Reinforcement Learning system to capture value from Brazilian post-harvest offers
This study assesses the value capture of a result-oriented Product-Service System offer that constitutes a post-harvest solution. Applying the reinforcement learning reward system and general linear models, we identified the Brazilian farmer's propensities to choose different products and servi...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Information Processing in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317323000641 |
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| author | Fernando Henrique Lermen Vera Lúcia Milani Martins Marcia Elisa Echeveste Filipe Ribeiro Carla Beatriz da Luz Peralta José Luis Duarte Ribeiro |
| author_facet | Fernando Henrique Lermen Vera Lúcia Milani Martins Marcia Elisa Echeveste Filipe Ribeiro Carla Beatriz da Luz Peralta José Luis Duarte Ribeiro |
| author_sort | Fernando Henrique Lermen |
| collection | DOAJ |
| description | This study assesses the value capture of a result-oriented Product-Service System offer that constitutes a post-harvest solution. Applying the reinforcement learning reward system and general linear models, we identified the Brazilian farmer's propensities to choose different products and services from the proposed system. Reinforcement learning enables one to understand the choice process by rewarding the attributes selected and applying penalties to those not chosen. Regarding product options, farmers' most valued attributes were extended capacity, fixed installation, automatic dryer, and CO2 emission control, considering the investigated system. Regarding service options, the farmers opted for maintenance plans, performance reports, no photovoltaic energy, and purchase over the rental modality. These results assist managers through a reward learning system that constantly updates the value assigned by farmers to product and service attributes. They allow real-time visualization of changes in farmers' preferences regarding the product-service system configurations. |
| format | Article |
| id | doaj-art-186e9845e7214be581d46075f54a3863 |
| institution | Kabale University |
| issn | 2214-3173 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj-art-186e9845e7214be581d46075f54a38632024-12-11T05:56:34ZengElsevierInformation Processing in Agriculture2214-31732024-12-01114499511Reinforcement Learning system to capture value from Brazilian post-harvest offersFernando Henrique Lermen0Vera Lúcia Milani Martins1Marcia Elisa Echeveste2Filipe Ribeiro3Carla Beatriz da Luz Peralta4José Luis Duarte Ribeiro5Industrial Engineering Department, Universidade Estadual do Paraná, R. Comendador Correia Júnior, 117, 83203-560 Paranaguá, Brazil; Industrial Engineering Department, Universidad Tecnológica del Perú, Av. Arequipa 265, 15046 Lima, Peru; Corresponding author at: Department of Industrial Engineering, Universidad Tecnológica de Peru, Av. Arequipa 265, 15046, Lima, Peru.Department of Mathematics, Statistic, and Physics, Federal Institute of Education, Science and Technology of Rio Grande do Sul, R. Cel. Vicente, 281, 90030-041 Porto Alegre, BrazilGraduate Program of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha 99, 90035-190 Porto Alegre, Brazil; Institute of Mathematics and Statistics, Federal University of Rio Grande do Sul, Av. Bento Gonçalves 9500, 91509-900 Porto Alegre, BrazilHP Inc., Av. Ipiranga, 6681, 90619-900 Porto Alegre, BrazilDepartment of Industrial Engineering, Federal University of Pampa, Av. Maria Anunciação Gomes Godoy, 1650, 96460-000 Bagé, BrazilGraduate Program of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha 99, 90035-190 Porto Alegre, BrazilThis study assesses the value capture of a result-oriented Product-Service System offer that constitutes a post-harvest solution. Applying the reinforcement learning reward system and general linear models, we identified the Brazilian farmer's propensities to choose different products and services from the proposed system. Reinforcement learning enables one to understand the choice process by rewarding the attributes selected and applying penalties to those not chosen. Regarding product options, farmers' most valued attributes were extended capacity, fixed installation, automatic dryer, and CO2 emission control, considering the investigated system. Regarding service options, the farmers opted for maintenance plans, performance reports, no photovoltaic energy, and purchase over the rental modality. These results assist managers through a reward learning system that constantly updates the value assigned by farmers to product and service attributes. They allow real-time visualization of changes in farmers' preferences regarding the product-service system configurations.http://www.sciencedirect.com/science/article/pii/S2214317323000641Product-service systemAgricultureValue captureChoice experimentsReinforcement learning |
| spellingShingle | Fernando Henrique Lermen Vera Lúcia Milani Martins Marcia Elisa Echeveste Filipe Ribeiro Carla Beatriz da Luz Peralta José Luis Duarte Ribeiro Reinforcement Learning system to capture value from Brazilian post-harvest offers Information Processing in Agriculture Product-service system Agriculture Value capture Choice experiments Reinforcement learning |
| title | Reinforcement Learning system to capture value from Brazilian post-harvest offers |
| title_full | Reinforcement Learning system to capture value from Brazilian post-harvest offers |
| title_fullStr | Reinforcement Learning system to capture value from Brazilian post-harvest offers |
| title_full_unstemmed | Reinforcement Learning system to capture value from Brazilian post-harvest offers |
| title_short | Reinforcement Learning system to capture value from Brazilian post-harvest offers |
| title_sort | reinforcement learning system to capture value from brazilian post harvest offers |
| topic | Product-service system Agriculture Value capture Choice experiments Reinforcement learning |
| url | http://www.sciencedirect.com/science/article/pii/S2214317323000641 |
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