- -

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

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

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

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Garrdo;Antonell;Martn ...
Tamaño: 1.300Mb
Formato: PDF
Descripción: Versión del Autor.
Abrir
[Cerrado]
Nombre: Garrido et al ...
Tamaño: 1.537Mb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Garrido, Alejandra es_ES
dc.contributor.author Antonelli, Leandro es_ES
dc.contributor.author Martin, Jonathan es_ES
dc.contributor.author Alemany Díaz, María Del Mar es_ES
dc.contributor.author Mula, Josefa es_ES
dc.date.accessioned 2021-04-23T03:32:01Z
dc.date.available 2021-04-23T03:32:01Z
dc.date.issued 2020-03 es_ES
dc.identifier.issn 0168-1699 es_ES
dc.identifier.uri http://hdl.handle.net/10251/165525
dc.description.abstract [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. es_ES
dc.description.sponsorship 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. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Computers and Electronics in Agriculture es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Language extended lexicon (LEL) es_ES
dc.subject Scenarios es_ES
dc.subject Software engineering es_ES
dc.subject Mathematical programming es_ES
dc.subject Fresh tomato packing es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.compag.2020.105242 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/691249/EU/Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ANPCyT//PICT-2015-3000/AR/Evaluación y reparación comunitaria de problemas de usabilidad y accesibilidad en aplicaciones web desktop y móviles/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation 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.2020.105242 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.compag.2020.105242 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 14 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 170 es_ES
dc.relation.pasarela S\405759 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Agencia Nacional de Promoción Científica y Tecnológica, Argentina es_ES
dc.description.references Alemany, 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.2018.05.040 es_ES
dc.description.references Alemany, 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.2011.02.002 es_ES
dc.description.references Alemany, 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.2012.07.022 es_ES
dc.description.references Alexander, 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:20040505 es_ES
dc.description.references Armengol, 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_21 es_ES
dc.description.references Baraniuk, 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.2004.1328080 es_ES
dc.description.references Cid-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.2019.104925 es_ES
dc.description.references Dominguez-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.2601361 es_ES
dc.description.references Esteso, 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.2018.07.025 es_ES
dc.description.references Esteso, 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:10.1080/00207543.2018.1447706 es_ES
dc.description.references Grillo, 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.2017.04.037 es_ES
dc.description.references Grossmann, I. (2005). Enterprise-wide optimization: A new frontier in process systems engineering. AIChE Journal, 51(7), 1846-1857. doi:10.1002/aic.10617 es_ES
dc.description.references Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199-220. doi:10.1006/knac.1993.1008 es_ES
dc.description.references Gruber, 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.1995.1081 es_ES
dc.description.references Hernández, 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.2008.06.004 es_ES
dc.description.references Laporti, 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.2009.02.011 es_ES
dc.description.references Do 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/pl00010342 es_ES
dc.description.references Lenat, D. B. (1995). CYC. Communications of the ACM, 38(11), 33-38. doi:10.1145/219717.219745 es_ES
dc.description.references Lesh, R. (1981). Applied mathematical problem solving. Educational Studies in Mathematics, 12(2), 235-264. doi:10.1007/bf00305624 es_ES
dc.description.references Lezoche, 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.2012.08.006 es_ES
dc.description.references Liu, 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.2019.02.030 es_ES
dc.description.references Miller, G. A. (1995). WordNet. Communications of the ACM, 38(11), 39-41. doi:10.1145/219717.219748 es_ES
dc.description.references Miller, 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)00110-2 es_ES
dc.description.references Moskaliuk, 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.x es_ES
dc.description.references Mula, 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.2005.09.001 es_ES
dc.description.references Mula, 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.2009.09.008 es_ES
dc.description.references MUNDI, 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/v22i2y201305 es_ES
dc.description.references Munir, 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.2017.07.003 es_ES
dc.description.references Perales, 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.2012040104 es_ES
dc.description.references Raghunathan, 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)00006-1 es_ES
dc.description.references Schneeweiss, 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-x es_ES
dc.description.references Schneeweiss, C. (2003). Distributed decision making––a unified approach. European Journal of Operational Research, 150(2), 237-252. doi:10.1016/s0377-2217(02)00501-5 es_ES
dc.description.references Schön, 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.2016.08.011 es_ES
dc.description.references Shapiro, 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)80188-4 es_ES
dc.description.references Udias, 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.2018.09.037 es_ES
dc.subject.ods 02.- Poner fin al hambre, conseguir la seguridad alimentaria y una mejor nutrición, y promover la agricultura sostenible es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem