performance. This paper aims to present an approach to predicting the average lead time and waiting queue time for the soybean seed classification process, which supports the decision regarding the number of workers and machines to be deployed in the process. The originality of the paper relies on the applied approach, which combines discrete event simulation with machine learning algorithms in a real-world applied case. The approach comprises three steps: data collection to structure the simulation scenarios; simulation runs to generate artificial historical data; and machine learning applications to predict lead and queuing times. As a result, various scenarios using the data generated by machine learning were simulated, making it possible to choose the one that generated the best trade-off between performance, investments, and operational costs. The approach can be adapted to support the solution of different logistic-related decision-making problems that combine human and equipment resources. |
| first_indexed | 2024-03-10T21:48:23Z |
| format | Article |
| id | doaj.art-2db124b84b934ff4b0e4c94f15cd6c61 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:48:23Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2db124b84b934ff4b0e4c94f15cd6c612023-11-19T14:05:49ZengMDPI AGApplied Sciences2076-34172023-09-0113191090410.3390/app131910904Machine Learning Applied to Logistics Decision Making: Improvements to the Soybean Seed Classification ProcessDjonathan Luiz de Oliveira Quadras0Ian Cavalcante1Mirko Kück2Lúcio Galvão Mendes3Enzo Morosini Frazzon4Graduate Program in Production Engineering, Federal University of Santa Catarina, Florianopolis 88040-970, SC, BrazilNeosilos, Curitiba 81280-340, PR, BrazilFaculty of Production Engineering, University of Bremen, 28359 Bremen, GermanyGraduate Program in Production Engineering, Federal University of Santa Catarina, Florianopolis 88040-970, SC, BrazilGraduate Program in Production Engineering, Federal University of Santa Catarina, Florianopolis 88040-970, SC, BrazilSoybean seed classification is a relevant and time-consuming process for Brazilian agribusiness cooperatives. This activity can generate queues and waiting times that directly affect logistics costs. This is the reason why it is so important to properly allocate resources, considering the most relevant factors that can influence their performance. This paper aims to present an approach to predicting the average lead time and waiting queue time for the soybean seed classification process, which supports the decision regarding the number of workers and machines to be deployed in the process. The originality of the paper relies on the applied approach, which combines discrete event simulation with machine learning algorithms in a real-world applied case. The approach comprises three steps: data collection to structure the simulation scenarios; simulation runs to generate artificial historical data; and machine learning applications to predict lead and queuing times. As a result, various scenarios using the data generated by machine learning were simulated, making it possible to choose the one that generated the best trade-off between performance, investments, and operational costs. The approach can be adapted to support the solution of different logistic-related decision-making problems that combine human and equipment resources.https://www.mdpi.com/2076-3417/13/19/10904machine learningdiscrete event simulationagribusiness 4.0 |
| spellingShingle | Djonathan Luiz de Oliveira Quadras Ian Cavalcante Mirko Kück Lúcio Galvão Mendes Enzo Morosini Frazzon Machine Learning Applied to Logistics Decision Making: Improvements to the Soybean Seed Classification Process Applied Sciences machine learning discrete event simulation agribusiness 4.0 |
| title | Machine Learning Applied to Logistics Decision Making: Improvements to the Soybean Seed Classification Process |
| title_full | Machine Learning Applied to Logistics Decision Making: Improvements to the Soybean Seed Classification Process |
| title_fullStr | Machine Learning Applied to Logistics Decision Making: Improvements to the Soybean Seed Classification Process |
| title_full_unstemmed | Machine Learning Applied to Logistics Decision Making: Improvements to the Soybean Seed Classification Process |
| title_short | Machine Learning Applied to Logistics Decision Making: Improvements to the Soybean Seed Classification Process |
| title_sort | machine learning applied to logistics decision making improvements to the soybean seed classification process |
| topic | machine learning discrete event simulation agribusiness 4.0 |
| url | https://www.mdpi.com/2076-3417/13/19/10904 |
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