Model for allocation of harvesting machines to rice crops
DOI:
https://doi.org/10.18041/1900-0642/criteriolibre.2019v17n30.5802Keywords:
allocation of machinery, management of operations, mathematical modeling, rice harvestAbstract
In Colombia, the small rice plantations must be associated with nearby ones to decrease the fixed costs of harvesting. These associations contract with third parties the mowing, threshing and cleaning combined machines of the grain. In the search for efficiency, the process of scheduling the dates in which each crop will be served by the combined machines must be managed in a centralized manner, taking into account the different machines efficiencies, the conditions of the land and the cost overruns for violating the windows time associated with the harvest time of the crops. In this sense, this article develops a solution to address the described problem, based on a whole linear programming model which minimizes the overall harvest costs and the costs of harvesting the crops in premature or anticipated moments. The model was validated in one of the associations, which showed results that minimize the total cost of machinery allocation by 7.04%.
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References
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Arango, J. A.; Giraldo, J. A.; Castrillón, O. D.; Moreno, E., & López, M. (2013). Programación de máquinas paralelas no relacionadas con tiempos de montaje dependientes de la secuencia y entrada dinámica usando algoritmos genéticos. Informacion Tecnologica, 24(3), 73–84. https://doi.org/10.4067/S0718-07642013000300009
Brotons, J. M.; Manera, F. J.; Conesa, A., & Porras, I. (2015). Study on maximising profits obtained by lemon-producers based on fruit harvesting time. ITEA-INFORMACION TECNICA ECONOMICA AGRARIA, 111(4), 384-401.
Burer, S.; Jones, P. C., & Lowe, T. J. (2008). Coordinating the supply chain in the agricultural seed industry. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 185(1), 354-377. https://doi.org/10.1016/j.ejor.2006.12.015
Busato, P. (2015). A simulation model for a rice-harvesting chain. BIOSYSTEMS ENGINEERING, 129, 149-159. https://doi.org/10.1016/j.biosyste-mseng.2014.09.012
Busato, P., & Berruto, R. (2016). Minimiing manpower in rice harvesting and transportation operations. BIOSYSTEMS ENGINEERING, 151, 435-445. https://doi.org/10.1016/j.biosystemseng.2016.08.029
Cortés, E.; Álvarez, F., & González, H. (2009). La mecanización agrícola: Gestión, selección y administración de la maquinaria para las operaciones de campo. CES Medicina Veterinaria y Zootecnia, 4(2), 151-160.
Dorward, A.; Kydd, J., & Poulton, C. (2005). Beyond liberalisation: “Developmental coordination’’ policies for African smallholder agriculture. IDS BULLETIN-INSTITUTE OF DEVELOPMENT STUDIES, 36(2), 80+.
Fedearroz (2014). Manejo integrado del cultivo de arroz. Jornadas de capacitación UNALMAgrobanco. Bogotá D.C.: Produmedios.
Flua, N.; Bengtson, R. J.; Schramm, R. C.; Patel, P. M.; Walker, T. H., & Lima, M. (2006). Optimization of yield and quality parameters for the Cocodrie rice variety as a function of harvest time. APPLIED ENGINEERING IN AGRICULTURE, 22(1), 95-99.
Frisk, M.; Flisberg, P.; Rönnqvist, M., & Andersson, G. (2016). Detailed scheduling of harvest teams and robust use of harvest and transportation resources. Scandinavian Journal of Forest Research, 31(7), 681-690. https://doi.org/10.1080/02827581.2016.1206144
Furian, P.; Schlosser, J.; Giacomini, U.; Lampert, R., & Machado, P. (2013). Eficiência operacional na colheita mecanizada em lavouras de arroz irrigado. Ciência Rural, 43(3), 445-451.
