Propuesta de Marco y Metodología para el Modelado del Proceso de Planificación Colaborativa en Redes de Suministro/Distribución basado en Programación Matemática. Aplicación a Empresas del Sector de Pavimientos y Revestimientos Cerámicos

En la actualidad, cada vez más son necesarias Herramientas de ayuda a la Toma de Decisiones para la Planificación de Operaciones Colaborativa en contextos de Cadenas de Suministro, o más ampliamente, lo que se denomina Redes de Suministro/Distribución (RdS/D). Entre dichas Herramientas son de especial relevancia las de optimización, y entre estas últimas, aquellas basadas en Modelos de Programación Matemática. Una extensa revisión de la literatura depara que dichos Modelos se han utilizado mayoritariamente considerando una Toma de Decisiones centralizada de la RdS/D. Sin embargo, la realidad muestra como las diferentes ¿Entidades¿ que forman parte de la RdS/D, no siempre comparten los mismos objetivos y en muchas ocasiones, son reáceas a compartir cierto tipo de información. Por esa razón, la situación más común es la toma de Decisiones descentralizada, en la que diferentes ¿Entidades¿ deben coordinarse para obtener un buen rendimiento individual y de la RdS/D en su conjunto. Por otra parte, los Modelos que sí se han aplicado al caso descentralizado han simplificado enormemente la realidad, sin considerar RdS/D con el suficiente grado de complejidad (productos, recursos¿) u omitiendo algunos aspectos importantes del propio proceso de Planificación Colaborativa, y particularmente la aplicación simultánea de aspectos ligados a la integración espacial (entre diferentes ¿decisores¿ perteneciente a un mismo Nivel Decisional) o la integración vertical (entre diferentes Niveles Decisionales). Además, en muchas de las ocasiones dichos Modelos no son fácilmente extrapolables a otras situaciones y se obvia la manera en que se han elaborado. Por todo ello, la presente Tesis propone un Marco, y posteriormente una Metodología basada en el mismo, que indique de forma estructurada, en primer lugar, los pasos para el Modelado del proceso de Planificación de Operaciones (Colaborativo) en contextos de RdS/D, y en segundo lugar, para el Modelado Analítico (basado en Programación Matemática) del mismo y su posterior Resolución/Evaluación. Más concretamente, el Marco propuesto integra cuatro visiones diferentes de modelado, como son las visiones Física, Organizacional, Decisional e Informacional y sus inter-relaciones, lo cual favorece la construcción de modelos integrados (unión de varias visiones) del proceso de Planificación Colaborativa. Si bien el Modelado de Procesos atiende fundamentalmente a la Visión Funcional, en este caso, por el tipo específico de Proceso que se desea modelar, ligado a la Toma de Decisiones, se utilizará la Visión Decisional como la visión base para modelar el Proceso, estando la Visión Funcional embebida en esta última. La inclusión del resto de Visiones es importante puesto que en la Toma de Decisiones en contextos de Planificación se actúa sobre unos Recursos/Ítems (Visión Física) y según una determinada Organización, en la que las diferentes ¿entidades¿ estarán más o menos integradas (Visión Organizacional). Por otra parte la propia actividad de la RdS/D generará y necesitará cierta Información (Visión Informacional), necesaria para tomar decisiones. Además dicho Marco contempla todo tipo de escenarios decisionales en los que se puede enmarcarse el Proceso de Planificación Colaborativa en una RdS/D, desde los más Centralizados a aquellos que tienen lugar en Entornos Distribuidos/Descentralizados, para lo cual se identifican diferentes Centros de Decisión, tanto en el Nivel Decisional Táctico como en el Operativo, considerando al mismo tiempo tanto su Integración Temporal como su Integración Espacial. Una vez aplicada la primera parte de la Metodología se obtendrá (nivel Macro) un Modelo integrado del Proceso, en el que se conocerá, entre otros aspectos, de qué Actividades Decisionales consta el Proceso, cuál es su orden de ejecución y qué tipo de Información ¿por interdependencias¿ es intercambiada entre las mismas. El modelo del Proceso anterior y todos los aspectos/conceptos analíticos descritos también en el Marco, serán especialmente relevantes para que en una segunda parte de la Metodología (nivel micro) se detalle como realizar el Modelado analítico del Proceso y cómo proceder a su Resolución/Evaluación integrada. Para el Modelado analítico se supondrá que cada uno de los Centros de Decisión (asociados a las Actividades Decisionales del Proceso), tomará las decisiones de planificación táctica/operativa en base a Modelos basados en Programación Matemática (Programación Lineal Entera Mixta). Además se particularizará para escenarios doblemente jerárquicos (desde el punto de vista Temporal y Espacial), de un ciclo Instrucción-Reacción y en contextos organizacionales (de ¿búsqueda de un objetivo conjunto¿) en los que puede existir cualquier ¿status de información¿ (asimetría), pero en la que ésta nunca se podrá utilizar con fines oportunistas. En cuanto a la resolución/evaluación, se irán ejecutando los diferentes Modelos teniendo en cuenta la secuencia e información ¿por interdependencias¿ (propia de escenarios colaborativos) definidas anteriormente en el Modelado del Proceso. Una vez ejecutados todos, se describe cómo evaluar cuantitativamente el ¿desempeño¿ conjunto de la RdS/D (o grado de Planificación Colaborativa actual) a partir de la definición de tres parámetros, denominados ¿Criterio Total¿, ¿Tiempo de Resolución Total¿ y ¿Consistencia Total¿. Además, la propia metodología facilita y guía en la ¿simulación¿ de diferentes escenarios de Planificación Colaborativa (TO-BE) de manera que puedan conocerse ¿a priori¿ los beneficios/costes que ello supone. El análisis de dichos escenarios podrá afectar indistintamente (con mayor o menor profundidad) a cualquiera de las Visiones Física, Organizacional o Decisional, y por ende a la Informacional. Por último, dicha Metodología, aunque aplicable/extrapolable a cualquier Sector Industrial, se ha implementado en una RdS/D concreta perteneciente al Sector de Pavimentos y Revestimientos Cerámicos. En primer lugar a través del Modelado de su Proceso de Planificación Colaborativa y la identificación de las diferentes Actividades Decisionales (Centros de Decisión) que lo conforman, así como su orden de ejecución e información compartida entre las mismas. En segundo lugar a través del Modelado basado en Programación Matemática de cada uno de los anteriores Centros de Decisión, en la que cabe resaltar, también como aportación, la complejidad de los diferente Modelos, interrelacionados entre sí, y que incluyen todas las características intrínsecas a la Planificación táctico/operativa en dicho Sector. En tercer y último lugar a través de la resolución integrada de los anteriores Modelos, lo que ha permitido evaluar cuantitativamente, a través de los parámetros antes mencionados, el grado de Planificación colaborativa actual (AS-IS) de dicha RdS/D.Pérez Perales, D. (2013). Propuesta de Marco y Metodología para el Modelado del Proceso de Planificación Colaborativa en Redes de Suministro/Distribución basado en Programación Matemática. Aplicación a Empresas del Sector de Pavimientos y Revestimientos Cerámicos [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/32665TESI

