Diseño de una cadena de suministro de biocombustible a partir de residuos de café, integrando decisiones de instalaciones, ruteo e inventario, bajo un enfoque de sostenibilidad

Vista sencilla de ítem
dc.contributor.advisorSarache, William
dc.contributor.advisorCosta Salas, Yasel J.
dc.contributor.authorMorales Chávez, Marcela María
dc.contributor.orcidMorales Chávez, Marcela María [0000-0002-7384-8745]spa
dc.date.accessioned2023-06-27T13:19:20Z
dc.date.available2023-06-27T13:19:20Z
dc.date.issued2022
dc.descriptiongraficas, tablasspa
dc.description.abstractLos biocombustibles surgen como alternativa a la crisis energética y ambiental que afecta al planeta. No obstante, integrar decisiones estratégicas, tácticas y operativas, desde un enfoque sostenible, plantea grandes desafíos para el diseño de su cadena de suministro (SCND: Supply Chain Network Design). La revisión de la literatura relacionada con el SCND considerando decisiones de localización, ruteo e inventario (ILRP: Inventory Location Rounting Problem) evidencia que las contribuciones sobre biocombustibles son limitadas, la mayoría de los modelos matemáticos no consideran métricas sostenibles y la estrategia de configuración dinámica (DCS: Dynamic Configuration Strategy) ha sido totalmente ignorada. De acuerdo con los vacíos de conocimiento identificados, esta tesis doctoral presenta un conjunto de modelos matemáticos altamente novedosos para el SCND de biocombustible a partir de residuos agrícolas, integrando decisiones de localización, ruteo e inventario bajo un enfoque sostenible y estocástico. Esta investigación es la primera en abordar dentro de la formulación ILRP la DCS, lo cual constituye un avance relevante en el campo de estudio. Adicionalmente, se diseñó una heurística para resolver el modelo de optimización NP-hard utilizando la metaheurística de recocido simulado. Se analiza un caso de estudio en Colombia y 15 conjuntos de datos de la literatura. Los resultados demuestran la eficiencia de la heurística comparada con el método exacto. Se observan las ventajas de la integración sostenible del ILRP en contraste con la optimización de un solo objetivo. Adicionalmente, se evidencia que la DCS alcanza mejor desempeño económico, ambiental y social comparada con la estrategia de configuración estática en el SCND. (Texto tomado de la fuente)spa
dc.description.abstractBiofuels arise as an alternative to the energy and environmental crisis that affects the entire planet. However, integrating strategic, tactical and operational decisions, from a sustainable approach, poses great challenges for the design of its supply chain (SCND: Supply Chain Network Design). Reviewing the state of the art related to the SCND considering location, routing and inventory decisions (ILRP: Inventory Location Rounting Problem) shows that contributions on biofuels are limited, most mathematical models don’t consider sustainable metrics and the dynamic configuration strategy (DCS: Dynamic Configuration Strategy) has been totally ignored. According to the knowledge gaps identified, this doctoral thesis presents a set of highly novel mathematical models for the SCND of biofuel from agricultural waste, integrating location, routing and inventory decisions under a sustainable and stochastic approach. This research is the first one addressing DCS within the ILRP formulation, which is a relevant advance in the field of study. In addition, a heuristic was designed to solve the NP-hard optimization model using simulated annealing metaheuristics. A case study in Colombia and 15 data sets from the literature are analyzed. The results demonstrate the efficiency of heuristics compared to the exact method. Also, the advantages of the sustainable integration of the ILRP formulation are observed in contrast to the optimization of a single objective. Thus, it is evident that the CSD achieves better economic, environmental and social performance compared to the static configuration strategy in the SCND.eng
dc.description.curricularareaIndustrial, Organizaciones Y Logística.Sede Manizalesspa
dc.description.degreelevelDoctoradospa
dc.description.degreenameDoctor en Ingenieríaspa
dc.format.extentxv, 158 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/84077
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Manizalesspa
dc.publisher.facultyFacultad de Ingeniería y Arquitecturaspa
dc.publisher.placeManizales, Colombiaspa
dc.publisher.programManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Industria y Organizacionesspa
dc.relation.referencesAhmadi-Javid, A., & Seddighi, A. H. (2012). A location-routing-inventory model for designing multisource distribution networks. Engineering Optimization, 44(6), 637–656. https://doi.org/10.1080/0305215X.2011.600756spa
dc.relation.referencesAhmadi Javid, A., & Azad, N. (2010). Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research Part E: Logistics and Transportation Review, 46(5), 582–597. https://doi.org/10.1016/j.tre.2009.06.005spa
dc.relation.referencesAlikhani, R., Torabi, S. A., & Altay, N. (2021). Retail supply chain network design with concurrent resilience capabilities. International Journal of Production Economics, 234. https://doi.org/10.1016/j.ijpe.2021.108042spa
