Sistema para planificación de cultivos agrícolas

El problema de investigación fue: ¿Cuál fue el efecto del uso de un sistema de planificación de cultivos agrícolas en el tiempo de planificación? El objetivo fue determinar el efecto del uso de un sistema de planificación de cultivos agrícolas en el tiempo de planificación. Asimismo, el enfoque de la investigación fue cuantitativo con un diseño de tipo descriptivo, donde se tomó como población 10 zonas de cultivo y 5 tipos de productos para realizar el proceso de reducción de tiempos de planificación desarrollados manualmente por el agricultor. El instrumento para la recolección de datos fue una hoja de registro de datos que se obtuvo a través los tiempos de simulación en la planificación de tipos de productos en diferentes zonas, reduciendo los tiempos de procesamiento. Los resultados fueron satisfactorios al utilizar Microsoft Visual Studio con un entorno de desarrollo integrado compatible con el lenguaje de programación C# y la conexión a la base de datos. El sistema mediante el cual se ejecutó la planificación redujo el tiempo del proceso de planificación favoreciendo un mejor desarrollo de la agricultura porque logró minimizar el tiempo y mejorar las respuestas de planificación manual. Finalmente, se recomienda ampliar el proyecto con más parámetros para mejorar los resultados de la proyección y el proceso agrícola y tener una mejor planificación futura

Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem

[EN] Mathematical programming models are invaluable tools at decision making, assisting managers to uncover otherwise unattainable means to optimize their processes. However, the value they provide is only as good as their capacity to capture the process domain. This information can only be obtained from stakeholders, i.e., clients or users, who can hardly communicate the requirements clearly and completely. Besides, existing conceptual models of mathematical programming models are not standardized, nor is the process of deriving the mathematical programming model from the concept model, which remains ad hoc. In this paper, we propose an agile methodology to construct mathematical programming models based on two techniques from requirements engineering that have been proven effective at requirements elicitation: the language extended lexicon (LEL) and scenarios. Using the pair of LEL + scenarios allows to create a conceptual model that is clear and complete enough to derive a mathematical programming model that effectively captures the business domain. We also define an ontology to describe the pair LEL + scenarios, which has been implemented with a semantic mediawiki and allows the collaborative construction of the conceptual model and the semi-automatic derivation of mathematical programming model elements. The process is applied and validated in a known fresh tomato packing optimization problem. This proposal can be of high relevance for the development and implementation of mathematical programming models for optimizing agriculture and supply chain management related processes in order to fill the current gap between mathematical programming models in the theory and the practice.This work was supported by the European Commission, project RUC-APS, grant number 691249, funded by the European Union's research and innovation programme under the H2020 Marie SklodowskaCurie Actions; and the Argentinian National Agency for Scientific and Technical Promotion (ANPCyT), grant number PICT-2015-3000.Garrido, A.; Antonelli, L.; Martin, J.; Alemany Díaz, MDM.; Mula, J. (2020). Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem. Computers and Electronics in Agriculture. 170:1-14. https://doi.org/10.1016/j.compag.2020.105242S114170Alemany, M., Ortiz, A., & Fuertes-Miquel, V. S. (2018). A decision support tool for the order promising process with product homogeneity requirements in hybrid Make-To-Stock and Make-To-Order environments. Application to a ceramic tile company. Computers & Industrial Engineering, 122, 219-234. doi:10.1016/j.cie.2018.05.040Alemany, M. M. E., Alarcón, F., Lario, F.-C., & Boj, J. J. (2011). An application to support the temporal and spatial distributed decision-making process in supply chain collaborative planning. Computers in Industry, 62(5), 519-540. doi:10.1016/j.compind.2011.02.002Alemany, M. M. E., Lario, F.-C., Ortiz, A., & Gómez, F. (2013). Available-To-Promise modeling for multi-plant manufacturing characterized by lack of homogeneity in the product: An illustration of a ceramic case. Applied Mathematical Modelling, 37(5), 3380-3398. doi:10.1016/j.apm.2012.07.022Alexander, I., & Maiden, N. (2004). Scenarios, stories, and use cases: the modern basis for system development. Computing and Control Engineering, 15(5), 24-29. doi:10.1049/cce:20040505Armengol, Á., Mula, J., Díaz-Madroñero, M., & Pelkonen, J. (2015). Conceptual Model for Associated Costs of the Internationalisation of Operations. Enhancing Synergies in a Collaborative Environment, 181-188. doi:10.1007/978-3-319-14078-0_21Baraniuk, R. G., Burrus, C. S., Johnson, D. H., & Jones, D. L. (2004). Signal processing education - Sharing knowledge and building communities in Signal Processing. IEEE Signal Processing Magazine, 21(5), 10-16. doi:10.1109/msp.2004.1328080Cid-Garcia, N. M., & Ibarra-Rojas, O. J. (2019). An integrated approach for the rectangular delineation of management zones and the crop planning problems. Computers and Electronics in Agriculture, 164, 104925. doi:10.1016/j.compag.2019.104925Dominguez-Ballesteros, B., Mitra, G., Lucas, C., & Koutsoukis, N.-S. (2002). Modelling and solving environments for mathematical programming (MP): a status review and new directions. Journal of the Operational Research Society, 53(10), 1072-1092. doi:10.1057/palgrave.jors.2601361Esteso, A., Alemany, M. M. E., Ortiz, Á., & Peidro, D. (2018). A multi-objective model for inventory and planned production reassignment to committed orders with homogeneity requirements. Computers & Industrial Engineering, 124, 180-194. doi:10.1016/j.cie.2018.07.025Esteso, A., Alemany, M. M. E., & Ortiz, A. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research, 56(13), 4418-4446. doi:10.1080/00207543.2018.1447706Grillo, H., Alemany, M. M. E., Ortiz, A., & Fuertes-Miquel, V. S. (2017). Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products. Applied Mathematical Modelling, 49, 255-278. doi:10.1016/j.apm.2017.04.037Grossmann, I. (2005). Enterprise-wide optimization: A new frontier in process systems engineering. AIChE Journal, 51(7), 1846-1857. doi:10.1002/aic.10617Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199-220. doi:10.1006/knac.1993.1008Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing? International Journal of Human-Computer Studies, 43(5-6), 907-928. doi:10.1006/ijhc.1995.1081Hernández, J. E., Mula, J., Ferriols, F. J., & Poler, R. (2008). A conceptual model for the production and transport planning process: An application to the automobile sector. Computers in Industry, 59(8), 842-852. doi:10.1016/j.compind.2008.06.004Laporti, V., Borges, M. R. S., & Braganholo, V. (2009). Athena: A collaborative approach to requirements elicitation. Computers in Industry, 60(6), 367-380. doi:10.1016/j.compind.2009.02.011Do Prado Leite, J. C. S., Hadad, G. D. S., Doorn, J. H., & Kaplan, G. N. (2000). A Scenario Construction Process. Requirements Engineering, 5(1), 38-61. doi:10.1007/pl00010342Lenat, D. B. (1995). CYC. Communications of the ACM, 38(11), 33-38. doi:10.1145/219717.219745Lesh, R. (1981). Applied mathematical problem solving. Educational Studies in Mathematics, 12(2), 235-264. doi:10.1007/bf00305624Lezoche, M., Yahia, E., Aubry, A., Panetto, H., & Zdravković, M. (2012). Conceptualising and structuring semantics in cooperative enterprise information systems models. Computers in Industry, 63(8), 775-787. doi:10.1016/j.compind.2012.08.006Liu, L., Wang, H., & Xing, S. (2019). Optimization of distribution planning for agricultural products in logistics based on degree of maturity. Computers and Electronics in Agriculture, 160, 1-7. doi:10.1016/j.compag.2019.02.030Miller, G. A. (1995). WordNet. Communications of the ACM, 38(11), 39-41. doi:10.1145/219717.219748Miller, W. A., Leung, L. C., Azhar, T. M., & Sargent, S. (1997). Fuzzy production planning model for fresh tomato packing. International Journal of Production Economics, 53(3), 227-238. doi:10.1016/s0925-5273(97)00110-2Moskaliuk, J., Kimmerle, J., & Cress, U. (2009). Wiki-supported learning and knowledge building: effects of incongruity between knowledge and information. Journal of Computer Assisted Learning, 25(6), 549-561. doi:10.1111/j.1365-2729.2009.00331.xMula, J., Poler, R., García-Sabater, J. P., & Lario, F. C. (2006). Models for production planning under uncertainty: A review. International Journal of Production Economics, 103(1), 271-285. doi:10.1016/j.ijpe.2005.09.001Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204(3), 377-390. doi:10.1016/j.ejor.2009.09.008MUNDI, I., ALEMANY, M. M. E., BOZA, A., & POLER, R. (2013). A Model-Driven Decision Support System for the Master Planning of Ceramic Supply Chains with Non-uniformity of Finished Goods. Studies in Informatics and Control, 22(2). doi:10.24846/v22i2y201305Munir, K., & Sheraz Anjum, M. (2018). The use of ontologies for effective knowledge modelling and information retrieval. Applied Computing and Informatics, 14(2), 116-126. doi:10.1016/j.aci.2017.07.003Perales, D. D. P., Esteban, F.-C. L., Díaz, M. M. E. A., & Hernández, J. E. (2012). Framework for Modelling the Decision. International Journal of Decision Support System Technology, 4(2), 59-77. doi:10.4018/jdsst.2012040104Raghunathan, S. (1996). A structured modeling based methodology to design decision support systems. Decision Support Systems, 17(4), 299-312. doi:10.1016/0167-9236(96)00006-1Schneeweiss, C. (2003). Distributed decision making in supply chain management. International Journal of Production Economics, 84(1), 71-83. doi:10.1016/s0925-5273(02)00381-xSchneeweiss, C. (2003). Distributed decision making––a unified approach. European Journal of Operational Research, 150(2), 237-252. doi:10.1016/s0377-2217(02)00501-5Schön, E.-M., Thomaschewski, J., & Escalona, M. J. (2017). Agile Requirements Engineering: A systematic literature review. Computer Standards & Interfaces, 49, 79-91. doi:10.1016/j.csi.2016.08.011Shapiro, J. F. (1993). Chapter 8 Mathematical programming models and methods for production planning and scheduling. Handbooks in Operations Research and Management Science, 371-443. doi:10.1016/s0927-0507(05)80188-4Udias, A., Pastori, M., Dondeynaz, C., Carmona Moreno, C., Ali, A., Cattaneo, L., & Cano, J. (2018). A decision support tool to enhance agricultural growth in the Mékrou river basin (West Africa). Computers and Electronics in Agriculture, 154, 467-481. doi:10.1016/j.compag.2018.09.03