Search Results - "Toma de decisiones - Modelos matemáticos"

Relation: R. Albert and A.-L. Barabási. Statistical mechanics of complex networks. Reviews of Modern Physics, 2002; M. Newman. Networks. Oxford University Press, second ed. edition, 2018; A. Barrat, M. Barthélemy, and A. Vespignani. Dynamical Processes on Complex Networks. Cambridge University Press, Cambridge, 2008; E. Estrada. The Structure of Complex Networks: Theory and Applications. Oxford University Press, 10 2011; A. P. Riascos and J. L. Mateos. Fractional dynamics on networks: Emergence of anomalous diffusion and l´evy flights. Physical Review E, 90:032809, 2014; N. Masuda, M. A. Porter, and R. Lambiotte. Random walks and diffusion on networks. Physics Reports, 716-717:1–58, 2017; A. P. Riascos. Dissimilarity between synchronization processes on networks. Physical Review E, 109:044301, 2024; G. Simmons and J. S. Robertson. Ecuaciones diferenciales: con aplicaciones y notas históricas. McGraw-Hill, 1993; M. Kot. Elements of Mathematical Ecology. Cambridge University Press, 2001; J. D. Murray. Mathematical Biology I. An Introduction, volume 17 of Interdisciplinary Applied Mathematics. Springer, New York, 3 edition, 2002; R. May. Will a large complex system be stable? Nature, 238:413–414, 1972; S. Allesina and S. Tang. Stability criteria for complex ecosystems. Nature, 483(7388):205–208, 2012; A. M. Mambuca, C. Cammarota, and I. Neri. Dynamical systems on large networks with predator-prey interactions are stable and exhibit oscillations. Physical Review E, 105, 1 2022; X. Liu, G. W. Constable, and J. W. Pitchford. Feasibility and stability in large Lotka Volterra systems with interaction structure. Physical Review E, 107, 5 2023; T. A. D. Pirey and G. Bunin. Many-species ecological fluctuations as a jump process from the brink of extinction. Physical Review X, 14, 1 2024; A. Castellanos. Towards an ecological and functional framework for modeling the structure and dynamics of the human gut microbiome, 2023. Tesis de Maestría, Universidad de los Andes. Asesores: Andrés Quiñones, Maryam Chaib De Mares, Alejandro Reyes, Katherine Coyte; A.-L. Barabási. Network science. Cambridge University Press, Cambridge, 2016; A. P. Riascos and J. L. Mateos. Random walks on weighted networks: A survey of local and non-local dynamics. Journal of Complex Networks, 9, 2021; T. M. Michelitsch, A. P. Riascos, B. A. Collet, A. F. Nowakowski, and F. C. G. A. Nicolleau. Fractional Dynamics on Networks and Lattices. ISTE/Wiley, London, 2019; J. D. Noh and H. Rieger. Random walks on complex networks. Physical Review Letters, 92:118701, 2004; A. P. Riascos and F. H. Padilla. A measure of dissimilarity between diffusive processes on networks. Journal of Physics A: Mathematical and Theoretical, 56, 2023; Y. Kuramoto. Chemical Oscillations, Waves, and Turbulence. Springer Berlin, Heidelberg, Berlin, Heidelberg, 1984; A. Arenas, A. D´ıaz-Guilera, J. Kurths, Y. Moreno, and C. Zhou. Synchronization in complex networks. Physics Reports, 469(3):93–153, 2008; P. Ji, J. Ye, Y. Mu, W. Lin, Y. Tian, C. Hens, M. Perc, Y. Tang, J. Sun, and J. Kurths. Signal propagation in complex networks. Physics Reports, 1017:1–96, 2023; M. E. Muscarella and J. P. O’Dwyer. Species dynamics and interactions via metabolically informed consumer-resource models. Theoretical Ecology, 13(4):503–518, 2020; S. C. Chapra and R. P. Canale. Numerical methods for engineers. McGraw-Hill Higher Education, 2010; M. J. Hernandez. Disentangling nature, strength and stability issues in the characterization of population interactions. Journal of Theoretical Biology, 261:107–119, 11 2009.; I. Akjouj, M. Barbier, M. Clenet, W. Hachem, M. Ma¨ıda, F. Massol, J. Najim, and V. C. Tran. Complex systems in ecology: a guided tour with large Lotka-Volterra models and random matrices. Proceedings of the Royal Society A, 480, 2024; Y. Dou and Z. Zhou. Continuity of periodic solutions for lotka–volterra equations in coefficient functions. Zeitschrift f¨ur angewandte Mathematik und Physik, 74, 2023; A. Ferrarini. Evolutionary network control also holds for nonlinear networks: Ruling the Lotka-Volterra model. Network Biology, 5:34–42, 2015; C. G. Chakrabarti, S. Ghosh, and S. Bhadra. Non-equilibrium thermodynamics of Lotka-Volterra ecosystems: Stability and evolution. Journal of Biological Physics, 21(4):273–284, 1995; Q. Yu, D. Fang, X. Zhang, C. Jin, and Q. Ren. Stochastic evolution dynamic of the rock-scissors-paper game based on a quasi birth and death process. Scientific Reports, 6(1):28585, 2016; D. Griffon and M. J. Hernandez. Some theoretical notes on agrobiodiversity: spatial heterogeneity and population interactions. Agroecology and Sustainable Food Systems, 44:795–823, 7 2020; T. Verma and A. K. Gupta. Evolutionary dynamics of rock-paper-scissors game in the patchy network with mutations. Chaos, Solitons & Fractals, 153:111538, 2021; T. E. Gibson, Y. Kim, S. Acharya, D. E. Kaplan, N. DiBenedetto, R. Lavin, B. Berger, J. R. Allegretti, L. Bry, and G. K. Gerber. Intrinsic instability of the dysbiotic microbiome revealed through dynamical systems inference at scale. bioRxiv, 2021. bioRxiv 2021.12.14.469105; https://repositorio.unal.edu.co/handle/unal/87688; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

Availability: https://repositorio.unal.edu.co/handle/unal/87688
https://repositorio.unal.edu.co/

View record from BASE

5

Academic Journal

A bilevel framework for decision-making under uncertainty with contextual information

Authors: Morales-González, Juan Miguel, Pineda-Morente, Salvador, Muñoz Díaz, Miguel Ángel

Subject Terms: Toma de decisiones - Modelos matemáticos, Estadística, Data-driven decision-making under uncertainty, Bilevel programming, Statistical regression, Strategic producer, Electricity market

Relation: Omega;108; https://hdl.handle.net/10630/23434; https://doi.org/10.1016/j.omega.2021.102575

Availability: https://hdl.handle.net/10630/23434
https://doi.org/10.1016/j.omega.2021.102575

View record from BASE

6

Dissertation/ Thesis

Optimización de las estructuras tradicionales de reaseguros

Authors: Peña Muñoz, Alvaro

Contributors: Rojas Silva, Ignacio

Subject Terms: Reaseguros, Ratio de pérdidas (Seguros), Analítica de negocios, Evaluación de riesgos - Modelos matemáticos, Toma de decisiones - Modelos matemáticos, Riesgo (Seguros)