dc.relation.referencesAloui, A., Hamani, N., Derrouiche, R., & Delahoche, L. (2021). Assessing the benefits of horizontal collaboration using an integrated planning model for two-echelon energy efficiency-oriented logistics networks design. International Journal of Systems Science: Operations and Logistics. https://doi.org/10.1080/23302674.2021.1887397spa
dc.relation.referencesAmaruchkul, K. (2021). Planning migrant labor for green sugarcane harvest: A stochastic logistics model with dynamic yield prediction. Computers and Industrial Engineering, 154. https://doi.org/10.1016/j.cie.2020.107016spa
dc.relation.referencesAmbrosino, D., & Grazia Scutellà, M. (2005). Distribution network design: New problems and related models. European Journal of Operational Research, 165(3), 610–624. https://doi.org/10.1016/J.EJOR.2003.04.009spa
dc.relation.referencesAravendan, M., & Panneerselvam, R. (2014). Literature review on network design problems in closed loop and reverse supply chains. Intelligent Information Management, 2014.spa
dc.relation.referencesAsadi, E., Habibi, F., Nickel, S., & Sahebi, H. (2018). A bi-objective stochastic location-inventory-routing model for microalgae-based biofuel supply chain. Applied Energy, 228(July), 2235–2261. https://doi.org/10.1016/j.apenergy.2018.07.067spa
dc.relation.referencesAwudu, I., & Zhang, J. (2012). Uncertainties and sustainability concepts in biofuel supply chain management: A review. Renewable and Sustainable Energy Reviews, 16(2), 1359–1368. https://doi.org/http://dx.doi.org/10.1016/j.rser.2011.10.016spa
dc.relation.referencesAyoughi, H., Dehghani Podeh, H., Raad, A., & Talebi, D. (2020). Providing an Integrated Multi-Objective Model for Closed-Loop Supply Chain under Fuzzy Conditions with Upgral Approach. International Journal of Nonlinear Analysis and Applications, 11(1), 107–136.spa
dc.relation.referencesBabagolzadeh, M., Shrestha, A., Abbasi, B., & Zhang, Y. (2020). Sustainable cold supply chain management under demand uncertainty and carbon tax regulation. Transportation Research Part D, 80, 102245. https://doi.org/10.1016/j.trd.2020.102245spa
dc.relation.referencesBag, S., Dhamija, P., Bryde, D. J., & Singh, R. K. (2022). Effect of eco-innovation on green supply chain management, circular economy capability, and performance of small and medium enterprises. Journal of Business Research, 141, 60–72. https://doi.org/10.1016/j.jbusres.2021.12.011spa
dc.relation.referencesBagherinejad, J., & Shoeib, M. (2018). Dynamic capacitated maximal covering location problem by considering dynamic capacity. International Journal of Industrial Engineering Computations, 9(2), 249–264. https://doi.org/10.5267/j.ijiec.2017.5.004spa
dc.relation.referencesBanasik, A., Kanellopoulos, A., Claassen, G. D. H., Bloemhof-Ruwaard, J. M., & van der Vorst, J. G. A. J. (2017). Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain. International Journal of Production Economics, 183, 409–420. https://doi.org/10.1016/j.ijpe.2016.08.012spa
dc.relation.referencesBehzadi, G., O’Sullivan, M. J., Olsen, T. L., & Zhang, A. (2018). Agribusiness supply chain risk management: A review of quantitative decision models. Omega, 79, 21–42. https://doi.org/10.1016 / j.omega.2017.07.005spa
dc.relation.referencesBera, T., Inglett, K. S., Inglett, P. W., Vardanyan, L., Wilkie, A. C., O’Connor, G. A., & Reddy, K. R. (2021). Comparing first- and second-generation bioethanol by-products from sugarcane: Impact on soil carbon and nitrogen dynamics. Geoderma, 384. https://doi.org/10.1016/j.geoderma.2020.114818spa
dc.relation.referencesBeske, P., Land, A., & Seuring, S. (2014). Sustainable supply chain management practices and dynamic capabilities in the food industry: A critical analysis of the literature. International Journal of Production Economics, 152, 131–143. https://doi.org/10.1016/j.ijpe.2013.12.026spa
dc.relation.referencesBiuki, M., Kazemi, A., & Alinezhad, A. (2020). An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network. Journal of Cleaner Production, 260. https://doi.org/10.1016/j.jclepro.2020.120842spa
dc.relation.referencesBonilla-Hermosa, V. A., Duarte, W. F., & Schwan, R. F. (2014). Utilization of coffee by-products obtained from semi-washed process for production of value-added compounds. Bioresource Technology, 166, 142–150. https://doi.org/10.1016/j.biortech.2014.05.031spa
dc.relation.referencesBoostani, A., Jolai, F., & Bozorgi-Amiri, A. (2021). Designing a sustainable humanitarian relief logistics model in pre-and postdisaster management. International Journal of Sustainable Transportation, 15(8), 604–620.spa
dc.relation.referencesBorodin, V., Bourtembourg, J., Hnaien, F., & Labadie, N. (2016). Handling uncertainty in agricultural supply chain management: A state of the art. European Journal of Operational Research, 254(2), 348–359. https://doi.org/10.1016/j.ejor.2016.03.057spa
dc.relation.referencesCadena, E., Rocca, F., Gutierrez, J. A., & Carvalho, A. (2019). Social life cycle assessment methodology for evaluating production process design : Biore fi nery case study. Journal of Cleaner Production, 238, 117718. https://doi.org/10.1016/j.jclepro.2019.117718spa
dc.relation.referencesCenicafé. (2016). Manejo de Subproductos. https://www.cenicafe.org/es/index.php/cultivemos_cafe/manejo_de_subproductosspa
dc.relation.referencesChoi, I. S., Wi, S. G., Kim, S.-B., & Bae, H.-J. (2012). Conversion of coffee residue waste into bioethanol with using popping pretreatment. Bioresource Technology, 125, 132–137. https://doi.org/10.1016/j.biortech.2012.08.080spa