Gebresenbet, G., & Ljungberg, D. (2001). Coordination and route optimization of agricultural goods transport to attenuate environmental impact. JOURNAL OF AGRICULTURAL ENGINEERING RESEARCH, 80(4), 329-342. https://doi.org/10.1006/jaer.2001.0746
Ghaffariyan, M. R.; Naghdi, R.; Ghajar, I., & Nikooy, M. (2013). Time Prediction Models and Cost Evaluation of Cut-To-Length (CTL) Harvesting Method in a Mountainous Forest. SMALL-SCALE FORESTRY, 12(2), 181-192. https://doi.org/10.1007/s11842-012-9204-4
Guzmán, M. P.; Tirado, Y. C.; Hernández, F. J., & Cortés, I. C. Á. (2018). Seis años después seguimos avanzando hacia la competitividad. Arroz, 66(533), 22-34. Recuperado de: http://www.fedearroz.com.co/noticias/noticiasd2.php?id=2762
Kaack, K., & Pedersen, H. L. (2011). Modelling of the optimum harvest time for long-termstorage of `Elstar’ and `Aroma’ (Malus domestica) apples at low oxygen concentration. ACTA AGRICULTURAE SCANDINAVICA SECTION B-SOIL AND PLANT SCIENCE, 61(6), 565-572. https://doi.org/10.1080/09064710.2010.526135
Kim, S. Y.; Park, C. K.; Gwon, H. S.; Khan, M. I., & Kim, P. J. (2015). Optimizing the harvesting stage of rye as a green manure to maximize nutrient production and to minimize methane production in mono-rice paddies. SCIENCE OF THE TOTAL ENVIRONMENT, 537, 441-446. https://doi.org/10.1016/j.scitotenv.2015.07.061
Lee, H. J.; Kim, H., & Kim, O. W. (2016). OPTIMAL HARVEST TIMING FOR MAXIMIZING GRAIN YIELD AND WHOLE KERNEL RATIO OF SHORT-GRAIN RICE. TRANSACTIONS OF THE ASABE, 59(2), 745-750.
Lindemann-Zutz, K.; Fricke, A., & Stuetzel, H. (2016). Prediction of time to harvest and its variability of broccoli (Brassica oleracea var. italica) part II. Growth model description, parameterization and field evaluation. SCIENTIA HORTICULTURAE, 200, 151-160. https://doi.org/10.1016/j.scienta.2016.01.009
MacMillan, R. H., & Burrow, J. L. (1982). COMPUTER SIMULATION OF THE SELECTION AND MANAGEMENT OF WHEAT HARVESTING MACHINERY. In National Conference Publication - Institution of Engineers, Australia (pp. 52-56). Inst of Engineers, Australia, Barton, Aust. Recuperado de: https://www.scopus.com/inward/record.uri?eid=2-s2.00020243114&partnerID=40&md5=3953d-4f8683b4b07199c466e4bc89731
Maqsood, M., & Shehzad, M. A. (2013). OPTIMIZING NITROGEN INPUT AND HARVEST TIME TO MAXIMIZE THE MAIZE FODDER YIELD IN PUNJAB, PAKISTAN. PAKISTAN JOURNAL OF AGRICULTURAL SCIENCES, 50(1), 75-81.
Márquez Delgado, J. E.; Ávila Rondón, R. L.; Gómez- Elvira González, M. Á.; Herrera Márquez, C. R.; Márquez, J.; Ávila, R., … Herrera, C. (2012). Algoritmo genético aplicado al problema de programación en procesos tecnológicos de maquinado con ambiente Flow Shop. Revista Ciencias Técnicas Agropecuarias, 21(2), 70-75. Recuperado de: http://scielo.sld.cu/scielo.php?pid=S2071-00542012000200012&script=sci_arttext%5Cnhttp://scielo.sld.cu/pdf/rcta/v21n2/rcta12212.pdf
Miranda, A.; Castells, S.; Fernández, O.; Santos, F.,& Iglesias, C. (2013). Análisis de la utilización del tiempo de turno por las cosechadoras arroz CLAAS DOMINATOR. Ciencias Técnicas Agropecuarias, 22(4), 27-31.
Miranda, A.; Iglesias, C.; Ribert, Y.; Santos, F.; Castells, S.; Miranda Caballero, A.; … Castells Hernández, S. (2011). Determinación del contenido de impurezas durante la cosecha de arroz. Revista de Ciencias Técnicas Agropecuarias, 20(4), 53-56.
Munoz, C.; Avila, J.; Salvo, S., & Huircan, J. I. (2012). Prediction of harvest start date in highbush blueberry using time series regression models with correlated errors. SCIENTIA HORTICULTURAE, 138, 165-170. https://doi.org/10.1016/j.scienta.2012.02.023
Nalley, L.; Dixon, B.; Tack, J.; Barkley, A., & Jagadish, K. (2016). Optimal harvest moisture content for maximizing mid-south rice milling yields and returns. Agronomy Journal, 108(2), 701-712. https://doi.org/10.2134/agronj2015.0408
Olego, M. A.; Alvarez, J. C.; Tobes, A.; de Paz, J. M.; Coque, J. J. R., & Garzon-Jimeno, E. (2016). Determining optimum harvest time under Mediterranean conditions: developing a new model for measuring L-malic acid concentration in red grapes. AUSTRALIAN JOURNAL OF GRAPE AND WINE RESEARCH, 22(2), 232-239. https://doi.org/10.1111/ajgw.12181
Osorio, C. J.; Castrillon, O.; Toro, J., & Orejuela, J. P. (2008). Modelo de programación jerárquica de la producción en un Job shop flexible con interrupciones y tiempos de alistamiento dependientes de la secuencia. Revista Ingeniería E Investigación, 28(2), 72-79.
Pande, R. (2006). Profits and politics: Coordinating technology adoption in agriculture. JOURNAL OF DEVELOPMENT ECONOMICS, 81(2), 299-315. https://doi.org/10.1016/j.deveco.2005.06.012
Ramburan, S. (2014). Optimizing Sugarcane Cultivar Choice and Time of Harvest for Frost-Prone Environments in South Africa. AGRONOMY JOURNAL, 106(6), 2035-2042. https://doi.org/10.2134/agronj14.0159
Razali, M. H.; Ismail, W. I. W.; Ramli, A. R.; Sulaiman, M. N., & Harun, M. H. (2011). Prediction model for estimating optimum harvesting time of oil palm fresh fruit bunches. JOURNAL OF FOOD AGRICULTURE & ENVIRONMENT, 9(3-4, 1), 570-575.
Sánchez, J., & Meneses, O. (2012). Parámetros que influyen en la calidad industrial del arroz cosechado en el municipio La Sierpe. La Habana. Recuperado de: http://www.eumed.net/cursecon/ecolat/cu/2012/sdmr.html
Serna, H. (2008). Gerencia estratégica. Bogotá D.C.: 3R Editores. Siebenmorgen, T. J.; Bautista, R. C., & Counce, P. A. (2007). Optimal harvest moisture contents for maximizing milling quality of longand mediumgrainrice cultivars. Applied Engineering in Agriculture, 23(4), 517-527. https://doi.org/10.13031/2013.23476
Steger-Mate, M.; Ficzek, G.; Kallay, E.; Bujdoso, G.; Barta, J., & Toth, M. (2010). OPTIMISING HARVEST TIME OF SOUR CHERRY CULTIVARS ON THE BASIS OF QUALITY PARAMETERS. ACTA ALIMENTARIA, 39(1), 59-68. https://doi.org/10.1556/AAlim.39.2010.1.6
Zheng, Y.; Jiang, W.; Silva, E. N.; Mao, L.; Hannaway, D. B., & Lu, H. (2012). Optimization of shade condition and harvest time for Dendrobium candidum plants based on leaf gas exchange, alkaloids and polysaccharides contents. PLANT OMICS, 5(3), 253-260.