Using the Internet of Things in a Production Planning context

[EN] One of the most novel concepts that have been applied to companies in recent years is ¿Sensing Enterprises¿. This concept implies a drastic change in the way companies operate. Within the framework of this concept, another necessary and complementary concept arises, the so-called ¿Internet of Things¿ concept. It seems evident that the Internet of Things can generally help to improve the functioning of the processes undertaken in companies, particularly one of the key processes; the production planning process. Despite being able to find abundant information on both themes, and the apparent relevance that using the Internet of Things could have for the production planning process, no works that have jointly studied these matters were found. To bridge this gap, the present work intends to reflect on how the characteristics and advantages of the Internet of Things can be put to good use in the production planning process.Alarcón Valero, F.; Pérez Perales, D.; Boza, A. (2016). Using the Internet of Things in a Production Planning context. Brazilian Journal of Operations & Production Management. 13(1):72-76. doi:10.14488/BJOPM.2016.v13.n1.a8S727613

Project portfolio selection for increasing sustainability in supply chains

[EN] Sustainability practices impact on the competitiveness of organizations. Enterprises need approaches that both support the implementation of these practices by helping to define the strategic elements of sustainable supply chains and prioritize projects to increase profitability. The purpose of this paper is to propose an approach using the Analytic Hierarchy Process that supports the portfolio project decision by aligning the project selection process to the strategic objectives of a supply chain that pursue sustainability. This approach will benefit enterprises to prioritize projects that have the highest impact on the sustainability strategy of the supply chain over time. The approach has been applied to an Agri-food supply chain.Authors of this publication acknowledge the contribution of the Project GV/2017/065 "Development of a decision support tool for the management and improvement of sustainability in supply chains" funded by the Regional Government of Valencia.Verdecho Sáez, MJ.; Pérez Perales, D.; Alarcón Valero, F. (2020). Project portfolio selection for increasing sustainability in supply chains. Economics and business letters. 9(4):317-325. https://doi.org/10.17811/ebl.9.4.2020.317-325S3173259