dc.relation.referencesChristopher, M. (2007). New directions in logistics. Waters, D., Global Logistics: New Directions in Supply Chain Management, London, Kogan Page Limited, 21–32.spa
dc.relation.referencesConpes. (2008). C o n p e s 3510. Lineamientos de política para promover la producción sostenible de Biocombustibles en Colombia. http://www.minminas.gov.co/minminas/downloads/UserFiles/File/Conpes 3510.pdfspa
dc.relation.referencesConpes. (2022). CONPES 4075. Política de transición energética. https://colaboracion.dnp.gov.co/CDT/Conpes/Económicos/4075.pdfspa
dc.relation.referencesCorrea, D. F., Beyer, H. L., Possingham, H. P., Fargione, J. E., Hill, J. D., & Schenk, P. M. (2021). Microalgal biofuel production at national scales: Reducing conflicts with agricultural lands and biodiversity within countries. Energy, 215. https://doi.org/10.1016/j.energy.2020.119033spa
dc.relation.referencesČuček, L., Martín, M. J. P., Grossmann, I. E., & Kravanja, Z. (2012). Multi-objective optimization of a biorefinery’s supply network. AIChE 2012 - 2012 AIChE Annual Meeting, 1.spa
dc.relation.referencesda Silva, C., Barbosa-Póvoa, A. P., & Carvalho, A. (2020). Environmental monetization and risk assessment in supply chain design and planning. Journal of Cleaner Production, 270. https://doi.org/10.1016/j.jclepro.2020.121552spa
dc.relation.referencesDuarte, A., Sarache, W., & Costa, Y. (2014). A facility-location model for biofuel plants: Applications in the Colombian context. Energy, 72, 476–483. https://doi.org/10.1016/j.energy.2014.05.069spa
dc.relation.referencesDuarte, A., Sarache, W., & Costa, Y. (2016). Biofuel supply chain design from Coffee Cut Stem under environmental analysis. Energy, 100, 321–331. https://doi.org/10.1016/J.ENERGY.2016.01.076spa
dc.relation.referencesEcheverry, M. (2009). Algoritmos evolutivos y técnicas bio-inspiradas. De la teoría a la práctica. Universidad tecnológica de Pereira Pereira.spa
dc.relation.referencesEskandarpour, M., Dejax, P., Miemczyk, J., & Péton, O. (2015). Sustainable supply chain network design: An optimization-oriented review. Omega (United Kingdom), 54, 11–32. https://doi.org/10.1016/j.omega.2015.01.006spa
dc.relation.referencesFallah-Tafti, A., Vahdatzad, M. A., & Sadegheiyeh, A. (2019). A comprehensive mathematical model for a location-routing-inventory problem under uncertain demand: A numerical illustration in cash-in-transit sector. International Journal of Engineering, Transactions B: Applications, 32(11), 1634 – 1642. https://doi.org/10.5829/ije.2019.32.11b.15spa
dc.relation.referencesFAO. (2013). Food wastage footprint: impacts on natural resources: summary report. Food \& Agriculture Org.spa
dc.relation.referencesFatemi Ghomi, S. M. T., & Asgarian, B. (2019). Development of metaheuristics to solve a transportation inventory location routing problem considering lost sale for perishable goods. Journal of Modelling in Management, 14(1), 175–198. https://doi.org/10.1108/JM2-05-2018-0064spa
dc.relation.referencesFedebiocombustibles. (2022). Fedebiocombustibles. https://fedebiocombustibles.com/2022/06/01/4-contribuciones-clave-del-sector-de-los-biocombustibles-ante-los-compromisos-del-cop-26-2/spa
dc.relation.referencesFeitó-Cespón, M., Costa, Y., Pishvaee, M. S., & Cespón-Castro, R. (2021). A fuzzy inference based scenario building in two-stage optimization framework for sustainable recycling supply chain redesign. Expert Systems with Applications, 165. https://doi.org/10.1016/j.eswa.2020.113906spa
dc.relation.referencesFNC. (2013a). Familias cafeteras en Colombia. http://www.federaciondecafeteros.org/particulares/es/nuestros_caficultoresspa
dc.relation.referencesFNC. (2013b). La decisión tranquila de zoquear hoy un cafetal - Federación Nacional de Cafeteros. https://federaciondecafeteros.org/wp/blog/la-decision-tranquila-de-zoquear-hoy-un-cafetal/spa
dc.relation.referencesFNC. (2022). Precios, área y producción de café. https://federaciondecafeteros.org/wp/estadisticas-cafeteras/spa
dc.relation.referencesGamborg, C., Millar, K., Shortall, O., & Sandøe, P. (2012). Bioenergy and Land Use: Framing the Ethical Debate. Journal of Agricultural and Environmental Ethics, 25(6), 909–925. http://www.scopus.com/inward/record.url?eid=2-s2.0-84869082138&partnerID=40&md5=114a56e8989289127258d3e34e663bf0spa
dc.relation.referencesGhelichi, Z., Saidi-Mehrabad, M., & Pishvaee, M. S. (2018). A stochastic programming approach toward optimal design and planning of an integrated green biodiesel supply chain network under uncertainty: A case study. Energy, 156, 661–687. https://doi.org/10.1016/j.energy.2018.05.103spa
dc.relation.referencesGholipour, S., Ashoftehfard, A., & Mina, H. (2020). Green supply chain network design considering inventory-location-routing problem: A fuzzy solution approach. International Journal of Logistics Systems and Management, 35(4), 436–452. https://doi.org/10.1504/IJLSM.2020.106272spa
dc.relation.referencesGhorashi, S. B., Hamedi, M., & Sadeghian, R. (2020). Modeling and optimization of a reliable blood supply chain network in crisis considering blood compatibility using MOGWO. Neural Computing and Applications, 32(16), 12173 – 12200. https://doi.org/10.1007/s00521-019-04343-1spa
dc.relation.referencesGovindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9–28. https://doi.org/10.1016/j.ijpe.2013.12.028spa
