Improving Vegetables' Quality in Small-Scale Farms Through Stakeholders' Collaboration

[EN] Small farms are responsible for 80% of theworld¿s agricultural production although they have difficulties to meet the market quality requirements. Corporate social responsibility (CSR) programs where modern retailers invest in empowering small farmers have been implemented obtaining an increase of the supply chain (SC) profits in cases where supply and demand are balanced. In this paper, a MILP model based onWahyudin et al. (In: Proceedings of the international multiconference of engineers and computer scientists, Hong Kong, pp. 877¿882, [1]) to select the investments to carry out by modern retailers, and the product flow through the SC in situations of supply and demand imbalance is proposed. Its objective is to find out if collaboration programs have a positive impact on SC profits when supply and demand are not balanced. This model allows for the rejection of demand and product wastes. Results show that collaboration programs positively impact on the SC profits and consumer satisfaction level when there is an imbalance between demand and supply.The first author acknowledges the partial support of the Programme of Formation of University Professors of the Spanish Ministry of Education, Culture, and Sport (FPU15/03595), and the partial support of Project Development of an integrated maturity model for agility, resilience and gender perspective in supply chains (MoMARGE). Application to the agricultural sector. Ref. GV/2017/025, funded by the Generalitat Valenciana. The other authors acknowledge the partial support of Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MCSA-RISE-2015.Esteso, A.; Alemany Díaz, MDM.; Ortiz Bas, Á. (2020). Improving Vegetables' Quality in Small-Scale Farms Through Stakeholders' Collaboration. Lecture Notes in Management and Industrial Engineering. 95-103. https://doi.org/10.1007/978-3-030-44530-0_12S95103Wahyudin RS, Hisjam M, Yuniaristanto, Kurniawan B (2015) An agri-food supply chain model for cultivating the capabilities of farmers in accessing capital using corporate social responsibility program. In: Proceedings of the international multiconference of engineers and computer scientists, Hong Kong, pp 877–882Lowder SK, Skoet J, Raney T (2016) The number, size, and distribution of farms, smallholder farms, and family farms worldwide. World Dev 87:16–29Sutopo W, Hisjam M, Yuniaristanto (2011) An agri-food supply chain model for cultivating the capabilities of farmers accessing market using social responsibility program. Int Sch Sci Res Innov 5(11):1588–1592Sutopo W, Hisjam M, Yuniaristanto (2012) An agri-food supply chain model to enhance the business skills of small-scale farmers using corporate social responsibility. Makara J Technol 16(1):43–50Sutopo W et al (2013a) A goal programming approach for assessing the financial risk of corporate social responsibility programs in agri-food supply chain network. Proc World Congr Eng 2013:732–736Sutopo W, Hisjam M, Yuniaristanto (2013b) An agri-food supply chain model to empower farmers for supplying deteriorated product to modern retailer. In: IAENG transactions on engineering technologies: special issue of the international multiconference of engineers and computer scientists 2012. Springer Netherlands, Dordrecht, 189–202Grillo H, Alemany MME, Ortiz A, Fuertes-Miquel VS (2017) Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products. Appl Math Model 49:255–278Sutopo W, Hisjam M, Yuniaristanto (2013c) Developing an agri-food supply chain application for determining the priority of CSR program to empower farmers as a qualified supplier of modern retailer. In: 2013 World Congress on Engineering and Computer Science, pp 1180–1184Esteso A, Alemany MME, Ortiz A (2017) Conceptual framework for managing uncertainty in a collaborative agri-food supply chain context. Working conference on virtual enterprises. Springer, Cham, pp 715–72

Métodos y Modelos Deterministas e Inciertos para la Gestión de Cadenas de Suministro Agroalimentarias

[EN] The market for agricultural products has grown substantially. At the same time, social concern in food issues such as food safety, food quality, traceability and sustainability is constantly increasing. These reasons have pointed out the need of new models and tools to manage the agri-food supply chains while considering the characteristics that differentiate them from other industrial supply chains as well as the uncertainties present in the sector. Thus, the aim of this paper is to present the current status of a project which mains objectives are to describe the complexity faced by agri-food supply chain decision makers, and to develop new tools based on mathematical programming models to help the decision making process in agri-food supply chain planning. These models novelty will include the consideration of the inherent characteristics of agri-food supply chains and the sources of uncertainty present in the sector. The proposed models and tools will be applied to a real agri-food supply chain in order to prove their validity and applicability and to compare the results obtained by deterministic and uncertain tools.[ES] El mercado de productos agrícolas está en continuo crecimiento, al igual que la preocupación social en temas alimentarios como la calidad y seguridad alimentaria. Esto genera la necesidad de desarrollar modelos y herramientas para gestionar las cadenas de suministro agroalimentarias de manera ajustada y teniendo en cuenta sus características y fuentes de incertidumbre inherentes. Este articulo presenta el estado actual de un proyecto cuyos principales objetivos son: describir la complejidad enfrentada por los decisores de las cadenas de suministro agroalimentarias, y desarrollar nuevas herramientas basadas en programación matemática para apoyar la toma de decisiones en este sector.This research has been supported by the Program of Formation of University Professors (FPU) of the Spanish Ministry of Education, Culture and Sport (FPU15/03595)Esteso, A.; Alemany Díaz, MDM.; Ortiz Bas, Á. (2017). Deterministic and Uncertain Methods and Models for Managing Agri-Food Supply Chain. Dirección y organización (Online). (62):41-46. http://hdl.handle.net/10251/108673S41466

Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models

This is an Author's Accepted Manuscript of an article published in [include the complete citation information for the final version of the article as published in the International Journal of Production Research (2018) © Taylor & Francis, available online at: http://doi.org/10.1080/00207543.2018.1447706[EN] Agri-food sector performance strongly impacts global economy, which means that developing optimisation models to support the decision-making process in agri-food supply chains (AFSC) is necessary. These models should contemplate AFSC¿s inherent characteristics and sources of uncertainty to provide applicable and accurate solutions. To the best of our knowledge, there are no conceptual frameworks available to design AFSC through mathematical programming modelling while considering their inherent characteristics and sources of uncertainty, nor any there literature reviews that address such characteristics and uncertainty sources in existing AFSC design models. This paper aims to fill these gaps in the literature by proposing such a conceptual framework and state of the art. The framework can be used as a guide tool for both developing and analysing models based on mathematical programming to design AFSC. The implementation of the framework into the state of the art validates its. Finally, some literature gaps and future research lines were identified.This first author was partially supported by the Programme of Formation of University Professors of the Spanish Ministry of Education, Culture, and Sport [grant number FPU15/03595]; the partial support of Project 'Development of an integrated maturity model for agility, resilience and gender perspective in supply chains (MoMARGE). Application to the agricultural sector.' Ref. GV/2017/025, funded by the Generalitat Valenciana. The other authors acknowledge the partial support of Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MSCA-RISE-2015.Esteso, A.; Alemany Díaz, MDM.; Ortiz Bas, Á. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research. 56(13):4418-4446. https://doi.org/10.1080/00207543.2018.1447706S44184446561

A mathematical programming tool for an efficient decision-making on teaching assignment under non-regular time schedules

[EN] In this paper, an optimization tool based on a MILP model to support the teaching assignment process is proposed. It considers not only hierarchical issues among lecturers but also their preferences to teach a particular subject, the non-regular time schedules throughout the academic year, different type of credits, number of groups and other specific characteristics. Besides, it adds restrictions based on the time compatibility among the different subjects, the lecturers' availability, the maximum number of subjects per lecturer, the maximum number of lecturers per subject as well as the maximum and minimum saturation level for each lecturer, all of them in order to increase the teaching quality. Schedules heterogeneity and other features regarding the operation of some universities justify the usefulness of this model since no study that deals with all of them has been found in the literature review. Model validation has been performed with two real data sets collected from one academic year schedule at the Spanish University Universitat Politecnica de Valencia.Solano Cutillas, P.; Pérez Perales, D.; Alemany Díaz, MDM. (2022). A mathematical programming tool for an efficient decision-making on teaching assignment under non-regular time schedules. Operational Research. 22(3):2899-2942. https://doi.org/10.1007/s12351-021-00638-12899294222

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

[EN] Order promising in manufacturing systems that produce non-uniform units of the same finished good becomes a more complex process when customer orders need to be served with homogeneous units. To facilitate this task, we propose a mathematical model-based decision tool to support the order promising process according to product homogeneity requirements in hybrid Make-To-Stock (MTS) and Make-To-Order (MTO) contexts. In these manufacturing environments, the comparison of Available-To-Promise (ATP) and/or Capable-To-Promise (CTP) quantities with homogeneous ones ordered by customers is necessary during the order commitment. To properly deal with customers' product uniformity requirements, different ATP consumption rules are implemented by defining a novel objective function. CTP modelling in these systems also entails having to address new aspects, such as estimating future homogeneous quantities in additional lots to the master plan, accomplishing minimum lot sizes and saving in setups when programming new lots. By including CTP in the order promising model, a closer integration with the master production schedule is achieved. The resulting mathematical model was applied to a ceramic tile company in different supply scenarios and execution modes, and at several availability levels (ATP and ATP&CTP). The results validate model performance and provide insights into the impact of ATP consumption rules on the profits made from committed customer orders in different scenarios for the specific ceramic tile company.This work was supported by the Spanish Ministry of Economy and Competitiveness with Grant DPI2011-23597 and the Universitat Polito cnica de Valencia with Grant Ref. PAID-06-11/1840.Alemany Díaz, MDM.; Ortiz Bas, Á.; Fuertes-Miquel, VS. (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. https://doi.org/10.1016/j.cie.2018.05.040S21923412