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

Sustainability vs. Circular Economy from a Disposition Decision Perspective: A Proposal of a Methodology and an Applied Example in SMEs

[EN] Disposition Decision (DD) consists of deciding how to treat a recovered product, and it is one of the most important decisions in reverse logistics. Any of the selected disposition alternatives will have a significant impact on the enterprise sustainability. However, the most sustainable alternative may not be an alternative to make circular economy (CE) possible. In these cases, if the company wishes to adopt a CE strategy, it will have to switch from the most sustainable alternative to a less sustainable one that CE allows. Then, how much should be sacrificed for each sustainability dimension to make CE possible? This paper proposes a methodology for quantitatively comparing the most sustainable disposition alternative and the most sustainable CE alternative. This comparison allows small and medium enterprises (SMEs) to know how exactly all dimensions increase or decrease when selecting the most sustainable CE disposition alternative and to, therefore, assess the interest of adopting a CE policy. The proposed methodology is applied to a used tire recovery company. The results of this example show that the CE alternative offers a better environmental result but presents worst economic and social results. This example can be used as a guide for future applications other SMEs.The authors would like to acknowledge the predisposition of the recovery and treatment company of used tires by facilitating all necessary data to be used in the example application. The support of the Project GV/2017/065 "Development of a decision support tool for the management and improvement of sustainability in supply chains" funded by the Regional Government of Valencia is gratefully acknowledged. The authors also thank the anonymous reviewers and assistant editor who reviewed earlier versions of this paper.Alarcón Valero, F.; Cortés-Pellicer, P.; Pérez Perales, D.; Sanchis, R. (2020). Sustainability vs. Circular Economy from a Disposition Decision Perspective: A Proposal of a Methodology and an Applied Example in SMEs. Sustainability. 12(23):1-26. https://doi.org/10.3390/su122310109S1261223Pieroni, M. P. P., McAloone, T. C., & Pigosso, D. C. A. (2019). Business model innovation for circular economy and sustainability: A review of approaches. Journal of Cleaner Production, 215, 198-216. doi:10.1016/j.jclepro.2019.01.036Geissdoerfer, M., Savaget, P., Bocken, N. M. P., & Hultink, E. J. (2017). The Circular Economy – A new sustainability paradigm? Journal of Cleaner Production, 143, 757-768. doi:10.1016/j.jclepro.2016.12.048Geisendorf, S., & Pietrulla, F. (2017). The circular economy and circular economic concepts-a literature analysis and redefinition. Thunderbird International Business Review, 60(5), 771-782. doi:10.1002/tie.21924Millar, N., McLaughlin, E., & Börger, T. (2019). The Circular Economy: Swings and Roundabouts? Ecological Economics, 158, 11-19. doi:10.1016/j.ecolecon.2018.12.012Prieto-Sandoval, V., Jaca, C., & Ormazabal, M. (2018). Towards a consensus on the circular economy. Journal of Cleaner Production, 179, 605-615. doi:10.1016/j.jclepro.2017.12.224Kalmykova, Y., Sadagopan, M., & Rosado, L. (2018). Circular economy – From review of theories and practices to development of implementation tools. Resources, Conservation and Recycling, 135, 190-201. doi:10.1016/j.resconrec.2017.10.034Korhonen, J., Honkasalo, A., & Seppälä, J. (2018). Circular Economy: The Concept and its Limitations. Ecological Economics, 143, 37-46. doi:10.1016/j.ecolecon.2017.06.041Mota, B., Gomes, M. I., Carvalho, A., & Barbosa-Povoa, A. P. (2015). Towards supply chain sustainability: economic, environmental and social design and planning. Journal of Cleaner Production, 105, 14-27. doi:10.1016/j.jclepro.2014.07.052Guarnieri, P., Camara e Silva, L., & Vieira, B. (2020). How to Assess Reverse Logistics of e-Waste Considering a Multicriteria Perspective? A Model Proposition. Logistics, 4(4), 25. doi:10.3390/logistics4040025Agrawal, S., & Singh, R. K. (2019). Analyzing disposition decisions for sustainable reverse logistics: Triple Bottom Line approach. Resources, Conservation and Recycling, 150, 104448. doi:10.1016/j.resconrec.2019.104448Farahani, S., Otieno, W., & Barah, M. (2019). Environmentally friendly disposition decisions for end-of-life electrical and electronic products: The case of computer remanufacture. Journal of Cleaner