dc.relation.referencesGovindan, K., Mina, H., Esmaeili, A., & Gholami-Zanjani, S. M. (2020). An Integrated Hybrid Approach for Circular supplier selection and Closed loop Supply Chain Network Design under Uncertainty. Journal of Cleaner Production, 242. https://doi.org/10.1016/j.jclepro.2019.118317spa
dc.relation.referencesGRI. (2016). Global Reporting Initiative. In GRI 306: Effluents and Waste. GRI Standards. https://www.globalreporting.org/standards/gri-standards-download-center/spa
dc.relation.referencesGuerrero, W. J., Prodhon, C., Velasco, N., & Amaya, C. A. (2013). Hybrid heuristic for the inventory location-routing problem with deterministic demand. International Journal of Production Economics, 146(1), 359–370. https://doi.org/10.1016/j.ijpe.2013.07.025spa
dc.relation.referencesHabibi, F., Asadi, E., & Sadjadi, S. J. (2017). Developing a location-inventory-routing model using METRIC approach in inventory policy. Uncertain Supply Chain Management, 5(4), 337–358. https://doi.org/10.5267/j.uscm.2017.4.003spa
dc.relation.referencesHabibi, F., Asadi, E., & Sadjadi, S. J. (2018). A location-inventory-routing optimization model for cost effective design of microalgae biofuel distribution system: A case study in Iran. Energy Strategy Reviews, 22(April 2017), 82–93. https://doi.org/10.1016/j.esr.2018.08.006spa
dc.relation.referencesHajirasouli, A., & Kumarasuriyar, A. (2016). The social dimention of sustainability: Towards some definitions and analysis. Journal of Social Science for Policy Implications, 4(2), 23–34.spa
dc.relation.referencesHernández-Sampieri, R., Fernández Collado, C., Baptista Lucio, P., & others. (2018). Metodología de la investigación (Vol. 4). McGraw-Hill Interamericana México.spa
dc.relation.referencesHo, D. P., Ngo, H. H., & Guo, W. (2014). A mini review on renewable sources for biofuel. Bioresource Technology, 169(0), 742–749. https://doi.org/http://dx.doi.org/10.1016/j.biortech.2014.07.022spa
dc.relation.referencesHu, J., & Li, X. (2022). Construction and optimization of green supply chain management mode of agricultural enterprises in the digital economy. International Journal of Information Systems and Supply Chain Management, 15(2), 1–18. https://doi.org/10.4018/IJISSCM.287864spa
dc.relation.referencesHuang, E., & Goetschalckx, M. (2014). Strategic robust supply chain design based on the Pareto-optimal tradeoff between efficiency and risk. European Journal of Operational Research, 237(2), 508–518. https://doi.org/10.1016/j.ejor.2014.02.038spa
dc.relation.referencesHurford, A. P., Huskova, I., & Harou, J. J. (2014). Using many-objective trade-off analysis to help dams promote economic development, protect the poor and enhance ecological health. Environmental Science & Policy, 38, 72–86. https://doi.org/10.1016/J.ENVSCI.2013.10.003spa
dc.relation.referencesHuysman, S., Sala, S., Mancini, L., Ardente, F., Alvarenga, R. A. F., De Meester, S., Mathieux, F., & Dewulf, J. (2015). Toward a systematized framework for resource efficiency indicators. Resources, Conservation and Recycling, 95, 68–76. https://doi.org/10.1016/j.resconrec.2014.10.014spa
dc.relation.referencesICO. (2020). International Coffee Organization. https://www.ico.org/spa
dc.relation.referencesICO. (2022). Total production of exporting countries. http://www.ico.org/prices/po.htmspa
dc.relation.referencesIPCC. (2007). Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (L. A. M. B. Metz, O.R. Davidson, P.R. Bosch, R. Dave (ed.)). https://archive.ipcc.ch/publications_and_data/ar4/wg3/en/contents.htmlspa
dc.relation.referencesIPCC. (2011). Summary for Policymakers. In: IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation (C. von S. O. Edenhofer, R. Pichs-Madruga, Y. Sokona, K. Seyboth, P. Matschoss, S. Kadner, T. Zwickel, P. Eickemeier, G. Hansen, S. Schlömer (ed.)).spa
dc.relation.referencesIPCC. (2018). Summary for Policymakers. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to (V. Masson-Delmotte, P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P. R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J. B. R. Matthews, Y. Chen, X. Zhou, M. I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, & T. Waterfield (eds.)).spa
dc.relation.referencesIPCC. (2019). Summary for Policymakers. In: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems ((eds.)]. In press. [P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.- O. Pörtner, D. C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, (ed.)).spa
dc.relation.referencesIvanov, B., & Stoyanov, S. (2016). A mathematical model formulation for the design of an integrated biodiesel-petroleum diesel blends system. Energy, 99. https://doi.org/10.1016/j.energy.2016.01.038spa
dc.relation.referencesJena, S. D., Cordeau, J.-F., & Gendron, B. (2016). Solving a dynamic facility location problem with partial closing and reopening. Computers and Operations Research, 67, 143–154. https://doi.org/10.1016/j.cor.2015.10.011spa
dc.relation.referencesJiménez, J. E., & Hernández, S. (2002). Marco conceptual de la cadena de suministro : un nuevo enfoque logístico. In Instituto Mexicano del Transporte (Issue 215). http://www.elmayorportaldegerencia.com/Documentos/Cadena Suministros/[PD] Documentos - Un nuevo enfoque logistico.pdfspa
dc.relation.referencesJonkman, J., Barbosa-Póvoa, A. P., & Bloemhof, J. M. (2019). Integrating harvesting decisions in the design of agro-food supply chains. European Journal of Operational Research, 276(1), 247–258. https://doi.org/10.1016/j.ejor.2018.12.024spa
dc.relation.referencesKamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179–194. https://doi.org/10.1016/j.ijpe.2019.05.022spa
dc.relation.referencesKarakostas, P., Sifaleras, A., & Georgiadis, M. C. (2020). Adaptive variable neighborhood search solution methods for the fleet size and mix pollution location-inventory-routing problem. Expert Systems with Applications, 153. https://doi.org/10.1016/j.eswa.2020.113444spa
dc.relation.referencesKaya, O., & Ozkok, D. (2020). A Blood Bank Network Design Problem with Integrated Facility Location, Inventory and Routing Decisions. Networks and Spatial Economics, 20(3), 757 – 783. https://doi.org/10.1007/s11067-020-09500-xspa
dc.relation.referencesKirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680. https://doi.org/10.1126 / science.220.4598.671spa
dc.relation.referencesKumar, M., Tiwari, M. K., Wong, K. Y., Govindan, K., & Kuah, C. T. (2014). Evaluating reverse supply chain efficiency: Manufacturer’s perspective. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/901914spa
dc.relation.referencesLahri, V., Shaw, K., & Ishizaka, A. (2021). Sustainable supply chain network design problem: Using the integrated BWM, TOPSIS, possibilistic programming, and ε-constrained methods. Expert Systems with Applications, 168. https://doi.org/10.1016/j.eswa.2020.114373spa
dc.relation.referencesLee, S., Park, S. J., & Seshadri, S. (2017). Plant location and inventory level decisions in global supply chains: Evidence from Korean firms. European Journal of Operational Research, 262(1), 163–179. https://doi.org/10.1016/j.ejor.2017.03.044spa
dc.relation.referencesLiu, S. C., & Lee, S. B. (2003). A two-phase heuristic method for the multi-depot location routing problem taking inventory control decisions into consideration. The International Journal of Advanced Manufacturing Technology, 22(11–12), 941–950. https://doi.org/10.1007/s00170-003-1639-5spa
dc.relation.referencesLiu, Y., Ma, L., & Liu, Y. (2021). A novel robust fuzzy mean-UPM model for green closed-loop supply chain network design under distribution ambiguity. Applied Mathematical Modelling, 92, 99–135. https://doi.org/10.1016/j.apm.2020.10.042spa
dc.relation.referencesMaass, K. L., Daskin, M. S., & Shen, S. (2016). Mitigating hard capacity constraints with inventory in facility location modeling. IIE Transactions (Institute of Industrial Engineers), 48(2), 120–133. https://doi.org/10.1080/0740817X.2015.1078015spa
dc.relation.referencesMelo, M. T., Nickel, S., & Saldanha-da-Gama, F. (2009). Facility location and supply chain management – A review. European Journal of Operational Research, 196(2), 401–412. https://doi.org/10.1016/j.ejor.2008.05.007spa
dc.relation.referencesMeredith, J. R., Raturi, A., Amoako-Gyampah, K., & Kaplan, B. (1989). Alternative research paradigms in operations. Journal of Operations Management, 8(4), 297–326. https://doi.org/10.1016/0272-6963(89)90033-8spa
dc.relation.referencesMessmann, L., Zender, V., Thorenz, A., & Tuma, A. (2020). How to quantify social impacts in strategic supply chain optimization: State of the art. Journal of Cleaner Production, 257, 120459. https://doi.org/10.1016 / j.jclepro.2020.120459spa
dc.relation.referencesMetropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092.spa
dc.relation.referencesMirhashemi, M. S., Mohseni, S., Hasanzadeh, M., & Pishvaee, M. S. (2018). Moringa oleifera biomass-to-biodiesel supply chain design: An opportunity to combat desertification in Iran. Journal of Cleaner Production, 203, 313–327. https://doi.org/10.1016/j.jclepro.2018.08.257spa
dc.relation.referencesMonemi, R. N., Gelareh, S., Nagih, A., & Jones, D. (2021). Bi-objective load balancing multiple allocation hub location: a compromise programming approach. Annals of Operations Research, 296(1), 363–406.spa
dc.relation.referencesMorales-Chavez, M. M., Costa, Y., & Sarache, W. (2021). A three-objective stochastic location-inventory-routing model for agricultural waste-based biofuel supply chain. Computers \& Industrial Engineering, 162, 107759. https://doi.org/doi.org/10.1016/j.cie.2021.107759spa
dc.relation.referencesMorales-Chavez, M. M., Sarache, W., & Costa, Y. (2018). Towards a comprehensive model of a biofuel supply chain optimization from coffee crop residues. Transportation Research Part E: Logistics and Transportation Review, 116(May), 136–162. https://doi.org/10.1016/j.tre.2018.06.001spa
dc.relation.referencesMorales-Chavez, M. M., Sarache, W., Costa, Y., & Soto, J. (2020). Multiobjective stochastic scheduling of upstream operations in a sustainable sugarcane supply chain. Journal of Cleaner Production, 123305. https://doi.org/10.1016/j.jclepro.2020.123305spa
dc.relation.referencesMoreno-Camacho, C. A., Montoya-Torres, J. R., Jaegler, A., & Gondran, N. (2019). Sustainability metrics for real case applications of the supply chain network design problem: A systematic literature review. Journal of Cleaner Production, 231, 600–618. https://doi.org/10.1016/j.jclepro.2019.05.278spa
dc.relation.referencesMottaghi, M., Bairamzadeh, S., & M.S., P. (2022). A taxonomic review and analysis on biomass supply chain design and planning: New trends, methodologies and applications. Industrial Crops and Products, 180. https://doi.org/10.1016/j.indcrop.2022.114747spa