File Description: 58 paginas.; application/pdf

Relation: LaReferencia; Superintendencia Financiera de Colombia. (2014). Circular Básica Jurídica 029 de 2014. Gobierno de Colombia. (2010). Decreto 255 de 2010 Borch, K. (2014). The Optimal Reinsurance Treaty. Cambridge University Press, págs. 293-297. Hu, S., Hu, X., & Hu, J. (2021). The Optimal Reinsurance Strategy under Conditional Tail Expectation (CTE) and Wang’s Premium Principle. Hindawi, Mathematical Problems in Engineering. Noviyanti, L., Zanbar, A., Chadidjah, A., & Afifah, H. (2018). Optimal Retention for a Quota Share Reinsurance. Jurnal Teknik Industri. Gavranovic, G., Haberman, S. (2003) Optimal Quota Share Life Reinsurance on a Risk Premium Basis Putre, A. D. (2021). Quota-share and stop-loss reinsurance. Journal of Physics: Conference Series, Conf. Ser. 1725 012097. Zanotto, A. (2019). Optimal reinsurance treaties: assessment of capital requierement and profitbailty for a multi-line insurer. Milano: Università Cattolica Del Sacro Cuore Vermassen, O. (2017) Felxible modeling of frequency-severity data. Universiteit Gent. Hewitt, C., Lefkowitz, B. Methods for Fitting Distribuciones to Insurance Loss Data Clemente, C. Guerrerio, G., Bravo, J. (2023) Modelling Motor Insurance Claim Frecuency and Severity. Risks Atehortua, L. (2019) Optimización del Reaseguro a Través de la Metodología de Frontera Eficiente. Universidad EAFIT Van Lelyveld, Liedorp y Kampman (2011). An empirical assessment of reinsurance risk. Journal of Financial Stability, 7(4), 191-203. Willis Re (2016). Global reinsurance and risk appetite report 2016. Krvavych, Y., Sherris, M. (2006) Enhancing insurer value through reinsurance optimizacion Miller, M. M. (2006). Risk Management and Insurance. McGraw-Hill Education. Swiss Re (2015) La regulacion de la solvencia del seguro en Latinoamerica: modernizacion a differentes velocidades 46 Cruz, M. El regimen de solvencia en Colombia: una mirada desde las cifras. Fasecolda Crisafulli, M. (2023) Efficient reinsurance strategies considering counterparty default risk. Sapienza Universita Di Roma Selva, V. (2021) A Risk Profitability Analysis of Mulit-Line Reinsurance Contracts in the Solvency II Framework. Universita Cattolica del Sacro Cuore di Milano Ingram, D. (2018) The P&C Reinsurance Landscape Chernobai, A., Burnecki, K., Rachev, S., Truck, S., Weron, R. Modelling catastrophe claims with left-truncated severity distributions. European Commission. (2009). Solvency I: An Overview. Disponible en https://commission.europa.eu/finance/solvency-ii_en Cummins, J. D., & Weiss, M. A. (2013). The Economics of Insurance: An Introduction. Edward Elgar Publishing. Alexander, K., & Dhumale, R. (2007). The Regulation and Supervision of Financial Markets. Routledge. Ohlsson, E., & Johansson, B. (2010). Non-Life Insurance Pricing with Generalized Linear Models. Springer. Frees, E. W. (2014). Regression Modeling with Actuarial and Financial Applications. Cambridge University Press.2 Burnham, K. P., & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed.). Springer-Verlag. Harrington, S. E., & Niehaus, G. R. (2003). Risk Management and Insurance. McGraw-Hill Education. Ross, S. A., Westerfield, R. W., & Jaffe, J. (2013). Corporate Finance. McGraw-Hill Education. Bazaraa, M. S., Jarvis, J. J., & Sherali, H. D. (2013). Linear Programming and Network Flows. Wiley.; https://repositorio.escuelaing.edu.co/handle/001/3439

Availability: https://repositorio.escuelaing.edu.co/handle/001/3439
https://catalogo-intra.escuelaing.edu.co/cgi-bin/koha/catalogue/detail.pl?biblionumber=23948

View record from BASE

7

Academic Journal

Knowledge modelling for supporting decision making in optimal distributed design process

Authors: Mejía Gutiérrez, Ricardo, Fischer Estia, Xavier, Bennis, Fouad

Contributors: Universidad EAFIT. Departamento de Ingeniería de Diseño, Ricardo Mejia (rmejiag@eafit.edu.co), Ingeniería de Diseño - GRID

Source: 2007 IEEE International Conference on Industrial Engineering and Engineering Management. :2076-2080

Subject Terms: Mathematical models, 0209 industrial biotechnology, Mathematical optimization, OPTIMIZACIÓN MATEMÁTICA, 02 engineering and technology, Intelligent control systems, DISEÑO DE PRODUCTOS, INTELIGENCIA ARTIFICIAL DISTRIBUIDA, TOMA DE DECISIONES - MODELOS MATEMÁTICOS, 0202 electrical engineering, electronic engineering, information engineering, Distributed artificial intelligence, Sistemas Multiagentes, MODELOS MATEMÁTICOS, SISTEMAS DE CONTROL INTELIGENTE, Decision-making

File Description: application/pdf

Access URL: http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000004419557
https://repository.eafit.edu.co/xmlui/handle/10784/5030
https://hal.archives-ouvertes.fr/hal-00212040
http://hdl.handle.net/10784/5030

8

Dissertation/ Thesis

Gestión de flujo de pacientes críticos mediante una política proactiva de traslados

Authors: González Hodar, Jaime

Contributors: Ferrer Ortiz, Juan Carlos, Pontificia Universidad Católica de Chile. Escuela de Ingeniería

Subject Terms: Toma de decisiones - Modelos matemáticos, Transferencia de pacientes, Medicina y salud, Procesos de Markov

File Description: xi, 56 hojas; application/pdf

9

Dissertation/ Thesis

Empirical basis for the development of adaptative interfaces : behavioral and neurophysiological evidences of decision-making and expertise development in a sequencial choice scenario

Authors: Moenne Vargas, Cristóbal Matías

Contributors: Mery Quiroz, Domingo, Cosmelli, Diego, Pontificia Universidad Católica de Chile. Escuela de Ingeniería

Subject Terms: Toma de decisiones - Modelos matemáticos, Ingeniería, Interfaces de usuarios (Sistemas computacionales), Procesos de Markov

File Description: xvi, 91 hojas; application/pdf

10

Dissertation/ Thesis

Entry-timing advantages in renewable natural resources industries

Authors: Sepúlveda Vargas, José Ignacio

Contributors: Mac Cawley Vergara, Alejandro Francisco, Pontificia Universidad Católica de Chile. Escuela de Ingeniería

Subject Terms: Toma de decisiones - Modelos matemáticos, Economía, Recursos naturales renovables - Aspectos económicos - Modelos matemáticos

File Description: ix, 59 hojas; application/pdf

11

Academic Journal

Óptimo económico de máquinas y equipos Parte I. Delimitación del problema

Authors: Poveda Ramos, Gabriel

Source: Revista Facultad de Ingeniería Universidad de Antioquia, Iss 27 (2002)
Poveda-Ramos, G. (2002). Óptimo económico de máquinas y equipos Parte I. Delimitación del problema. Revista Facultad de Ingeniería Universidad de Antioquia, (27), 151-163.

Subject Terms: Toma de decisiones - Modelos matemáticos, vida útil, Bienes de capital, Ingeniería económica, Depreciación, Engineering (General). Civil engineering (General), Vida útil, Productividad industrial, valor de salvamento, TA1-2040, Ingeniería industrial, inversión, depreciación, bienes de capital

Access URL: https://doaj.org/article/ce874bc8c09b4fe0a58b3f3c5c2ab94b
https://repositorio.accefyn.org.co/handle/001/1622

View record at OpenAIRE

12

Academic Journal

Development of a decision support system for a transport company. An application for the Revenue Management and non-linear programming ; Desarrollo de un sistema de soporte de decisiones para una empresa de transporte. Una aplicación del revenue management y de la programación no lineal

Authors: Mendoza-Quispe, José Antonio

Source: Interfases; No. 009 (2016); 31-66 ; Interfases; Núm. 009 (2016); 31-66 ; Interfases; n. 009 (2016); 31-66 ; 1993-4912 ; 10.26439/interfases2016.n009

Subject Terms: decision support system, non-linear programming, revenue management, empresas de transporte, programación no lineal, toma de decisiones-modelos matemáticos, viajes-programacion, precios-control

File Description: application/pdf

Relation: https://revistas.ulima.edu.pe/index.php/Interfases/article/view/1240/1200; https://revistas.ulima.edu.pe/index.php/Interfases/article/view/1240

Availability: https://revistas.ulima.edu.pe/index.php/Interfases/article/view/1240
https://doi.org/10.26439/interfases2016.n009.1240

View record from BASE

13

Report

Bayesian Search Under Dynamic Disaster Scenarios

Authors: Betancourt Reyes, José Daniel

Contributors: Galindo Pacheco, Gina

Subject Terms: Teoría bayesiana de decisiones estadísticas, Optimización matemática, Toma de decisiones -- Modelos matemáticos

File Description: application/pdf

Relation: http://hdl.handle.net/10584/8965

Availability: http://hdl.handle.net/10584/8965

View record from BASE

14

Book

Optimización

Authors: Jiménez Lozano, Guillermo

Subject Terms: 51 Matemáticas / Mathematics, 65 Gerencia y servicios auxiliares / Management and public relations, Investigación de operaciones, Concavidad y convexidad, Programación lineal, Teoría de decisiones, Modelo de redes, Programación no lineal, Programación dinámica, Modelo de inventarios, Teoría de colas, Optimización estocástica, Análisis de Markov, Optimización matemática, Toma de decisiones - modelos matemáticos