Increasing the Sustainability of a Fresh Vegetables Supply Chain Through the Optimization of Funding Programs: A Multi-Objective Mathematical Programming Approach

[EN] Purpose: This research develops a model to improve the quality and freshness of sold vegetables through a funding program between farmers and retailers. Through this program, retailers who are interested in the distribution of first quality vegetables provide funds to farmers to increase their production this type of vegetables through the acquisition of new machinery, technology, or training. Design/methodology/approach: The problem is solved through a multi-objective mathematical programming model that simultaneously optimizes the supply chain profits, the waste of vegetables, the economic unfairness among farmers, the unfairness in the distribution of funds, and the freshness of sold vegetables. The ¿-constraint method is used to obtain several non-dominated solutions to the problem after linearizing the non-lineal equations related to the unfairness objectives. Findings: Results show that it is possible to improve the indicators related to the vegetable waste, the economic unfairness, the unfairness in the distribution of funds and the freshness of vegetables while maintaining similar to optimal profits for the supply chain. Interesting trade-offs between the five objectives are identified, which can be used by supply chain members to select the most appropriate solution to be implemented in the real supply chain. Originality/value: This research models aspects relevant to the agri-food sector that have not been previously modelled for the problem under study. The main novelties of this paper are the consideration of the limited shelf life of the vegetables as well as the requirement of ensuring a minimum freshness at the moment of their sale, the price dependence on the quality and freshness of vegetables, the optimization of vegetable waste and the freshness of vegetables sold, as well as the joint optimization of the five previously defined objectives.The authors acknowledge the support of the Project 691249, "RUCAPS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems", funded by the EU under its funding scheme H2020-MCSA-RISE-2015.Esteso, A.; Alemany Díaz, MDM.; Ortiz Bas, Á.; Panetto, H. (2022). Increasing the Sustainability of a Fresh Vegetables Supply Chain Through the Optimization of Funding Programs: A Multi-Objective Mathematical Programming Approach. Journal of Industrial Engineering and Management. 15(2):256-274. https://doi.org/10.3926/jiem.371925627415

Collaborative Plan to Reduce Inequalities Among the Farms through Optimization

[EN] The crop planning problem consists in defining the crop and acreage to be planted at each farm. There are several centralized mathematical programming models to support crop planning in literature. However, centralized solutions often produce economic unfairness among the members of the supply chain, being especially relevant among the farmers in the agri-food sector. To solve it, this paper tries to answer the following research question: is it possible to reduce inequalities among the farmers through a collaborative plan? A centralized multi-objective mathematical programming model to support crop planning and the next decisions up to the sale of vegetables through a collaborative plan is proposed to answer this question. To show the validity of the proposed collaborative plan, results obtained are compared against those obtained without collaboration. The analysis of results shows that inequalities among the supply chain members can be highly reduced in a centralized decision-making approach by implementing the proposed collaborative plan, reducing a bit the supply chain profit.We acknowledge the support of the project 691249, RUCAPS: "Enhancing and implementing knowledge based ICT solutions within high risk and uncertain conditions for agriculture production systems", funded by the European Union's research and innovation programme under the H2020 Marie Sklodowska-Curie Actions.Esteso, A.; Alemany Díaz, MDM.; Ortiz Bas, Á.; Iannacone, R. (2021). Collaborative Plan to Reduce Inequalities Among the Farms through Optimization. IFIP Advances in Information and Communication Technology. 629:125-137. https://doi.org/10.1007/978-3-030-85969-5_11S12513762