dc.relation.referencesNakhjirkan, S., Rafiei, F. M., & Kashan, A. H. (2019). Developing an integrated decision making model in supply chain under demand uncertainty using genetic algorithm and network data envelopment analysis. International Journal of Mathematics in Operational Research, 14(1), 53 – 81. https://doi.org/10.1504/IJMOR.2019.096979spa
dc.relation.referencesNasr, N., Niaki, S. T. A., Hussenzadek Kashan, A., & Seifbarghy, M. (2021). An efficient solution method for an agri-fresh food supply chain: hybridization of Lagrangian relaxation and genetic algorithm. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-021-13718-8spa
dc.relation.referencesNematollahi, M., & Tajbakhsh, A. (2020). Past, present, and prospective themes of sustainable agricultural supply chains: A content analysis. Journal of Cleaner Production, 271. https://doi.org/10.1016/j.jclepro.2020.122201spa
dc.relation.referencesNg, R. T. L., & Maravelias, C. T. (2016). Design of Cellulosic Ethanol Supply Chains with Regional Depots. Industrial and Engineering Chemistry Research, 55(12). https://doi.org/10.1021/acs.iecr.5b03677spa
dc.relation.referencesNguyen, T. H., Granger, J., Pandya, D., & Paustian, K. (2019). High-resolution multi-objective optimization of feedstock landscape design for hybrid first and second generation biorefineries. Applied Energy, 238, 1484–1496. https://doi.org/10.1016/j.apenergy.2019.01.117spa
dc.relation.referencesO’Neill, E. G., & Maravelias, C. T. (2021). Towards integrated landscape design and biofuel supply chain optimization. Current Opinion in Chemical Engineering, 31, 1–7. https://doi.org/10.1016/j.coche.2020.100666spa
dc.relation.referencesOCDE, & FAO, F. and A. O. of U. N.-. (2017). Perspectivas Agrícolas OCDE-FAO. https://doi.org/10.1007/BF02915673spa
dc.relation.referencesONU. (2018). Generación de residuos. https://www.unep.org/es/noticias-y-reportajes/comunicado-de-prensa/un-tercio-de-los-residuos-de-america-latina-y-el-caribespa
dc.relation.referencesOrganizacion de las naciones Unidas - Medio Ambiente (ONU). (2018). Perspectiva de la Gestión de Residuos en América Latina y el Caribe Perspectiva de la Gestión de Residuos en América Latina y el Caribe.spa
dc.relation.referencesPehlivan, C., Augusto, V., & Xie, X. (2014). Dynamic capacity planning and location of hierarchical service networks under service level constraints. IEEE Transactions on Automation Science and Engineering, 11(3), 863–880. https://doi.org/10.1109/TASE.2014.2309255spa
dc.relation.referencesPourhejazy, P., Kwon, O. K., & Lim, H. (2019). Integrating Sustainability into the Optimization of Fuel Logistics Networks. KSCE Journal of Civil Engineering, 23(3), 1369 – 1383. https://doi.org/10.1007/s12205-019-1373-7spa
dc.relation.referencesPrograma Mundial de Alimentos. (2020). Programa Mundial de Alimentos. https://www.un.org/sustainabledevelopment/es/hunger/spa
dc.relation.referencesRabbani, M., Amirhossein Sadati, S., & Farrokhi-Asl, H. (2020). Incorporating location routing model and decision making techniques in industrial waste management: Application in the automotive industry. Computers and Industrial Engineering, 148. https://doi.org/10.1016/j.cie.2020.106692spa
dc.relation.referencesRabbani, M., Heidari, R., & Yazdanparast, R. (2019). A stochastic multi-period industrial hazardous waste location-routing problem: Integrating NSGA-II and Monte Carlo simulation. European Journal of Operational Research, 272(3), 945 – 961. https://doi.org/10.1016/j.ejor.2018.07.024spa
dc.relation.referencesRahbari, M., Arshadi Khamseh, A., Sadati-Keneti, Y., & Jafari, M. J. (2022). A risk-based green location-inventory-routing problem for hazardous materials: NSGA II, MOSA, and multi-objective black widow optimization. Environment, Development and Sustainability, 24(2), 2804 – 2840. https://doi.org/10.1007/s10668-021-01555-1spa
dc.relation.referencesRíos, P. (2018). Metodología de la Investigación: Un enfoque pedagógico. Editorial Cognitus CA.spa
dc.relation.referencesRocha, M. V. P., de Matos, L. J. B. L., Lima, L. P. D., Figueiredo, P. M. D. S., Lucena, I. L., Fernandes, F. A. N., & Gonçalves, L. R. B. (2014). Ultrasound-assisted production of biodiesel and ethanol from spent coffee grounds. Bioresource Technology, 167, 343–348. https://doi.org/10.1016/j.biortech.2014.06.032spa
dc.relation.referencesRodríguez Valencia, N., Zambrano Franco, A., Rodríguez, N., & Zambrano, D. (2010). Los subproductos del café: fuente de energía renovable. In Avances Técnicos Cenicafé (Colombia)(no. 393) 0120-0178 (Issue 3). https://doi.org/doi.org/ISSN-0120-0178spa
dc.relation.referencesRomero, C. (1993). Teoría de la decisión multicriterio: conceptos, técnicas y aplicaciones. (Issue 338 ROM).spa
dc.relation.referencesRomero, C., & Rehman, T. (2003). Multiple criteria analysis for agricultural decisions (Vol. 11). Elsevier.spa
dc.relation.referencesSaif-Eddine, A. S., El-Beheiry, M. M., & El-Kharbotly, A. K. (2019). An improved genetic algorithm for optimizing total supply chain cost in inventory location routing problem. Ain Shams Engineering Journal, 10(1), 63–76. https://doi.org/10.1016/j.asej.2018.09.002spa
dc.relation.referencesSampieri, R., Fernández, C., & Baptista, P. (2010). METODOLOGÍA de la investigación (5th ed.). Mc Graw Hill.spa
dc.relation.referencesSarache, W., & Morales-Chavez, M. M. (2016). Localización, transporte e inventarios: tres decisiones estructurales en el diseño de cadenas de abastecimiento (Editorial Universidad Nacional de Colombia (ed.)).spa
dc.relation.referencesSarma, D., Das, A., & Bera, U. K. (2020). Uncertain demand estimation with optimization of time and cost using Facebook disaster map in emergency relief operation. Applied Soft Computing, 87, 105992. https://doi.org/10.1016/j.asoc.2019.105992spa
dc.relation.referencesSasikumar, P., & Kannan, G. (2008a). Issues in reverse supply chains, part I: End-of-life product recovery and inventory management - an overview. International Journal of Sustainable Engineering, 1(3), 154–172. https://doi.org/10.1080/19397030802433860spa
dc.relation.referencesSasikumar, P., & Kannan, G. (2008b). Issues in reverse supply chains, part II: Reverse distribution issues - an overview. International Journal of Sustainable Engineering, 1(4), 234–249. https://doi.org/10.1080/19397030802509974spa
dc.relation.referencesSchaffel, S. B., & La Rovere, E. L. (2010). The quest for eco-social efficiency in biofuels production in Brazil. Journal of Cleaner Production, 18(16–17), 1663–1670. https://doi.org/10.1016/j.jclepro.2010.06.031spa
dc.relation.referencesSerageldin, I., & Steer, A. (1994). Making development sustainable: from concepts to action. In Making development sustainable: from concepts to action. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041151110&partnerID=40&md5=e7bb7fe34d6c61fd78bf0acf58238f88spa
dc.relation.referencesSeuring, S, & Gold, S. (2012). Conducting content-analysis based literature reviews in supply chain management. Supply Chain Management, 17(5), 544–555. https://doi.org/10.1108/13598541211258609spa
dc.relation.referencesSeuring, Stefan, & Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15), 1699–1710. https://doi.org/10.1016/j.jclepro.2008.04.020spa
dc.relation.referencesShanmugam, S., Hari, A., Kumar, D., Rajendran, K., Mathimani, T., Atabani, A. E., Brindhadevi, K., & Pugazhendhi, A. (2021). Recent developments and strategies in genome engineering and integrated fermentation approaches for biobutanol production from microalgae. Fuel, 285. https://doi.org/10.1016/j.fuel.2020.119052spa
dc.relation.referencesSharma, B., Ingalls, R. G., Jones, C. L., & Khanchi, A. (2013). Biomass supply chain design and analysis: Basis, overview, modeling, challenges, and future. Renewable and Sustainable Energy Reviews, 24(0), 608–627. https://doi.org/http://dx.doi.org/10.1016/j.rser.2013.03.049spa
dc.relation.referencesSingh, A. R., Mishra, P. K., Jain, R., & Khurana, M. K. (2012). Design of global supply chain network with operational risks. International Journal of Advanced Manufacturing Technology, 60(1–4), 273–290. https://doi.org/10.1007/s00170-011-3615-9spa
dc.relation.referencesTavakkoli-Moghaddam, R., & Raziei, Z. (2016). A New Bi-Objective Location-Routing-Inventory Problem with Fuzzy Demands. IFAC-PapersOnLine, 49(12), 1116–1121. https://doi.org/10.1016/J.IFACOL.2016.07.646spa
dc.relation.referencesTavana, M., Abtahi, A.-R., Di Caprio, D., Hashemi, R., & Yousefi-Zenouz, R. (2018). An integrated location-inventory-routing humanitarian supply chain network with pre- and post-disaster management considerations. Socio-Economic Planning Sciences, 64, 21 – 37. https://doi.org/10.1016/j.seps.2017.12.004spa
dc.relation.referencesTavana, M., Tohidi, H., Alimohammadi, M., & Lesansalmasi, R. (2021). A location-inventory-routing model for green supply chains with low-carbon emissions under uncertainty. Environmental Science and Pollution Research, 28(36), 50636 – 50648. https://doi.org/10.1007/s11356-021-13815-8spa
dc.relation.referencesTriana, C. F., Quintero, J. A., Agudelo, R. A., Cardona, C. A., & Higuita, J. C. (2011). Analysis of coffee cut-stems (CCS) as raw material for fuel ethanol production. Energy, 36(7), 4182–4190. https://doi.org/10.1016/j.energy.2011.04.025spa
dc.relation.referencesValderrama, C. V., Santiba\vnez-González, E., Pimentel, B., Candia-Véjar, A., & Canales-Bustos, L. (2020). Designing an environmental supply chain network in the mining industry to reduce carbon emissions. Journal of Cleaner Production, 119688.spa
dc.relation.referencesVan Engeland, J., Beliën, J., De Boeck, L., & De Jaeger, S. (2020). Literature review: Strategic network optimization models in waste reverse supply chains. Omega (United Kingdom), 91. https://doi.org/10.1016/j.omega.2018.12.001spa
dc.relation.referencesWaltho, C., Elhedhli, S., & Gzara, F. (2019). Green supply chain network design: A review focused on policy adoption and emission quantification. International Journal of Production Economics, 208, 305–318. https://doi.org/10.1016/j.ijpe.2018.12.003spa
dc.relation.referencesWilliams, P. R. D., Inman, D., Aden, A., & Heath, G. A. (2009). Environmental and sustainability factors associated with next-generation biofuels in the U.S.: What do we really know? Environmental Science and Technology, 43(13), 4763–4775. https://doi.org/10.1021/es900250dspa
dc.relation.referencesWu, W., Zhou, W., Lin, Y., Xie, Y., & Jin, W. (2021). A hybrid metaheuristic algorithm for location inventory routing problem with time windows and fuel consumption. Expert Systems with Applications, 166. https://doi.org/10.1016/j.eswa.2020.114034spa
dc.relation.referencesYaghoubi, A., & Akrami, F. (2019). Proposing a new model for location - routing problem of perishable raw material suppliers with using meta-heuristic algorithms. Heliyon, 5(12), e03020. https://doi.org/10.1016/j.heliyon.2019.e03020spa
dc.relation.referencesYao, X., & Askin, R. (2019). Review of supply chain configuration and design decision-making for new product. International Journal of Production Research, 57(7), 2226–2246. https://doi.org/10.1080/00207543.2019.1567954spa
dc.relation.referencesYuchi, Q., Wang, N., He, Z., & Chen, H. (2021). Hybrid heuristic for the location-inventory-routing problem in closed-loop supply chain. International Transactions in Operational Research, 28(3), 1265 – 1295. https://doi.org/10.1111/itor.12621spa
dc.relation.referencesZandkarimkhani, S., Mina, H., Biuki, M., & Govindan, K. (2020). A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design. Annals of Operations Research, 295(1), 425 – 452. https://doi.org/10.1007/s10479-020-03677-7spa
dc.relation.referencesZeleny, M. (1973). Compromise programming, multiple criteria decision-making. Multiple Criteria Decision Making. University of South Carolina Press, Columbia, 263–301.spa
dc.relation.referencesZhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M. (2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 89, 182–214. https://doi.org/10.1016/j.tre.2016.02.011spa
dc.relation.referencesZhang, Y., Qi, M., Miao, L., & Liu, E. (2014). Hybrid metaheuristic solutions to inventory location routing problem. Transportation Research Part E: Logistics and Transportation Review, 70(1), 305–323. https://doi.org/10.1016/j.tre.2014.07.010spa
dc.relation.referencesZhao, J., & Ke, G. Y. (2017). Incorporating inventory risks in location-routing models for explosive waste management. International Journal of Production Economics, 193, 123–136. https://doi.org/10.1016/j.ijpe.2017.07.001spa
dc.relation.referencesZhao, X., Ke, Y., Zuo, J., Xiong, W., & Wu, P. (2020). Evaluation of sustainable transport research in 2000 e 2019. Journal of Cleaner Production, 256, 120404. https://doi.org/10.1016/j.jclepro.2020.120404spa
dc.relation.referencesZheng, X., Yin, M., & Zhang, Y. (2019). Integrated optimization of location, inventory and routing in supply chain network design. Transportation Research Part B: Methodological, 121, 1–20. https://doi.org/10.1016/j.trb.2019.01.003spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.ddc620 - Ingeniería y operaciones afinesspa
dc.subject.proposalProblema de inventario localización y ruteamientospa
dc.subject.proposalEstrategia de configuración dinámicaspa
dc.subject.proposalCadena de suministro sosteniblespa
dc.subject.proposalAlgoritmo de recocido simuladospa
dc.subject.proposalInventory location routing problemeng
dc.subject.proposalDynamic configuration strategyeng
dc.subject.proposalSustainable supply chaineng
dc.subject.proposalSimulated annealing algorithmeng
dc.subject.unescoDesarrollo sosteniblespa
dc.subject.unescoSustainable developmenteng
dc.subject.unescoDesperdicio agrícolaspa
dc.subject.unescoAgricultural wasteseng
dc.subject.unescoFuente de energía renovablespa
dc.subject.unescoRenewable energy sourceseng
dc.subject.unescoModelo matemáticospa
dc.subject.unescoMathematical modelseng
dc.subject.unescoOptimizaciónspa
dc.subject.unescoOptimizationeng
dc.titleDiseño de una cadena de suministro de biocombustible a partir de residuos de café, integrando decisiones de instalaciones, ruteo e inventario, bajo un enfoque de sostenibilidadspa
dc.title.translatedDesign of a biofuel supply chain from coffee waste, integrating facility, routing and inventory decisions, under a sustainable approacheng
dc.typeTrabajo de grado - Doctoradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_db06spa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentImagespa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/doctoralThesisspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentAdministradoresspa
dcterms.audience.professionaldevelopmentBibliotecariosspa
dcterms.audience.professionaldevelopmentConsejerosspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentGrupos comunitariosspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMaestrosspa
dcterms.audience.professionaldevelopmentMedios de comunicaciónspa
dcterms.audience.professionaldevelopmentPadres y familiasspa
dcterms.audience.professionaldevelopmentPersonal de apoyo escolarspa
dcterms.audience.professionaldevelopmentProveedores de ayuda financiera para estudiantesspa
dcterms.audience.professionaldevelopmentPúblico generalspa
dcterms.audience.professionaldevelopmentReceptores de fondos federales y solicitantesspa
dcterms.audience.professionaldevelopmentResponsables políticosspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

Archivos

Bloque original

Mostrando 1 - 2 de 2
Miniatura
Nombre:
42153083.2023.pdf
Tamaño:
6.7 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Doctorado en Ingeniería - Industria y Organizaciones
Descargar
Miniatura
Nombre:
Anexos.pdf
Tamaño:
1.89 MB
Formato:
Adobe Portable Document Format
Descripción:
Anexos
Descargar

Bloque de licencias

Mostrando 1 - 1 de 1
Miniatura
Nombre:
license.txt
Tamaño:
5.74 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Doctorado en Ingeniería - Industria y Organizaciones