Production, 224, 25-39. doi:10.1016/j.jclepro.2019.03.182Hazen, B. T., Hall, D. J., & Hanna, J. B. (2012). Reverse logistics disposition decision‐making. International Journal of Physical Distribution & Logistics Management, 42(3), 244-274. doi:10.1108/09600031211225954Agrawal, S., Singh, R. K., & Murtaza, Q. (2015). A literature review and perspectives in reverse logistics. Resources, Conservation and Recycling, 97, 76-92. doi:10.1016/j.resconrec.2015.02.009Kumar, S., & Malegeant, P. (2006). Strategic alliance in a closed-loop supply chain, a case of manufacturer and eco-non-profit organization. Technovation, 26(10), 1127-1135. doi:10.1016/j.technovation.2005.08.002Singh, R. K., & Agrawal, S. (2018). Analyzing disposition strategies in reverse supply chains: fuzzy TOPSIS approach. Management of Environmental Quality: An International Journal, 29(3), 427-443. doi:10.1108/meq-12-2017-0177Agrawal, S., Singh, R. K., & Murtaza, Q. (2016). Disposition decisions in reverse logistics: Graph theory and matrix approach. Journal of Cleaner Production, 137, 93-104. doi:10.1016/j.jclepro.2016.07.045Skinner, L. R., Bryant, P. T., & Glenn Richey, R. (2008). Examining the impact of reverse logistics disposition strategies. International Journal of Physical Distribution & Logistics Management, 38(7), 518-539. doi:10.1108/09600030810900932Prahinski, C., & Kocabasoglu, C. (2006). Empirical research opportunities in reverse supply chains. Omega, 34(6), 519-532. doi:10.1016/j.omega.2005.01.003Thierry, M., Salomon, M., Van Nunen, J., & Van Wassenhove, L. (1995). Strategic Issues in Product Recovery Management. California Management Review, 37(2), 114-136. doi:10.2307/41165792Krikke, H. (2011). Impact of closed-loop network configurations on carbon footprints: A case study in copiers. Resources, Conservation and Recycling, 55(12), 1196-1205. doi:10.1016/j.resconrec.2011.07.001Geissdoerfer, M., Morioka, S. N., de Carvalho, M. M., & Evans, S. (2018). Business models and supply chains for the circular economy. Journal of Cleaner Production, 190, 712-721. doi:10.1016/j.jclepro.2018.04.159Rizos, V., Behrens, A., van der Gaast, W., Hofman, E., Ioannou, A., Kafyeke, T., … Topi, C. (2016). Implementation of Circular Economy Business Models by Small and Medium-Sized Enterprises (SMEs): Barriers and Enablers. Sustainability, 8(11), 1212. doi:10.3390/su8111212Reike, D., Vermeulen, W. J. V., & Witjes, S. (2018). The circular economy: New or Refurbished as CE 3.0? — Exploring Controversies in the Conceptualization of the Circular Economy through a Focus on History and Resource Value Retention Options. Resources, Conservation and Recycling, 135, 246-264. doi:10.1016/j.resconrec.2017.08.027A. Narayana, S., A. Elias, A., & K. Pati, R. (2014). Reverse logistics in the pharmaceuticals industry: a systemic analysis. The International Journal of Logistics Management, 25(2), 379-398. doi:10.1108/ijlm-08-2012-0073Sarkis, J., Helms, M. M., & Hervani, A. A. (2010). Reverse logistics and social sustainability. Corporate Social Responsibility and Environmental Management, 17(6), 337-354. doi:10.1002/csr.220Fleischmann, M., Krikke, H. R., Dekker, R., & Flapper, S. D. P. (2000). A characterisation of logistics networks for product recovery. Omega, 28(6), 653-666. doi:10.1016/s0305-0483(00)00022-0Sangwan, K. S. (2017). Key Activities, Decision Variables and Performance Indicators of Reverse Logistics. Procedia CIRP, 61, 257-262. doi:10.1016/j.procir.2016.11.185Mangla, S. K., Kusi-Sarpong, S., Luthra, S., Bai, C., Jakhar, S. K., & Khan, S. A. (2020). Operational excellence for improving sustainable supply chain performance. Resources, Conservation and Recycling, 162, 105025. doi:10.1016/j.resconrec.2020.105025Hazen, B. T., Wu, Y., Cegielski, C. G., Jones-Farmer, L. A., & Hall, D. J. (2012). Consumer reactions to the adoption of green reverse logistics. The International Review of Retail, Distribution and Consumer Research, 22(4), 417-434. doi:10.1080/09593969.2012.690777Krumwiede, D. W., & Sheu, C. (2002). A model for reverse logistics entry by third-party providers. Omega, 30(5), 325-333. doi:10.1016/s0305-0483(02)00049-xSouza, G. C. (2012). Closed-Loop Supply Chains: A Critical Review, and Future Research*. Decision Sciences, 44(1), 7-38. doi:10.1111/j.1540-5915.2012.00394.xKannan, G., Pokharel, S., & Sasi Kumar, P. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, Conservation and Recycling, 54(1), 28-36. doi:10.1016/j.resconrec.2009.06.004Wadhwa, S., Madaan, J., & Chan, F. T. S. (2009). Flexible decision modeling of reverse logistics system: A value adding MCDM approach for alternative selection. Robotics and Computer-Integrated Manufacturing, 25(2), 460-469. doi:10.1016/j.rcim.2008.01.006Ziout, A., Azab, A., & Atwan, M. (2014). A holistic approach for decision on selection of end-of-life products recovery options. Journal of Cleaner Production, 65, 497-516. doi:10.1016/j.jclepro.2013.10.001Daugherty, P. J., Richey, R. G., Genchev, S. E., & Chen, H. (2005). Reverse logistics: superior performance through focused resource commitments to information technology. Transportation Research Part E: Logistics and Transportation Review, 41(2), 77-92. doi:10.1016/j.tre.2004.04.002Stock, J. R., & Mulki, J. P. (2009). PRODUCT RETURNS PROCESSING: AN EXAMINATION OF PRACTICES OF MANUFACTURERS, WHOLESALERS/DISTRIBUTORS, AND RETAILERS. Journal of Business Logistics, 30(1), 33-62. doi:10.1002/j.2158-1592.2009.tb00098.xTibben‐Lembke, R. S., & Rogers, D. S. (2002). Differences between forward and reverse logistics in a retail environment. Supply Chain Management: An International Journal, 7(5), 271-282. doi:10.1108/13598540210447719Fleischmann, M., Bloemhof-Ruwaard, J. M., Dekker, R., van der Laan, E., van Nunen, J. A. E. E., & Van Wassenhove, L. N. (1997). Quantitative models for reverse logistics: A review. European Journal of Operational Research, 103(1), 1-17. doi:10.1016/s0377-2217(97)00230-0Jeswiet, J., & Hauschild, M. (2005). EcoDesign and future environmental impacts. Materials & Design, 26(7), 629-634. doi:10.1016/j.matdes.2004.08.016Goggin, K., & Browne, J. (2000). The resource recovery level decision for end-of-life products. Production Planning & Control, 11(7), 628-640. doi:10.1080/095372800432098Mollenkopf, D. A., Frankel, R., & Russo, I. (2010). Creating value through returns management: Exploring the marketing-operations interface. Journal of Operations Management, 29(5), 391-403. doi:10.1016/j.jom.2010.11.004MOYER, L. K., & GUPTA, S. M. (1997). ENVIRONMENTAL CONCERNS AND RECYCLING/DISASSEMBLY EFFORTS IN THE ELECTRONICS INDUSTRY. Journal of Electronics Manufacturing, 07(01), 1-22. doi:10.1142/s0960313197000026Staikos, T., & Rahimifard, S. (2007). A decision-making model for waste management in the footwear industry. International Journal of Production Research, 45(18-19), 4403-4422. doi:10.1080/00207540701450187Mangun, D., & Thurston, D. L. (2002). Incorporating component reuse, remanufacture, and recycle into product portfolio design. IEEE Transactions on Engineering Management, 49(4), 479-490. doi:10.1109/tem.2002.807292Parlamento Europeo y del Consejo de 20 de noviembre de 2013 relativa al Programa General de Acción de la Unión en materia de Medio Ambiente hasta 2020 https://eur-lex.europa.eu/legal-content/ES/TXT/?uri=CELEX%3A32013D1386Hazen, B. T., Cegielski, C., & Hanna, J. B. (2011). Diffusion of green supply chain management. The International Journal of Logistics Management, 22(3), 373-389. doi:10.1108/09574091111181372Transforming Our World: The 2030 Agenda for Sustainable Development, Resolution 70/1, Resolution Adopted by the General Assembly on 25 September 2015 http://www.un.org/en/ga/search/view_doc.asp?symbol=A/RES/70/1Glavič, P., & Lukman, R. (2007). Review of sustainability terms and their definitions. Journal of Cleaner Production, 15(18), 1875-1885. doi:10.1016/j.jclepro.2006.12.006Santillo, D. (2007). Reclaiming the Definition of Sustainability (7 pp). Environmental Science and Pollution Research - International, 14(1), 60-66. doi:10.1065/espr2007.01.375Kirchherr, J., Reike, D., & Hekkert, M. (2017). Conceptualizing the circular economy: An analysis of 114 definitions. Resources, Conservation and Recycling, 127, 221-232. doi:10.1016/j.resconrec.2017.09.005Stahel, W. R. (2016). The circular economy. Nature, 531(7595), 435-438. doi:10.1038/531435aZink, T., & Geyer, R. (2017). Circular Economy Rebound. Journal of Industrial Ecology, 21(3), 593-602. doi:10.1111/jiec.12545Gusmerotti, N. M., Testa, F., Corsini, F., Pretner, G., & Iraldo, F. (2019). Drivers and approaches to the circular economy in manufacturing firms. Journal of Cleaner Production, 230, 314-327. doi:10.1016/j.jclepro.2019.05.044Schroeder, P., Anggraeni, K., & Weber, U. (2018). The Relevance of Circular Economy Practices to the Sustainable Development Goals. Journal of Industrial Ecology, 23(1), 77-95. doi:10.1111/jiec.12732Vermunt, D. A., Negro, S. O., Verweij, P. A., Kuppens, D. V., & Hekkert, M. P. (2019). Exploring barriers to implementing different circular business models. Journal of Cleaner Production, 222, 