Event Management for Sensing Enterprises with Decision Support Systems

The final publication is available at Springer via http://dx.doi.org/10.1007/s40745-015-0034-z[EN] Sensing enterprises make use of new technologies to capture real-time information and fed constantly the decision making process. Decision support systems (DSS) are exposed to these real-time events and it is possible to start the decision process from scratch in case any unexpected internal and external events take place. Thus, an event monitoring and management system should interact with the DSS to manage events that might affect their decisions. It should act as a supra-system to identify when decisions made are still valid or need to be reanalysed. The traditional configuration of DSS (where they collect internal and external information of the organization and the decision-maker is involved in the decision-making process) should be extended to treat event management using a monitoring and management system, which monitors internal and external information and facilitate the introduction of no monitored events. This monitor and manager systems become more and more necessary due to the incessant incorporation of new technologies that enables the companies to be more context-sensitive. Furthermore, this new and/or more accurate information, which is obtained for the organization, requires a proper management.This research has been carried out in the framework of the project PAID-06-21Universitat Politècnica de València (Sistema de ayuda a la toma de decisiones ante decisiones no programadas en la planificación jerárquica de la producción) and GV/2014/010 Generalitat Valenciana (Identificación de la información proporcionada por los nuevos sistemas de detección accesibles mediante internet en el ámbito de las “sensing enterprises” para la mejora de la toma de decisiones en la planificación de la producción).Boza, A.; Alemany Díaz, MDM.; Cuenca, L.; Ortiz Bas, Á. (2015). Event Management for Sensing Enterprises with Decision Support Systems. Annals of Data Science. 2(1):103-109. https://doi.org/10.1007/s40745-015-0034-zS10310921Estupinyà P (2010) El ladrón de cerebros: Compartiendo el conocimiento científico de las mentes más brillantes. Penguin Random House Grupo Editorial EspañaVicens E, Alemany ME, Andrés C, Guarch JJ (2001) A design and application methodology for hierarchical production planning decision support systems in an enterprise integration context. Int J Prod Econ 74:5–20. doi: 10.1016/S0925-5273(01)00103-7Van Wezel W, Van Donk DP, Gaalman G (2006) The planning flexibility bottleneck in food processing industries. J Oper Manag 24:287–300. doi: 10.1016/j.jom.2004.11.001Winter R (1994) Multi-stage production controlling based on continuous, flexible abstraction hierarchies. IEPMÖzdamar L, Bozyel MA, Birbil SI (1998) A hierarchical decision support system for production planning (with case study). Eur J Oper Res 104:403–422. doi: 10.1016/S0377-2217(97)00016-7FInES FIESC (2012) FInES Research Roadmap 2025Shamsuzzoha Ah, Rintala S, Cunha PF, Ferreira PS, Kankaanpää T, Maia Carneiro L (2013) Event monitoring and management process in a non-hierarchical business network. In: Poler R, Carneiro L, Jasinski T, Zolghadri rc, Pedrazzoli P (eds) Intelligent non-hierarchical manufacturing networks. Wiley, New York, pp 349–374Committee of Sponsoring Organizations of the Treadway Commission (2004) COSO enterprise risk management-integrated framework: application techniques. Committee of Sponsoring Organizations of the Treadway CommissionSantucci G, Martinez C, Vlad-Câlcic D (2012) The sensing enterprise. FInES workshop at FIA 2012Vargas A, Cuenca L, Boza A, Sacala I, Moisescu M (2014) Towards the development of the framework for inter sensing enterprise architecture, J Intell Manuf, 1–18Anthony RN (1965) Planning and control systems: a framework for analysis. Harvard University, CambridgeSimon HA (1960) The new science of management decision. Harper & Brothers, New YorkShim JP, Warkentin M, Courtney JF, Power DJ, Sharda R, Carlsson C (2002) Past, present, and future of decision support technology. Decis Support Syst 33:111–126. doi: 10.1016/S0167-9236(01)00139-7Peng Y, Kou G, Shi Y, Chen ZA (2008) Descriptive framework for the field of data mining and knowledge discovery. Int J Inf Technol Decis Mak 7:639–682. doi: 10.1142/S0219622008003204Alarcón F, Alemany MME, Lario FC, Oltra RF (2011) La falta de homogeneidad del producto (FHP) en las empresas cerámicas y su impacto en la reasignación del inventario. Boletín de la Sociedad Española de Cerámica y Vidrio 50:49–58. doi: 10.3989/cyv.072011Cegarra J, van Wezel W (2011) A comparison of task analysis methods for planning and scheduling. In: Fransoo JC, Waefler T, Wilson JR (eds) Behavioral operations in planning and scheduling. Springer, Berlin Heidelberg, pp 323–338FP7-ICT (2012) ICT: Information and Communication Technologies: work programme 2013Barash G, Bartolini C, Wu L (2007) Measuring and improving the performance of an IT support organization in managing service incidents. In: 2nd IEEE/IFIP international workshop on business-driven IT management, BDIM ’07, pp 11–18Bartolini C, Stefanelli C, Tortonesi M (2010) SYMIAN: analysis and performance improvement of the IT incident management process. IEEE Trans Netw Serv Manag 7:132–144. doi: 10.1109/TNSM.2010.1009.I9P0321Boza A, Alemany MME, Alarcón F, Cuenca L (2013) A model-driven DSS architecture for delivery management in collaborative supply chains with lack of homogeneity in products. Prod Plan Control 25:650–661. doi: 10.1080/09537287.2013.798085Boza A, Ortiz A, Vicens E, Poler R (2009) A framework for a decision support system in a hierarchical extended enterprise decision context. In: Poler R, van Sinderen M, Sanchis R (eds) Enterprise interoperability. Springer, Berlin Heidelberg, pp 113–124Grefen P, Dijkman R (2013) Hybrid control of supply chains: a structured exploration from a systems perspective. Int J Prod Manag Eng 1:39–5

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