Zhu, X.; Yan, R., & Wang, H. (2015). Harvesting scheduling operations for the machinery owners under multi-farmland, multi-type situation with time window-An empirical study arising in agricultural contexts in China. INMATEH – Agricultural Engineering, 46(2), 175-182. Recuperado de: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941268254&partnerID=40&md5=4cb373296ebb587ce57ae7375698dbcc
Arango, J. A.; Giraldo, J. A.; Castrillón, O. D.; Moreno, E., & López, M. (2013). Programación de máquinas paralelas no relacionadas con tiempos de montaje dependientes de la secuencia y entrada dinámica usando algoritmos genéticos. Informacion Tecnologica, 24(3), 73–84. https://doi.org/10.4067/S0718-07642013000300009
Brotons, J. M.; Manera, F. J.; Conesa, A., & Porras, I. (2015). Study on maximising profits obtained by lemon-producers based on fruit harvesting time. ITEA-INFORMACION TECNICA ECONOMICA AGRARIA, 111(4), 384-401.
Burer, S.; Jones, P. C., & Lowe, T. J. (2008). Coordinating the supply chain in the agricultural seed industry. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 185(1), 354-377. https://doi.org/10.1016/j.ejor.2006.12.015
Busato, P. (2015). A simulation model for a rice-harvesting chain. BIOSYSTEMS ENGINEERING, 129, 149-159. https://doi.org/10.1016/j.biosyste-mseng.2014.09.012
Busato, P., & Berruto, R. (2016). Minimiing manpower in rice harvesting and transportation operations. BIOSYSTEMS ENGINEERING, 151, 435-445. https://doi.org/10.1016/j.biosystemseng.2016.08.029
Cortés, E.; Álvarez, F., & González, H. (2009). La mecanización agrícola: Gestión, selección y administración de la maquinaria para las operaciones de campo. CES Medicina Veterinaria y Zootecnia, 4(2), 151-160.
Dorward, A.; Kydd, J., & Poulton, C. (2005). Beyond liberalisation: “Developmental coordination’’ policies for African smallholder agriculture. IDS BULLETIN-INSTITUTE OF DEVELOPMENT STUDIES, 36(2), 80+.
Fedearroz (2014). Manejo integrado del cultivo de arroz. Jornadas de capacitación UNALMAgrobanco. Bogotá D.C.: Produmedios.
Flua, N.; Bengtson, R. J.; Schramm, R. C.; Patel, P. M.; Walker, T. H., & Lima, M. (2006). Optimization of yield and quality parameters for the Cocodrie rice variety as a function of harvest time. APPLIED ENGINEERING IN AGRICULTURE, 22(1), 95-99.
Frisk, M.; Flisberg, P.; Rönnqvist, M., & Andersson, G. (2016). Detailed scheduling of harvest teams and robust use of harvest and transportation resources. Scandinavian Journal of Forest Research, 31(7), 681-690. https://doi.org/10.1080/02827581.2016.1206144
Furian, P.; Schlosser, J.; Giacomini, U.; Lampert, R., & Machado, P. (2013). Eficiência operacional na colheita mecanizada em lavouras de arroz irrigado. Ciência Rural, 43(3), 445-451.
Gebresenbet, G., & Ljungberg, D. (2001). Coordination and route optimization of agricultural goods transport to attenuate environmental impact. JOURNAL OF AGRICULTURAL ENGINEERING RESEARCH, 80(4), 329-342. https://doi.org/10.1006/jaer.2001.0746
Ghaffariyan, M. R.; Naghdi, R.; Ghajar, I., & Nikooy, M. (2013). Time Prediction Models and Cost Evaluation of Cut-To-Length (CTL) Harvesting Method in a Mountainous Forest. SMALL-SCALE FORESTRY, 12(2), 181-192. https://doi.org/10.1007/s11842-012-9204-4
Guzmán, M. P.; Tirado, Y. C.; Hernández, F. J., & Cortés, I. C. Á. (2018). Seis años después seguimos avanzando hacia la competitividad. Arroz, 66(533), 22-34. Recuperado de: http://www.fedearroz.com.co/noticias/noticiasd2.php?id=2762
Kaack, K., & Pedersen, H. L. (2011). Modelling of the optimum harvest time for long-termstorage of `Elstar’ and `Aroma’ (Malus domestica) apples at low oxygen concentration. ACTA AGRICULTURAE SCANDINAVICA SECTION B-SOIL AND PLANT SCIENCE, 61(6), 565-572. https://doi.org/10.1080/09064710.2010.526135
Kim, S. Y.; Park, C. K.; Gwon, H. S.; Khan, M. I., & Kim, P. J. (2015). Optimizing the harvesting stage of rye as a green manure to maximize nutrient production and to minimize methane production in mono-rice paddies. SCIENCE OF THE TOTAL ENVIRONMENT, 537, 441-446. https://doi.org/10.1016/j.scitotenv.2015.07.061
Lee, H. J.; Kim, H., & Kim, O. W. (2016). OPTIMAL HARVEST TIMING FOR MAXIMIZING GRAIN YIELD AND WHOLE KERNEL RATIO OF SHORT-GRAIN RICE. TRANSACTIONS OF THE ASABE, 59(2), 745-750.
Lindemann-Zutz, K.; Fricke, A., & Stuetzel, H. (2016). Prediction of time to harvest and its variability of broccoli (Brassica oleracea var. italica) part II. Growth model description, parameterization and field evaluation. SCIENTIA HORTICULTURAE, 200, 151-160. https://doi.org/10.1016/j.scienta.2016.01.009
MacMillan, R. H., & Burrow, J. L. (1982). COMPUTER SIMULATION OF THE SELECTION AND MANAGEMENT OF WHEAT HARVESTING MACHINERY. In National Conference Publication - Institution of Engineers, Australia (pp. 52-56). Inst of Engineers, Australia, Barton, Aust. Recuperado de: https://www.scopus.com/inward/record.uri?eid=2-s2.00020243114&partnerID=40&md5=3953d-4f8683b4b07199c466e4bc89731
Maqsood, M., & Shehzad, M. A. (2013). OPTIMIZING NITROGEN INPUT AND HARVEST TIME TO MAXIMIZE THE MAIZE FODDER YIELD IN PUNJAB, PAKISTAN. PAKISTAN JOURNAL OF AGRICULTURAL SCIENCES, 50(1), 75-81.
Márquez Delgado, J. E.; Ávila Rondón, R. L.; Gómez- Elvira González, M. Á.; Herrera Márquez, C. R.; Márquez, J.; Ávila, R., … Herrera, C. (2012). Algoritmo genético aplicado al problema de programación en procesos tecnológicos de maquinado con ambiente Flow Shop. Revista Ciencias Técnicas Agropecuarias, 21(2), 70-75. Recuperado de: http://scielo.sld.cu/scielo.php?pid=S2071-00542012000200012&script=sci_arttext%5Cnhttp://scielo.sld.cu/pdf/rcta/v21n2/rcta12212.pdf
Miranda, A.; Castells, S.; Fernández, O.; Santos, F.,& Iglesias, C. (2013). Análisis de la utilización del tiempo de turno por las cosechadoras arroz CLAAS DOMINATOR. Ciencias Técnicas Agropecuarias, 22(4), 27-31.
Miranda, A.; Iglesias, C.; Ribert, Y.; Santos, F.; Castells, S.; Miranda Caballero, A.; … Castells Hernández, S. (2011). Determinación del contenido de impurezas durante la cosecha de arroz. Revista de Ciencias Técnicas Agropecuarias, 20(4), 53-56.