891-902. doi:10.1016/j.jclepro.2019.03.052Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603-626. doi:10.1016/j.ejor.2014.07.012Bufardi, A., Gheorghe, R., Kiritsis, D., & Xirouchakis, P. (2004). Multicriteria decision-aid approach for product end-of-life alternative selection. International Journal of Production Research, 42(16), 3139-3157. doi:10.1080/00207540410001699192Iakovou, E., Moussiopoulos, N., Xanthopoulos, A., Achillas, C., Michailidis, N., Chatzipanagioti, M., … Kikis, V. (2009). A methodological framework for end-of-life management of electronic products. Resources, Conservation and Recycling, 53(6), 329-339. doi:10.1016/j.resconrec.2009.02.001Lee, S. G., Lye, S. W., & Khoo, M. K. (2001). A Multi-Objective Methodology for Evaluating Product End-of-Life Options and Disassembly. The International Journal of Advanced Manufacturing Technology, 18(2), 148-156. doi:10.1007/s001700170086Chan, J. W. K. (2008). Product end-of-life options selection: grey relational analysis approach. International Journal of Production Research, 46(11), 2889-2912. doi:10.1080/00207540601043124Haji Vahabzadeh, A., Asiaei, A., & Zailani, S. (2015). Green decision-making model in reverse logistics using FUZZY-VIKOR method. Resources, Conservation and Recycling, 103, 125-138. doi:10.1016/j.resconrec.2015.05.023Azapagic, A., Millington, A., & Collett, A. (2006). A Methodology for Integrating Sustainability Considerations into Process Design. Chemical Engineering Research and Design, 84(6), 439-452. doi:10.1205/cherd05007Silva, N. D., Jawahir, I. S., Jr., O. D., & Russell, M. (2009). A new comprehensive methodology for the evaluation of product sustainability at the design and development stage of consumer electronic products. International Journal of Sustainable Manufacturing, 1(3), 251. doi:10.1504/ijsm.2009.023973Hula, A., Jalali, K., Hamza, K., Skerlos, S. J., & Saitou, K. (2003). Multi-Criteria Decision-Making for Optimization of Product Disassembly under Multiple Situations. Environmental Science & Technology, 37(23), 5303-5313. doi:10.1021/es0345423Jaafar, I. H., Venkatachalam, A., Joshi, K., Ungureanu, A. C., De Silva, N., Rouch, K. E., … Jawahir, I. S. (s. f.). Product Design for Sustainability: A New Assessment Methodology and Case Studies. Environmentally Conscious Mechanical Design, 25-65. doi:10.1002/9780470168202.ch2Mateus, R., & Bragança, L. (2011). Sustainability assessment and rating of buildings: Developing the methodology SBToolPT–H. Building and Environment, 46(10), 1962-1971. doi:10.1016/j.buildenv.2011.04.023Culaba, A. B., & Purvis, M. R. I. (1999). A methodology for the life cycle and sustainability analysis of manufacturing processes. Journal of Cleaner Production, 7(6), 435-445. doi:10.1016/s0959-6526(99)00231-0Andreoli, M., & Tellarini, V. (2000). Farm sustainability evaluation: methodology and practice. Agriculture, Ecosystems & Environment, 77(1-2), 43-52. doi:10.1016/s0167-8809(99)00091-2Girardi, P., & Temporelli, A. (2017). Smartainability: A Methodology for Assessing the Sustainability of the Smart City. Energy Procedia, 111, 810-816. doi:10.1016/j.egypro.2017.03.243Presley, A., Meade, L., & Sarkis, J. (2007). A strategic sustainability justification methodology for organizational decisions: a reverse logistics illustration. International Journal of Production Research, 45(18-19), 4595-4620. doi:10.1080/00207540701440220León‐Soriano, R., Jesús Muñoz‐Torres, M., & Chalmeta‐Rosaleñ, R. (2010). Methodology for sustainability strategic planning and management. Industrial Management & Data Systems, 110(2), 249-268. doi:10.1108/02635571011020331Bakar, M. S. A., & Rahimifard, S. (2007). Computer-aided recycling process planning for end-of-life electrical and electronic equipment. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 221(8), 1369-1374. doi:10.1243/09544054jem801scKusi-Sarpong, S., Gupta, H., & Sarkis, J. (2018). A supply chain sustainability innovation framework and evaluation methodology. International Journal of Production Research, 57(7), 1990-2008. doi:10.1080/00207543.2018.1518607Sienkiewicz, M., Janik, H., Borzędowska-Labuda, K., & Kucińska-Lipka, J. (2017). Environmentally friendly polymer-rubber composites obtained from waste tyres: A review. Journal of Cleaner Production, 147, 560-571. doi:10.1016/j.jclepro.2017.01.121SIGNUS Memoria Web https://www.signus.es/memoria2019/Horodytska, O., Kiritsis, D., & Fullana, A. (2020). Upcycling of printed plastic films: LCA analysis and effects on the circular economy. Journal of Cleaner Production, 268, 122138. doi:10.1016/j.jclepro.2020.12213