File Description: application/pdf

Relation: Universidad Nacional de Colombia Sede Manizales Facultad de Administración Departamento de Informática y Computación; Departamento de Informática y Computación; Jiménez Lozano, Guillermo (2009) Optimización. Universidad Nacional de Colombia - Sede Manizales, Manizales, Colombia. ISBN 978-958-8280-22-6; https://repositorio.unal.edu.co/handle/unal/8415; http://bdigital.unal.edu.co/5031/

Availability: https://repositorio.unal.edu.co/handle/unal/8415
http://bdigital.unal.edu.co/5031/

View record from BASE

15

Conference

Modelación de una situación empresarial para la enseñanza de simulación discreta

Authors: Carmona González, Guillermo L., Montoya Agudelo, Juan Sebastián, Cano Escobar, María Adelaida, Álvarez Zapata, Daniela, Rodríguez Betancur, María Antonia

Contributors: Universidad EAFIT. Departamento de Ingeniería de Producción, Gestión de Producción y Logística

File Description: application/pdf

Access URL: http://hdl.handle.net/10784/5040

16

Dissertation/ Thesis

Patrones de relación entre información biológica e información física y socioeconómica : cuenca alta del Río Otún, Risaralda Colombia

Authors: Coronel Picón, Yulieth Rossio

Contributors: Obregón Neira, Nelson

Subject Terms: Hidrología, Toma de decisiones - Modelos matemáticos, Maestría en hidrosistemas - Tesis y disertaciones académicas, Colombia) - Modelos matemáticos, Cuencas hidrográficas - Río Otún (Risaralda, Mamíferos - Hábitat - Río Otún (Risaralda, Colombia)

File Description: PDF; application/pdf

17

Report

Diseño de un modelo dinámico de asignación de recursos públicos para la formación de capital humano demandado por sectores estratégicos en crecimiento

Authors: García Pertuz, Claudia Marcela

Contributors: Santander Mercado, Alcides Ricardo

Subject Terms: Toma de decisiones -- Modelos matemáticos, Investigación operacional

File Description: application/pdf

Relation: http://hdl.handle.net/10584/8970

Availability: http://hdl.handle.net/10584/8970

View record from BASE

18

Dissertation/ Thesis

Desempeño sostenible en el diseño y gestión de cadenas de abastecimiento bajo condiciones de incertidumbre. Aplicación a la producción de biocombustibles a partir de caña de azúcar ; Designing supply chain under uncertain conditions from sustainable performance perspective. An application at sugarcane based biofuel production

Authors: Carvajal Beltrán, Jimmy Alexander

Contributors: Sarache, William, Costa, Yasel, Innovación y desarrollo Tecnológico

Subject Terms: 620 - Ingeniería y operaciones afines, Cadenas de abastecimiento -- Metodología -- Toma de decisiones -- Modelos matemáticos, Cadenas de abastecimiento, programación estocástica de dos etapas, Cadenas de Markov, Optimización multi-objetivo, Biocombustibles, Caña de azúcar, Desempeño sostenible, Distribución justa del beneficio, Supply chains, Two-stage stochastic programming, Markov chains, Multi-objective optimization, Biofuels, sugarcane, Sustainable performance, Fair profit distribution