Munoz, C.; Avila, J.; Salvo, S., & Huircan, J. I. (2012). Prediction of harvest start date in highbush blueberry using time series regression models with correlated errors. SCIENTIA HORTICULTURAE, 138, 165-170. https://doi.org/10.1016/j.scienta.2012.02.023
Nalley, L.; Dixon, B.; Tack, J.; Barkley, A., & Jagadish, K. (2016). Optimal harvest moisture content for maximizing mid-south rice milling yields and returns. Agronomy Journal, 108(2), 701-712. https://doi.org/10.2134/agronj2015.0408
Olego, M. A.; Alvarez, J. C.; Tobes, A.; de Paz, J. M.; Coque, J. J. R., & Garzon-Jimeno, E. (2016). Determining optimum harvest time under Mediterranean conditions: developing a new model for measuring L-malic acid concentration in red grapes. AUSTRALIAN JOURNAL OF GRAPE AND WINE RESEARCH, 22(2), 232-239. https://doi.org/10.1111/ajgw.12181
Osorio, C. J.; Castrillon, O.; Toro, J., & Orejuela, J. P. (2008). Modelo de programación jerárquica de la producción en un Job shop flexible con interrupciones y tiempos de alistamiento dependientes de la secuencia. Revista Ingeniería E Investigación, 28(2), 72-79.
Pande, R. (2006). Profits and politics: Coordinating technology adoption in agriculture. JOURNAL OF DEVELOPMENT ECONOMICS, 81(2), 299-315. https://doi.org/10.1016/j.deveco.2005.06.012
Ramburan, S. (2014). Optimizing Sugarcane Cultivar Choice and Time of Harvest for Frost-Prone Environments in South Africa. AGRONOMY JOURNAL, 106(6), 2035-2042. https://doi.org/10.2134/agronj14.0159
Razali, M. H.; Ismail, W. I. W.; Ramli, A. R.; Sulaiman, M. N., & Harun, M. H. (2011). Prediction model for estimating optimum harvesting time of oil palm fresh fruit bunches. JOURNAL OF FOOD AGRICULTURE & ENVIRONMENT, 9(3-4, 1), 570-575.
Sánchez, J., & Meneses, O. (2012). Parámetros que influyen en la calidad industrial del arroz cosechado en el municipio La Sierpe. La Habana. Recuperado de: http://www.eumed.net/cursecon/ecolat/cu/2012/sdmr.html
Serna, H. (2008). Gerencia estratégica. Bogotá D.C.: 3R Editores. Siebenmorgen, T. J.; Bautista, R. C., & Counce, P. A. (2007). Optimal harvest moisture contents for maximizing milling quality of longand mediumgrainrice cultivars. Applied Engineering in Agriculture, 23(4), 517-527. https://doi.org/10.13031/2013.23476
Steger-Mate, M.; Ficzek, G.; Kallay, E.; Bujdoso, G.; Barta, J., & Toth, M. (2010). OPTIMISING HARVEST TIME OF SOUR CHERRY CULTIVARS ON THE BASIS OF QUALITY PARAMETERS. ACTA ALIMENTARIA, 39(1), 59-68. https://doi.org/10.1556/AAlim.39.2010.1.6
Zheng, Y.; Jiang, W.; Silva, E. N.; Mao, L.; Hannaway, D. B., & Lu, H. (2012). Optimization of shade condition and harvest time for Dendrobium candidum plants based on leaf gas exchange, alkaloids and polysaccharides contents. PLANT OMICS, 5(3), 253-260.
Zhu, X.; Yan, R., & Wang, H. (2015). Harvesting scheduling operations for the machinery owners under multi-farmland, multi-type situation with time window-An empirical study arising in agricultural contexts in China. INMATEH – Agricultural Engineering, 46(2), 175-182. Recuperado de: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941268254&partnerID=40&md5=4cb373296ebb587ce57ae7375698dbcc
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2019-12-04
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