A Mathematical Programming Model for Tactical Planning with Set-up Continuity in a Two-stage Ceramic Firm

[EN] It is known that capacity issues in tactical production plans in a hierarchical context are relevant since its inaccurate determination may lead to unrealistic or simply non-feasible plans at the operational level. Semi-continuous industrial processes, such as ceramic ones, often imply large setups and their consideration is crucial for accurate capacity estimation. However, in most of production planning models developed in a hierarchical context at this tactical (aggregated) level, setup changes are not explicitly considered. Their consideration includes not only decisions about lot sizing of production, but also allocation, known as Capacitated Lot Sizing and Loading Problem (CLSLP). However, CLSLP does not account for set-up continuity, specially important in contexts with lengthy and costly set-ups and where product families minimum run length are similar to planning periods. In this work, a mixed integer linear programming (MILP) model for a two stage ceramic firm which accounts for lot sizing and loading decisions including minimum lot-sizes and set-up continuity between two consecutive periods is proposed. Set-up continuity inclusion is modelled just considering which product families are produced at the beginning and at the end of each period of time, and not the complete sequence. The model is solved over a simplified two-stage real-case within a Spanish ceramic firm. Obtained results confirm its validity.Pérez Perales, D.; Alemany, ME. (2016). A Mathematical Programming Model for Tactical Planning with Set-up Continuity in a Two-stage Ceramic Firm. International Journal of Production Management and Engineering. 4(2):53-64. doi:10.4995/ijpme.2016.5209SWORD53644