File Description: xvi, 174 páginas; application/pdf

Relation: Abdul-Jalbar, B., Colebrook, M., Dorta-Guerra, R., & Gutiérrez, J. M. (2016). Centralized and decentralized inventory policies for a single-vendor two-buyer system with permissible delay in payments. Computers & Operations Research, 74, 187-195. https://doi.org/10.1016/j.cor.2016.04.030; Adam, N.-R. B., Dauhoo, M. Z., Khoodaruth, A. A. H., & Elahee, M. K. (2016). A two-stage stochastic programming optimisation for sugar-ethanol-electricity production from sugarcane: A case study of Mauritius. International Journal of Mathematical Modelling and Numerical Optimisation, 7(1), 20-32.; Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European Journal of Operational Research, 196(1), 1-20. https://doi.org/10.1016/j.ejor.2008.02.014; Ahumada, O., & Villalobos, J. R. (2011). A tactical model for planning the production and distribution of fresh produce. Annals of Operations Research, 190(1), 339-358. https://doi.org/10.1007/s10479-009-0614-4; Alonso Pippo, W., Luengo, C. A., Alonsoamador Morales Alberteris, L., Garzone, P., & Cornacchia, G. (2011). Energy Recovery from Sugarcane-Trash in the Light of 2nd Generation Biofuel. Part 2: Socio-Economic Aspects and Techno-Economic Analysis. Waste and Biomass Valorization, 2(3), 257-266. https://doi.org/10.1007/s12649-011-9069-3; Alonso-Pippo, W., Luengo, C. A., Alonsoamador Morales Alberteris, L., García del Pino, G., & Duvoisin, S. (2013). Practical implementation of liquid biofuels: The transferability of the Brazilian experiences. Energy Policy, 60, 70-80. https://doi.org/10.1016/j.enpol.2013.04.038; Álvarez-Rodríguez, D. A., Normey-Rico, J. E., & Flesch, R. C. C. (2017). Model predictive control for inventory management in biomass manufacturing supply chains. International Journal of Production Research, 55(12), 3596-3608. https://doi.org/10.1080/00207543.2017.1315191; Amu, L.G., Garcia, J.A., Galvis , D.E., & Rubiano, O. (2013). Optimisation of harvest resources in a colombian sugar mill by use of simulation models. Proceedings of the International Society of Sugar Cane Technologists, 28, 2042-2049. http://bonsucro.com/site/wp-content/uploads/2013/02/ISSCT-Development-Bonsucro-Standard-Viart-N-and-Rein-P-2013.pdf; Asocaña. (2017). Más que azúcar, una fuente de energía renovable para el país. https://www.asocana.org/documentos/562017-ED2FFB51-00FF00,000A000,878787,C3C3C3,0F0F0F,B4B4B4,FF00FF,2D2D2D.pdf; Baghalian, A., Rezapour, S., & Farahani, R. Z. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research, 227(1), 199-215. https://doi.org/10.1016/j.ejor.2012.12.017; Ballou, R. H. (2007). Business logistics/supply chain management: Planning, organizing, and controlling the supply chain. Pearson Education India.; Banco Interamericano de Desarrollo (BID). (2012). “Evaluación del ciclo de vida de la cadena de producción de biocombustibles en Colombia”.; Barbosa-Póvoa, A. P., da Silva, C., & Carvalho, A. (2017). Opportunities and Challenges in Sustainable Supply Chain: An Operations Research Perspective. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2017.10.036; Barrett, C. B. (2021). Overcoming Global Food Security Challenges through Science and Solidarity. American Journal of Agricultural Economics, 103(2), 422-447. https://doi.org/10.1111/ajae.12160; Behzadi, G., O’Sullivan, M. J., Olsen, T. L., & Zhang, A. (2017). Agribusiness Supply Chain Risk Management: A Review of Quantitative Decision Models. Omega. https://doi.org/10.1016/j.omega.2017.07.005; Bekkering, J., Broekhuis, A. A., & van Gemert, W. J. T. (2010). Optimisation of a green gas supply chain – A review. Bioresource Technology, 101(2), 450-456. https://doi.org/10.1016/j.biortech.2009.08.106; Benoît, C., Norris, G. A., Valdivia, S., Ciroth, A., Moberg, A., Bos, U., Prakash, S., Ugaya, C., & Beck, T. (2010). The guidelines for social life cycle assessment of products: Just in time! The International Journal of Life Cycle Assessment, 15(2), 156-163. https://doi.org/10.1007/s11367-009-0147-8; Bertsimas, D., Farias, V. F., & Trichakis, N. (2011). The Price of Fairness. Operations Research, 59(1), 17-31. https://doi.org/10.1287/opre.1100.0865; Bezuidenhout, C. N., & Singels, A. (2007a). Operational forecasting of South African sugarcane production: Part 1 – System description. Agricultural Systems, 92(1), 23-38. https://doi.org/10.1016/j.agsy.2006.02.001; Bezuidenhout, C. N., & Singels, A. (2007b). Operational forecasting of South African sugarcane production: Part 2 – System evaluation. Agricultural Systems, 92(1), 39-51. https://doi.org/10.1016/j.agsy.2006.03.002; Birge, J. R., & Louveaux, F. (2011). Introduction to stochastic programming. Springer Science & Business Media.; Blanco, V., Carpente, L., Hinojosa, Y., & Puerto, J. (2010). Planning for Agricultural Forage Harvesters and Trucks: Model, Heuristics, and Case Study. Networks and Spatial Economics, 10(3), 321-343. https://doi.org/10.1007/s11067-009-9120-0; Bocca, F. F., & Rodrigues, L. H. A. (2016). The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling. Computers and Electronics in Agriculture, 128, 67-76. https://doi.org/10.1016/j.compag.2016.08.015; Bojesen, M., Skov-Petersen, H., & Gylling, M. (2015). Forecasting the potential of Danish biogas production – Spatial representation of Markov chains. Biomass and Bioenergy, 81, 462-472. https://doi.org/10.1016/j.biombioe.2015.07.030; Borgonovo, E., Gatti, S., & Peccati, L. (2010). What drives value creation in investment projects? An application of sensitivity analysis to project finance transactions. European Journal of Operational Research, 205(1), 227-236. https://doi.org/10.1016/j.ejor.2009.12.006; Borodin, 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.2016.03.057; Bot, P., van Donk, D. P., Pennink, B., & Simatupang, T. M. (2015). Uncertainties in the Bidirectional Biodiesel Supply Chain. Journal of Cleaner Production, 95, 174-183. https://doi.org/10.1016/j.jclepro.2015.02.064; Branco, J. E. H., Branco, D. H., de Aguiar, E. M., Caixeta Filho, J. V., & Rodrigues, L. (2019). Study of optimal locations for new sugarcane mills in Brazil: Application of a MINLP network equilibrium model. Biomass and Bioenergy, 127, 105249.; Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), 299-312. https://doi.org/10.1016/j.ejor.2013.09.032; Bubicz, M. E., Barbosa-Póvoa, A. P. F. D., & Carvalho, A. (2019). Incorporating social aspects in sustainable supply chains: Trends and future directions. Journal of Cleaner Production, 237, 117500. https://doi.org/10.1016/j.jclepro.2019.06.331; Budzianowski, W. M., & Postawa, K. (2016). Total Chain Integration of sustainable biorefinery systems. Applied Energy, 184, 1432-1446. https://doi.org/10.1016/j.apenergy.2016.06.050; Caixeta-Filho, J. V. (2006). Orange harvesting scheduling management: A case study. Journal of the Operational Research Society, 57(6), 637-642. https://doi.org/10.1057/palgrave.jors.2602041; Campos-Guzmán, V., García-Cáscales, M. S., Espinosa, N., & Urbina, A. (2019). Life Cycle Analysis with Multi-Criteria Decision Making: A review of approaches for the sustainability evaluation of renewable energy technologies. Renewable and Sustainable Energy Reviews, 104, 343-366. https://doi.org/10.1016/j.rser.2019.01.031; Cardoso, T. F., Chagas, M. F., Rivera, E. C., Cavalett, O., Morais, E. R., Geraldo, V. C., Braunbeck, O., da Cunha, M. P., Cortez, L. A. B., & Bonomi, A. (2015). A vertical integration simplified model for straw recovery as feedstock in sugarcane biorefineries. Biomass and Bioenergy, 81, 216-223. https://doi.org/10.1016/j.biombioe.2015.07.003; Carvajal, J., Sarache, W., & Costa, Y. (2019). Addressing a robust decision in the sugarcane supply chain: Introduction of a new agricultural investment project in Colombia. Computers and Electronics in Agriculture, 157, 77-89. https://doi.org/10.1016/j.compag.2018.12.030; Castaño, F., Rossi, A., Sevaux, M., & Velasco, N. (2014). A column generation approach to extend lifetime in wireless sensor networks with coverage and connectivity constraints. Computers & Operations Research, 52, 220-230.; Castaño, F., Velasco, N., & Carvajal, J. (2019). Content-Based Conference Scheduling Optimization. IEEE Latin America Transactions, 17(04), 597-606.; Chen, Y., Wang, S., Yao, J., Li, Y., & Yang, S. (2018). Socially responsible supplier selection and sustainable supply chain development: A combined approach of total interpretive structural modeling and fuzzy analytic network process. Business Strategy and the Environment, 27(8), 