A Reference Model of Reverse Logistics Process for Improving Sustainability in the Supply Chain

[EN] The reverse logistics process (RLP) has become a key process for the supply chain (SC) given its importance for treating the increasing quantity of returned or recovered products and its impact on sustainability. However, the RLP is complex and involves a high degree of uncertainty and difficult decisions that affect SC efficiency. One of the aspects that can help the most to reduce this complexity and to improve SC efficiency is to formalize this process. The consulted studies agree on the numerous benefits of RLP formalization, but no tools, methodologies or specific solutions were found that help companies to advance in this matter. This work aims to develop a specific tool for RLP formalization so that its efficiency can be increased, leading to an improvement of SC sustainability. The main results comprise a reference model for RLP (RM-RLP) and an associated methodology so that any company can formalize its RLP by modeling its activities. The proposed tool (RM-RLP and methodology) is applied to a closed loop SC of relaxing chairs as an example of RLP formalization, proving its usefulness and, additionally, the improvements that can be reached in three RLP key indicators: total process duration, customer response time and the perceived autonomy and trust of the workers participating in the process.Alarcón Valero, F.; Cortés-Pellicer, P.; Pérez Perales, D.; Mengual Recuerda, A. (2021). A Reference Model of Reverse Logistics Process for Improving Sustainability in the Supply Chain. Sustainability. 13(18):1-29. https://doi.org/10.3390/su131810383S129131

Objective Prediction of Human Visual Acuity Using Image Quality Metrics

This work addresses the objective prediction of human uncorrected decimal visual acuity, an unsolved challenge due to the contribution of both physical and neural factors. An alternative approach to assess the image quality of the human visual system can be addressed from the image and video processing perspective. Human tolerance to image degradation is quantified by mean opinion scores, and several image quality assessment algorithms are used to maintain, control, and improve the quality of processed images. The aberration map of the eye is used to obtain the degraded theoretical image from a set of natural images. The amount of distortion added by the eye to the natural image was quantified using different image processing metrics, and the correlation between the result of each metric and subjective visual acuity was assessed. The correlation obtained for a model based on a linear combination of the normalized mean square error metric and the feature similarity index metric was very good. It was concluded that the proposed method could be an objective way to determine subjects’ monocular and uncorrected decimal visual acuity with low uncertainty

Allergy to Uncommon Pets: New Allergies but the Same Allergens.

The prevalence of exotic pet allergies has been increasing over the last decade. Years ago, the main allergy-causing domestic animals were dogs and cats, although nowadays there is an increasing number of allergic diseases related to insects, rodents, amphibians, fish, and birds, among others. The current socio-economic situation, in which more and more people have to live in small apartments, might be related to this tendency. The main allergic symptoms related to exotic pets are the same as those described for dog and cat allergy: respiratory symptoms. Animal allergens are therefore, important sensitizing agents and an important risk factor for asthma. There are three main protein families implicated in these allergies, which are the lipocalin superfamily, serum albumin family, and secretoglobin superfamily. Detailed knowledge of the characteristics of allergens is crucial to improvement treatment of uncommon-pet allergies

Capability of different microalgae species for phytoremediation processes: Wastewater tertiary treatment, CO2 bio-fixation and low cost biofuels production.

Scenedesmus obliquus, Chlorella vulgaris, Chlorella kessleri and a natural Bloom were cultivated in batch experiments, under controlled conditions, in urban wastewater (WW) and synthetic wastewater (SW) under 5% CO2 in air, with the object of estimating their capacity for nutrient removal, carbon dioxide biofixation, and generation of valuable biomass. In both culture media, the Bloom (Bl) and Scenedesmus (Sc) showed higher final biomass concentration (dried weight, dw) than the other species; the maximum yield obtained was 1950 ± 243 mg L−1 for Bl and the minimum 821 ± 88 mg L−1 for Cv, both in synthetic wastewater. Maximum specific growth rate values do not show significant differences between any of the 4 strains tested (p ≤ 0.05), nor between the 2 culture media. A new homogeneous method of calculating productivities has been proposed. Nitrogen removal in all the reactors was higher than 90%, except for BlSW (79%), and for phosphorus, the removal was higher than 98% in all trials. Maximum CO2 consumption rates reached were 424.4 and 436.7 mg L−1 d−1 for ScSW and ScWW respectively