1708-1719. https://doi.org/10.1002/bse.2236; Colin, E. C. (2009). Mathematical programming accelerates implementation of agro-industrial sugarcane complex. European Journal of Operational Research, 199(1), 232-235. https://doi.org/10.1016/j.ejor.2008.11.016; Congreso de Colombia. (2014). LEY 1715 DE 2014 Diario Oficial No. 49.150. Bogotá, DC: Imprenta Nacional. Retrieved, 9, 2017.; Congreso de Colombia. (2019). LEY 1955 DE 2019, Plan Nacional de Desarrollo 2018-2022. “Pacto por Colombia, Pacto por la Equidad”. http://www.suin-juriscol.gov.co/viewDocument.asp?ruta=Leyes/30036488; Costa, A. M., dos Santos, L. M. R., Alem, D. J., & Santos, R. H. S. (2011). Sustainable vegetable crop supply problem with perishable stocks. Annals of Operations Research. https://doi.org/10.1007/s10479-010-0830-y; Council of Supply Chain Management Professionals, CSCMP. (2017). CSCMP Supply Chain Management Definitions and Glossary.; da Silva, A. F., & Marins, F. A. S. (2014). A Fuzzy Goal Programming model for solving aggregate production-planning problems under uncertainty: A case study in a Brazilian sugar mill. Energy Economics, 45, 196-204. https://doi.org/10.1016/j.eneco.2014.07.005; da Silva, A. F., Marins, F. A. S., & Dias, E. X. (2015). Addressing uncertainty in sugarcane harvest planning through a revised multi-choice goal programming model. Applied Mathematical Modelling, 39(18), 5540-5558. https://doi.org/10.1016/j.apm.2015.01.007; Darby-Dowman, K., Barker, S., Audsley, E., & Parsons, D. (2000). A two-stage stochastic programming with recourse model for determining robust planting plans in horticulture. Journal of the Operational Research Society, 51(1), 83-89. https://doi.org/10.1057/palgrave.jors.2600858; Das, R., Shaw, K., & Irfan, Mohd. (2020). Supply chain network design considering carbon footprint, water footprint, supplier’s social risk, solid waste, and service level under the uncertain condition. Clean Technologies and Environmental Policy, 22(2), 337-370. https://doi.org/10.1007/s10098-019-01785-y; Davis, K. F., Gephart, J. A., Emery, K. A., Leach, A. M., Galloway, J. N., & D’Odorico, P. (2016). Meeting future food demand with current agricultural resources. Global Environmental Change, 39, 125-132. https://doi.org/10.1016/j.gloenvcha.2016.05.004; De Oliveira Florentino, H., De Lima, A. D., De Carvalho, L. R., Balbo, A. R., & Homem, T. P. D. (2011). Multiobjective 0-1 integer programming for the use of sugarcane residual biomass in energy cogeneration. International Transactions in Operational Research, 18(5), 605-615. https://doi.org/10.1111/j.1475-3995.2011.00818.x; de Oliveira Florentino, H., & Pato, M. V. (2014). A bi-objective genetic approach for the selection of sugarcane varieties to comply with environmental and economic requirements. Journal of the Operational Research Society, 65(6), 842-854. https://doi.org/10.1057/jors.2013.21; de Oliveira Florentino, H., & Pereira Sartori, M. M. (2003). Game theory in sugarcane crop residue and available energy optimization. Biomass and Bioenergy, 25(1), 29-34. https://doi.org/10.1016/S0961-9534(02)00189-7; de Souza Dias, M. O., Maciel Filho, R., Mantelatto, P. E., Cavalett, O., Rossell, C. E. V., Bonomi, A., & Leal, M. R. L. V. (2015). Sugarcane processing for ethanol and sugar in Brazil. Environmental Development, 15, 35-51.; Departamento Nacional de Planeación. (2008). Lineamientos de politica para promover la produccion sostenible de biocombustibles en Colombia (Documento CONPES 3510). DNP Bogotá, Colombia.; dos Reis Ferreira, R. A., da Silva Meireles, C., Assunção, R. M. N., Barrozo, M. A. S., & Soares, R. R. (2020). Optimization of the oxidative fast pyrolysis process of sugarcane straw by TGA and DSC analyses. Biomass and Bioenergy, 134, 105456.; Du, C., Dias, L. C., & Freire, F. (2019). Robust multi-criteria weighting in comparative LCA and S-LCA: A case study of sugarcane production in Brazil. Journal of Cleaner Production, 218, 708-717. https://doi.org/10.1016/j.jclepro.2019.02.035; Dunford, R. W., Marti, C. E., & Mittelhammer, R. C. (1985). A Case Study of Rural Land Prices at the Urban Fringe Including Subjective Buyer Expectations. Land Economics, 61(1), 10. https://doi.org/10.2307/3146135; Ebadian, M., van Dyk, S., McMillan, J. D., & Saddler, J. (2020). Biofuels policies that have encouraged their production and use: An international perspective. Energy Policy, 147, 111906. https://doi.org/10.1016/j.enpol.2020.111906; Eizenberg, E., & Jabareen, Y. (2017). Social Sustainability: A New Conceptual Framework. Sustainability, 9(1), 68. https://doi.org/10.3390/su9010068; El Espectador. (2021, septiembre 20). ELESPECTADOR.COM. ELESPECTADOR.COM. https://www.elespectador.com/judicial/megaproyecto-de-produccion-de-etanol-el-alcaravan-fue-un-fracaso-contraloria/; Elkington, J. (1997). Cannibals with forks. The triple bottom line of 21st century, 73.; Eskandarpour, M., Dejax, P., Miemczyk, J., & Péton, O. (2015). Sustainable supply chain network design: An optimization-oriented review. Omega, 54, 11-32. https://doi.org/10.1016/j.omega.2015.01.006; Espinoza-Pérez, A. T., Camargo, M., Narváez-Rincón, P. C., & Alfaro-Marchant, M. (2017). Key challenges and requirements for sustainable and industrialized biorefinery supply chain design and management: A bibliographic analysis. Renewable and Sustainable Energy Reviews, 69, 350-359. https://doi.org/10.1016/j.rser.2016.11.084; Esteso, A., Alemany, M. M. E., & Ortiz, A. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research, 56(13), 4418-4446. https://doi.org/10.1080/00207543.2018.1447706; Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101-114. https://doi.org/10.1016/j.ijpe.2015.01.003; Farahani, R. Z., Hekmatfar, M., Fahimnia, B., & Kazemzadeh, N. (2014). Hierarchical facility location problem: Models, classifications, techniques, and applications. Computers & Industrial Engineering, 68, 104-117. https://doi.org/10.1016/j.cie.2013.12.005; Faria, L. F. F., Silva, J. E. A. R., Faria, L. F. F., & Silva, J. E. A. R. (2015). Effects of maintenance management procedures in sugarcane mechanic harvesting system equipment. Engenharia Agrícola, 35(6), 1187-1197. https://doi.org/10.1590/1809-4430-Eng.Agric.v35n6p1187-1197/2015; Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Mirjalili, S. (2018). Multi-objective stochastic closed-loop supply chain network design with social considerations. Applied Soft Computing, 71, 505-525. https://doi.org/10.1016/j.asoc.2018.07.025; Fattahi, M., & Govindan, K. (2018). A multi-stage stochastic program for the sustainable design of biofuel supply chain networks under biomass supply uncertainty and disruption risk: A real-life case study. Transportation Research Part E: Logistics and Transportation Review, 118, 534-567. https://doi.org/10.1016/j.tre.2018.08.008; Fedebiocombustibles. (2021, enero 1). Federación Nacional de Biocombustibles de Colombia, Marco Normativo de los biocombustibles en Colombia. fedebiocombustibles.com. http://www.fedebiocombustibles.com/v3/estadistica-mostrar_info-titulo-Alcohol_Carburante_(Etanol).htm; Florentino, H. de O., Irawan, C., Aliano, A. F., Jones, D. F., Cantane, D. R., & Nervis, J. J. (2018). A multiple objective methodology for sugarcane harvest management with varying maturation periods. Annals of Operations Research, 267(1-2), 153-177. https://doi.org/10.1007/s10479-017-2568-2; Florentino, H. de O., Jones, D. F., Irawan, C. A., Ouelhadj, D., Khosravi, B., & Cantane, D. R. (2020). An optimization model for combined selecting, planting and harvesting sugarcane varieties. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03610-y; Florio, M., & Colautti, S. (2005). A logistic growth theory of public expenditures: A study of five countries over 100 years. Public Choice, 122(3-4), 355-393. https://doi.org/10.1007/s11127-005-3900-y; Furlan, F. F., Costa, C. B. B., de Castro Fonseca, G., de Pelegrini Soares, R., Secchi, A. R., da Cruz, A. J. G., & de Campos Giordano, R. (2012). Assessing the production of first and second generation bioethanol from sugarcane through the integration of global optimization and process detailed modeling. Computers & Chemical Engineering, 43, 1-9.; Gao, J., & You, F. (2019). A stochastic game theoretic framework for decentralized optimization of multi-stakeholder supply chains under uncertainty. Computers & Chemical Engineering, 122, 31-46. https://doi.org/10.1016/j.compchemeng.2018.05.016; Gatti, S. (2013). Project finance in theory and practice: Designing, structuring, and financing private and public projects. Academic Press.; Ghaderi, H., Pishvaee, M. S., & Moini, A. (2016). Biomass supply chain network design: An optimization-oriented review and analysis. Industrial Crops and Products, 94, 972-1000. https://doi.org/10.1016/j.indcrop.2016.09.027; Gheewala, S., Silalertruksa, T., Nilsalab, P., Mungkung, R., Perret, S., & Chaiyawannakarn, N. (2014). Water Footprint and Impact of Water Consumption for Food, Feed, Fuel Crops Production in Thailand. Water, 6(6), 1698-1718. https://doi.org/10.3390/w6061698; Giannakis, M., & Papadopoulos, T. (2016). Supply chain sustainability: A risk management approach. International Journal of Production Economics, 171, 455-470. https://doi.org/10.1016/j.ijpe.2015.06.032; Gilani, H., & Sahebi, H. (2020). A multi-objective robust optimization model to design sustainable sugarcane-to-biofuel supply network: The case of study. Biomass Conversion and Biorefinery. https://doi.org/10.1007/s13399-020-00639-8; Gnansounou, E., Pachón, E. R., Sinsin, B., Teka, O., Togbé, E., & Mahamane, A. (2020). Using agricultural residues for sustainable transportation biofuels in 2050: Case of West Africa. Bioresource Technology, 305, 123080. https://doi.org/10.1016/j.biortech.2020.123080; Gobierno Digital Colombia. (2018). Datos abiertos Ministerio de Minas y energia Colombia. https://www.datos.gov.co/Minas-y-Energ-a/Tarifas-aplicadas-de-Gas-Natural/ek3f-5wn4/data; Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operational Research, 263(1), 108-141. https://doi.org/10.1016/j.ejor.2017.04.009; Govindan, K., Shaw, M., & Majumdar, A. (2020). Social Sustainability Tensions in Multi-tier Supply Chain: A Systematic Literature Review towards Conceptual Framework Development. Journal of Cleaner Production, 123075. https://doi.org/10.1016/j.jclepro.2020.123075; Grimsey, D., & Lewis, M. (2007). Public private partnerships: The worldwide revolution in infrastructure provision and project finance. Edward Elgar Publishing.; Grunow, M., Günther, H.-O., & Westinner, R. (2007). Supply optimization for the production of raw sugar. International Journal of Production Economics, 110(1-2), 224-239. https://doi.org/10.1016/j.ijpe.2007.02.019; Guo, M., van Dam, K. H., Touhami, N. O., Nguyen, R., Delval, F., Jamieson, C., & Shah, N. (2020). Multi-level system modelling of the resource-food-bioenergy nexus in the global south. Energy, 197, 117196. https://doi.org/10.1016/j.energy.2020.117196; Haberl, H., Wackernagel, M., & Wrbka, T. (2004). Land use and sustainability indicators. An introduction. Land Use Policy, 21(3), 193-198. https://doi.org/10.1016/j.landusepol.2003.10.004; Hahn, M. H., & Ribeiro, R. V. (1999). Heuristic guided simulator for the operational planning of the transport of sugar cane. Journal of the Operational Research Society, 50(5), 451-459.; Haj Hasan, A., & Avami, A. (2018). Comparative assessment of bioethanol supply chain: Insights from Iran. Biofuels, 1-9. https://doi.org/10.1080/17597269.2018.1496385; Hall, J., Matos, S., & Silvestre, B. (2012). Understanding why firms should invest in sustainable supply chains: A complexity approach. International Journal of Production Research, 50(5), 1332-1348. https://doi.org/10.1080/00207543.2011.571930; Hasani, A., & Khosrojerdi, A. (2016). Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study. Transportation Research Part E: Logistics and Transportation Review, 87, 20-52. https://doi.org/10.1016/j.tre.2015.12.009; Henao, R., Sarache, W., & Gómez, I. (2019). Lean manufacturing and sustainable performance: Trends and future challenges. Journal of Cleaner Production, 208, 99-116. https://doi.org/10.1016/j.jclepro.2018.10.116; Henao, R., Sarache, W., & Gomez, I. (2021). A social performance metrics framework for sustainable manufacturing. International Journal of Industrial and Systems Engineering, 38(2), 167-197.; Higgins, A. (2006). Scheduling of road vehicles in sugarcane transport: A case study at an Australian sugar mill. European Journal of Operational Research, 170(3), 987-1000. https://doi.org/10.1016/j.ejor.2004.07.055; Higgins, A., Antony, G., Sandell, G., Davies, I., Prestwidge, D., & Andrew, B. (2004). A framework for integrating a complex harvesting and transport system for sugar production. Agricultural Systems, 82(2), 99-115. https://doi.org/10.1016/j.agsy.2003.12.004; Higgins, A., & Davies, I. (2005). A simulation model for capacity planning in sugarcane transport. Computers and Electronics in Agriculture, 47(2), 85-102. https://doi.org/10.1016/j.compag.2004.10.006; Higgins, A. J. (1999). Optimizing cane supply decisions within a sugar mill region. Journal of Scheduling, 2(5), 229-244.; Higgins, A. J. (2002). Australian Sugar Mills Optimize Harvester Rosters to Improve Production. Interfaces, 32(3), 15-25. https://doi.org/10.1287/inte.32.3.15.41; Higgins, A. J., & Laredo, L. A. (2006). Improving harvesting and transport planning within a sugar value chain. Journal of the Operational Research Society, 57(4), 367-376. https://doi.org/10.1057/palgrave.jors.2602024; Higgins, A. J., & Muchow, R. C. (2003). Assessing the potential benefits of alternative cane supply arrangements in the Australian sugar industry. Agricultural Systems, 76(2), 623-638.; Higgins, A., Muchow, R. C., Rudd, A. V., & Ford, A. W. (1998). Optimising harvest date in sugar production: A case study for the Mossman mill region in Australia I. Development of operations research model and solution. Field Crops Research, 57, 153-162.; Higgins, A., Thorburn, P., Archer, A., & Jakku, E. (2007). Opportunities for value chain research in sugar industries. Agricultural Systems, 94(3), 611-621. https://doi.org/10.1016/j.agsy.2007.02.011; Hua, Z., Jun, L., Zhaonian, Y., Sanji, G., Yingying, Y., & Zhaoli, L. (2013). Agronomic techniques to sugarcane mechanical seeding [J]. Journal of Chinese Agricultural Mechanization, 1, 020.; Huijbregts, M. A. J., Steinmann, Z. J. N., Elshout, P. M. F., Stam, G., Verones, F., Vieira, M., Zijp, M., Hollander, A., & van Zelm, R. (2017). ReCiPe2016: A harmonised life cycle impact assessment method at midpoint and endpoint level. The International Journal of Life Cycle Assessment, 22(2), 138-147. https://doi.org/10.1007/s11367-016-1246-y; Illukpitiya, P., Yanagida, J. F., Ogoshi, R., & Uehara, G. (2013). Sugar-ethanol-electricity co-generation in Hawai’i: An application of linear programming (LP) for optimizing strategies. Biomass and Bioenergy, 48, 203-212. https://doi.org/10.1016/j.biombioe.2012.11.003; J. W. Mishoe, J. W. Jones, & G. J. Gascho. (1979). Harvesting Scheduling of Sugarcane for Optimum Biomass Production. Transactions of the ASAE, 22(6), 1299-1304. https://doi.org/10.13031/2013.35202; Jaehn, F. (2016). Sustainable Operations. European Journal of Operational Research, 253(2), 243-264. https://doi.org/10.1016/j.ejor.2016.02.046; Jahani, H., Abbasi, B., & Talluri, S. (2019). Supply Chain Network Redesign: A Technical Note on Optimising Financial Performance. Decision Sciences, deci.12374. https://doi.org/10.1111/deci.12374; Jena, S. D., & Poggi, M. (2013). Harvest planning in the Brazilian sugar cane industry via mixed integer programming. European Journal of Operational Research, 230(2), 374-384. https://doi.org/10.1016/j.ejor.2013.04.011; Jiao, Z., Higgins, A. J., & Prestwidge, D. B. (2005). An integrated statistical and optimisation approach to increasing sugar production within a mill region. Computers and Electronics in Agriculture, 48(2), 170-181. https://doi.org/10.1016/j.compag.2005.03.004; Jin, S., Jeong, S., & Kim, K. (2017). A Linkage Model of Supply Chain Operation and Financial Performance for Economic Sustainability of Firm. Sustainability, 9(1), 139. https://doi.org/10.3390/su9010139; Joelsson, E., Erdei, B., Galbe, M., & Wallberg, O. (2016). Techno-economic evaluation of integrated first- and second-generation ethanol production from grain and straw. Biotechnology for Biofuels, 9, 1. https://doi.org/10.1186/s13068-015-0423-8; Jonker, J. G. G., Junginger, H. M., Verstegen, J. A., Lin, T., Rodríguez, L. F., Ting, K. C., Faaij, A. P. C., & van der Hilst, F. (2016). Supply chain optimization of sugarcane first generation and eucalyptus second generation ethanol production in Brazil. Applied Energy, 173, 494-510. https://doi.org/10.1016/j.apenergy.2016.04.069; Junqueira, R. de Á. R., & Morabito, R. (2019). Modeling and solving a sugarcane harvest front scheduling problem. International Journal of Production Economics, 213, 150-160.; Karp, S. G., Medina, J. D. C., Letti, L. A. J., Woiciechowski, A. L., de Carvalho, J. C., Schmitt, C. C., de Oliveira Penha, R., Kumlehn, G. S., & Soccol, C. R. (2021). Bioeconomy and biofuels: The case of sugarcane ethanol in Brazil. Biofuels, Bioproducts and Biorefining, n/a(n/a). https://doi.org/10.1002/bbb.2195; Khamjan, W., Khamjan, S., & Pathumnakul, S. (2013). Determination of the locations and capacities of sugar cane loading stations in Thailand. Computers & Industrial Engineering, 66(4), 663-674. https://doi.org/10.1016/j.cie.2013.09.006; Khan, S. A. R., Yu, Z., Golpira, H., Sharif, A., & Mardani, A. (2021). A state-of-the-art review and meta-analysis on sustainable supply chain management: Future research directions. Journal of Cleaner Production, 278, 123357. https://doi.org/10.1016/j.jclepro.2020.123357; Khatiwada, D., Leduc, S., Silveira, S., & McCallum, I. (2016). Optimizing ethanol and bioelectricity production in sugarcane biorefineries in Brazil. Renewable Energy, 85, 371-386. https://doi.org/10.1016/j.renene.2015.06.009; Kittilertpaisan, K., & Pathumnakul, S. (2017). Integrating a multiple crop year routing design for sugarcane harvesters to plant a new crop. Computers and Electronics in Agriculture, 136, 58-70. https://doi.org/10.1016/j.compag.2017.03.001; Kostin, A. M., Guillén-Gosálbez, G., Mele, F. D., Bagajewicz, M. J., & Jiménez, L. (2010). Integrating pricing policies in the strategic planning of supply chains: A case study of the sugar cane industry in Argentina. En S. Pierucci & G. B. Ferraris (Eds.), Computer Aided Chemical Engineering (Vol. 28, pp. 103-108). Elsevier. https://doi.org/10.1016/S1570-7946(10)28018-5; Kostin, A. M., Guillén-Gosálbez, G., Mele, F. D., Bagajewicz, M. J., & Jiménez, L. (2011). A novel rolling horizon strategy for the strategic planning of supply chains. Application to the sugar cane industry of Argentina. Computers & Chemical Engineering, 35(11), 2540-2563. https://doi.org/10.1016/j.compchemeng.2011.04.006; Kostin, A. M., Guillén-Gosálbez, G., Mele, F. D., Bagajewicz, M. J., & Jiménez, L. (2012). Design and planning of infrastructures for bioethanol and sugar production under demand uncertainty. Chemical Engineering Research and Design, 90(3), 359-376. https://doi.org/10.1016/j.cherd.2011.07.013; Kravanja, Z., & Čuček, L. (2013). Multi-objective optimisation for generating sustainable solutions considering total effects on the environment. Applied Energy, 101, 67-80. https://doi.org/10.1016/j.apenergy.2012.04.025; Kulkarni, V. G. (2016). Modeling and analysis of stochastic systems. Chapman and Hall/CRC.; Kumar, N., Patel, S. S., Chalodia, A. L., Vadaviya, O. U., Pandya, H. R., Pisal, R. R., Dakhore, K. K., & Patel, M. L. (2015). Markov chain and incomplete Gamma distribution analysis of weekly rainfall over Navsari region of south Gujarat. Mausam, 10.; Kusumastuti, R. D., Donk, D. P. van, & Teunter, R. (2016). Crop-related harvesting and processing planning: A review. International Journal of Production Economics, 174, 76-92. https://doi.org/10.1016/j.ijpe.2016.01.010; Le Gal, P.-Y., Le Masson, J., Bezuidenhout, C. N., & Lagrange, L. F. (2009). Coupled modelling of sugarcane supply planning and logistics as a management tool. Computers and Electronics in Agriculture, 68(2), 168-177. https://doi.org/10.1016/j.compag.2009.05.006; Le Gal, P.-Y., Lyne, P. W. L., Meyer, E., & Soler, L.-G. (2008). Impact of sugarcane supply scheduling on mill sugar production: A South African case study. Agricultural Systems, 96(1), 64-74. https://doi.org/10.1016/j.agsy.2007.05.006; Leduc, S., Starfelt, F., Dotzauer, E., Kindermann, G., McCallum, I., Obersteiner, M., & Lundgren, J. (2010). Optimal location of lignocellulosic ethanol refineries with polygeneration in Sweden. Energy, 35(6), 2709-2716. https://doi.org/10.1016/j.energy.2009.07.018; Lejars, C., Le Gal, P.-Y., & Auzoux, S. (2008). A decision support approach for cane supply management within a sugar mill area. Computers and Electronics in Agriculture, 60(2), 239-249. https://doi.org/10.1016/j.compag.2007.08.008; Liobikiene, G., Balezentis, T., Streimikiene, D., & Chen, X. (2019). Evaluation of bioeconomy in the context of strong sustainability. Sustainable Development, 27(5), 955-964. https://doi.org/10.1002/sd.1984; Liu, L., Parlar, M., & Zhu, S. X. (2007). Pricing and Lead Time Decisions in Decentralized Supply Chains. Management Science, 53(5), 713-725. https://doi.org/10.1287/mnsc.1060.0653; Liu, S., & Papageorgiou, L. G. (2018). Fair profit distribution in multi-echelon supply chains via transfer prices. Omega, 80, 77-94. https://doi.org/10.1016/j.omega.2017.08.010; Londoño, L. (2017). Desempeño de la Agroindustria de la Caña en Colombia 2016-2017, Performance of the Agroindustry of the Sugarcane in Colombia 2016-2017 (pp. 1-32). http://www.asocana.org//documentos/2452017.pdf; Longinidis, P., & Georgiadis, M. C. (2011). Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty. International Journal of Production Economics, 129(2), 262-276. https://doi.org/10.1016/j.ijpe.2010.10.018; Longinidis, P., & Georgiadis, M. C. (2013). Managing the trade-offs between financial performance and credit solvency in the optimal design of supply chain networks under economic uncertainty. Computers & Chemical Engineering, 48, 264-279. https://doi.org/10.1016/j.compchemeng.2012.09.019; Longinidis, P., & Georgiadis, M. C. (2014). Integration of sale and leaseback in the optimal design of supply chain networks. Omega, 47, 73-89. https://doi.org/10.1016/j.omega.2013.08.004; Longinidis, P., Georgiadis, M. C., & Kozanidis, G. (2015). Integrating Operational Hedging of Exchange Rate Risk in the Optimal Design of Global Supply Chain Networks. Industrial & Engineering Chemistry Research, 54(24), 6311-6325. https://doi.org/10.1021/acs.iecr.5b00349; Lopez Milan, E., Miquel Fernandez, S., & Pla Aragones, L. M. (2006). Sugar cane transportation in Cuba, a case study. European Journal of Operational Research, 174(1), 374-386. https://doi.org/10.1016/j.ejor.2005.01.028; Lowe, T. J., & Preckel, P. V. (2004). Decision Technologies for Agribusiness Problems: A Brief Review of Selected Literature and a Call for Research. Manufacturing & Service Operations Management, 6(3), 201-208. https://doi.org/10.1287/msom.1040.0051; Macowski, D. H., Bonfim-Rocha, L., Orgeda, R., Camilo, R., & Ravagnani, M. A. S. S. (2020). Multi-objective optimization of the Brazilian industrial sugarcane scenario: A profitable and ecological approach. Clean Technologies and Environmental Policy, 22(3), 591-611. https://doi.org/10.1007/s10098-019-01802-0; Mallawaarachchi, T., & Quiggin, J. (2001). Modelling socially optimal land allocations for sugar cane growing in North Queensland: A linked mathematical programming and choice modelling study. Australian Journal of Agricultural and Resource Economics, 45(3), 383-409. https://doi.org/10.1111/1467-8489.00149; Marin, F., Jones, J. W., & Boote, K. J. (2017). A Stochastic Method for Crop Models: Including Uncertainty in a Sugarcane Model. Agronomy Journal, 109(2), 483. https://doi.org/10.2134/agronj2016.02.0103; Martínez-Guido, SergioI., Betzabe González-Campos, J., Ponce-Ortega, JoséM., Nápoles-Rivera, F., & El-Halwagi, MahmoudM. (2016). Optimal reconfiguration of a sugar cane industry to yield an integrated biorefinery. Clean Technologies and Environmental Policy, 18(2), 553-562. https://doi.org/10.1007/s10098-015-1039-1; Martinez-Hernandez, E. (2017). Trends in sustainable process design—From molecular to global scales. Current Opinion in Chemical Engineering, 17, 35-41. https://doi.org/10.1016/j.coche.2017.05.005; Matindi, R., Masoud, M., Hobson, P., Kent, G., & Liu, S. Q. (2018). Harvesting and transport operations to optimise biomass supply chain and industrial biorefinery processes. International Journal of Industrial Engineering Computations, 265-288. https://doi.org/10.5267/j.ijiec.2017.9.001; Matis, J. H., Saito, T., Grant, W. E., Iwig, W. C., & Ritchie, J. T. (1985). A Markov chain approach to crop yield forecasting. Agricultural Systems, 18(3), 171-187. https://doi.org/10.1016/0308-521X(85)90030-7; Maxwell, D., & van der Vorst, R. (2003). Developing sustainable products and services. Journal of Cleaner Production, 11(8), 883-895. https://doi.org/10.1016/S0959-6526(02)00164-6; Meemken, E.-M., Barrett, C. B., Michelson, H. C., Qaim, M., Reardon, T., & Sellare, J. (2021). Sustainability standards in global agrifood supply chains. Nature Food, 2(10), 758-765. https://doi.org/10.1038/s43016-021-00360-3; Mele, F. D., Kostin, A. M., Guillén-Gosálbez, G., & Jiménez, L. (2011). Multiobjective Model for More Sustainable Fuel Supply Chains. A Case Study of the Sugar Cane Industry in Argentina. Industrial & Engineering Chemistry Research, 50(9), 4939-4958. https://doi.org/10.1021/ie101400g; Melo, 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.2008.05.007; Messmann, 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.2020.120459; Meza-Palacios, R., Aguilar-Lasserre, A. A., Morales-Mendoza, L. F., Pérez-Gallardo, J. R., Rico-Contreras, J. O., & Avarado-Lassman, A. (2019). Life cycle assessment of cane sugar production: The environmental contribution to human health, climate change, ecosystem quality and resources in México. Journal of Environmental Science and Health, Part A, 54(7), 668-678. https://doi.org/10.1080/10934529.2019.1579537; Ministerio de Ciencia, Tecnología e Innovación. (2019). Descripción de focos y líneas de investigación. https://minciencias.gov.co/sites/default/files/upload/convocatoria/anexo_1._descripcion_de_focos_y_lineas_de_investigacion.pdf; Ministerio Minas y Energía. (2018). Resolución 40185. República de Colombia.; Mohammadi, A., Abbasi, A., Alimohammadlou, M., Eghtesadifard, M., & Khalifeh, M. (2017). Optimal design of a multi-echelon supply chain in a system thinking framework: An integrated financial-operational approach. Computers & Industrial Engineering, 114, 297-315. https://doi.org/10.1016/j.cie.2017.10.019; Morales Chávez, 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, 136-162. https://doi.org/10.1016/j.tre.2018.06.001; Morales 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, 276, 123305. https://doi.org/10.1016/j.jclepro.2020.123305; Morales-Chávez, M. M., Soto-Mejía, J. A., & Sarache, W. A. (2016). A mixed-integer linear programming model for harvesting, loading and transporting sugarcane. A case study in Peru. DYNA, 83(195), 173-179. https://doi.org/10.15446/dyna.v83n195.49490; Mota, 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. https://doi.org/10.1016/j.jclepro.2014.07.052; Muchow, R. C., Higgins, A. J., Rudd, A. V., & Ford, A. W. (1998). Optimising harvest date in sugar production: A case study for the Mossman mill region in Australia: II. Sensitivity to crop age and crop class distribution. Field Crops Research, 57(3), 243-251.; Mutenurea, M., Čučekb, L., Isafiade, A. J., & Kravanjab, Z. (2016). Synthesis of South Africa’s Biomass to Bioethanol Supply Network. CHEMICAL ENGINEERING, 52.; Mutran, V. M., Ribeiro, C. O., Nascimento, C. A. O., & Chachuat, B. (2020). Risk-conscious optimization model to support bioenergy investments in the Brazilian sugarcane industry. Applied Energy, 258, 113978. https://doi.org/10.1016/j.apenergy.2019.113978; Oliveira, J. B., Lima, R. S., & Montevechi, J. A. B. (2016). Perspectives and relationships in Supply Chain Simulation: A systematic literature review. Simulation Modelling Practice and Theory, 62, 166-191. https://doi.org/10.1016/j.simpat.2016.02.001; Ometto, A. R., Hauschild, M. Z., & Roma, W. N. L. (2009). Lifecycle assessment of fuel ethanol from sugarcane in Brazil. Int J Life Cycle Assess, 12.; Osaki, M. R., & Seleghim Jr, P. (2017). Bioethanol and power from integrated second generation biomass: A Monte Carlo simulation. Energy Conversion and Management, 141, 274-284.; Osmani, A., & Zhang, J. (2013). Stochastic optimization of a multi-feedstock lignocellulosic-based bioethanol supply chain under multiple uncertainties. Energy, 59, 157-172. https://doi.org/10.1016/j.energy.2013.07.043; Paiva, R. P. O., & Morabito, R. (2009). An optimization model for the aggregate production planning of a Brazilian sugar and ethanol milling company. Annals of Operations Research, 169(1), 117-130. https://doi.org/10.1007/s10479-008-0428-9; Pashangpour, R., Faghihi, F., & Soleymani, S. (2018). Optimized scheduling for electric lift trucks in a sugarcane agro-industry based on thermal, biomass and solar resources. International Journal of Environmental Science and Technology, 15(11), 2349-2358.; Pathumnakul S., & Nakrachata-Amon T. (2015). The Applications of Operations Research in Harvest Planning: A Case Study of the Sugarcane Industry in Thailand. Journal of Japan Industrial Management Association, 65(4E), 328-333. https://doi.org/10.11221/jima.65.328; Pelletier, N., Ustaoglu, E., Benoit, C., Norris, G., Rosenbaum, E., Vasta, A., & Sala, S. (2018). Social sustainability in trade and development policy. The International Journal of Life Cycle Assessment, 23(3), 629-639. https://doi.org/10.1007/s11367-016-1059-z; Pereira, R. D., Badino, A. C., & Cruz, A. J. (2020). Framework Based on Artificial Intelligence to Increase Industrial Bioethanol Production. Energy & Fuels, 34(4), 4670-4677.; Piewthongngam, K., Pathumnakul, S., & Setthanan, K. (2009). Application of crop growth simulation and mathematical modeling to supply chain management in the Thai sugar industry. Agricultural Systems, 102(1-3), 58-66. https://doi.org/10.1016/j.agsy.2009.07.002; Ramirez, C. A. M. (2017). Asocaña. Sector Agroindustrial de la Caña. https://www.asocana.org/; Ramirez, C. A. M. (2021a). Balance azucarero colombiano Asocaña 2000—2020 (toneladas). Asocaña - Sector Agroindustrial de la Caña. http://www.asocana.org/modules/documentos/5528.aspx; Ramirez, C. A. M. (2021b). Informe anual 2019—2020. Asocaña - Sector Agroindustrial de la Caña. http://www.asocana.org/modules/documentos/15398.aspx; Rojas, L. S. B. (2011). OPORTUNIDADES Y AMENAZAS DE LOS BIOCOMBUSTIBLES EN COLOMBIA [PONTIFICIA UNIVERSIDAD JAVERIANA]. https://repository.javeriana.edu.co/bitstream/handle/10554/12377/BuenoRojasLucySikint2011.pdf?sequence=1; UPME. (2018). BOLETÍN ESTADÍSTICO DE MINAS Y ENERGÍA 2016—2018. Unidad de Planeación Minero Energética, UPME. Bogotá. https://www1.upme.gov.co/PromocionSector/SeccionesInteres/Documents/Boletines/Boletin_Estadistico_2018.pdf; Pitakaso, R., & Sethanan, K. (2019). Adaptive large neighborhood search for scheduling sugarcane inbound logistics equipment and machinery under a sharing infield resource system. Computers and Electronics in Agriculture, 158, 313-325. https://doi.org/10.1016/j.compag.2019.02.001; Plà, L. M., Sandars, D. L., & Higgins, A. J. (2014). A perspective on operational research prospects for agriculture. Journal of the Operational Research Society, 65(7), 1078-1089. https://doi.org/10.1057/jors.2013.45; Polo, A., Peña, N., Muñoz, D., Cañón, A., & Escobar, J. W. (2018). Robust design of a closed-loop supply chain under uncertainty conditions integrating financial criteria. Omega. https://doi.org/10.1016/j.omega.2018.09.003; Poltroniere, S. C., Aliano Filho, A., Caversan, A. S., Balbo, A. R., & Florentino, H. de O. (2021). Integrated planning for planting and harvesting sugarcane and energy-cane for the production of sucrose and energy. Computers and Electronics in Agriculture, 184, 105956. https://doi.org/10.1016/j.compag.2020.105956; Prasara-A, J., & Gheewala, S. H. (2016). Sustainability of sugarcane cultivation: Case study of selected sites in north-eastern Thailand. Journal of Cleaner Production, 134, 613-622. https://doi.org/10.1016/j.jclepro.2015.09.029; Procaña. (2018). Colombian sugarcane Industry: Description. http://www.procana.org/new/quienes-somos/presentacion-del-sector.html; Qureshi, M. E., Qureshi, S. E., Bajracharya, K., & Kirby, M. (2008). Integrated Biophysical and Economic ModellingFramework to Assess Impacts of Alternative Groundwater Management Options. Water Resources Management, 22(3), 321-341. https://doi.org/10.1007/s11269-007-9164-1; Qureshi, M. E., Qureshi, S. E., & Wegener, M. K. (2007). Economic implications of alternative mill mud management options in the Australian sugar industry. Agricultural Economics, 36(1), 113-122.; Ramezani, M., Kimiagari, A. M., & Karimi, B. (2014). Closed-loop supply chain network design: A financial approach. Applied Mathematical Modelling, 38(15-16), 4099-4119. https://doi.org/10.1016/j.apm.2014.02.004; Rebitzer, G., Ekvall, T., Frischknecht, R., Hunkeler, D., Norris, G., Rydberg, T., Schmidt, W.-P., Suh, S., Weidema, B. P., & Pennington, D. W. (2004). Life cycle assessment. Part 1: Framework, goal and scope definition, inventory analysis, and applications. Environment International, 30(5), 701-720. https://doi.org/10.1016/j.envint.2003.11.005; Renouf, M. A., Wegener, M. K., & Pagan, R. J. (2010). Life cycle assessment of Australian sugarcane production with a focus on sugarcane growing. The International Journal of Life Cycle Assessment, 15(9), 927-937. https://doi.org/10.1007/s11367-010-0226-x; Reynolds, C., Buckley, J., Weinstein, P., & Boland, J. (2014). Are the Dietary Guidelines for Meat, Fat, Fruit and Vegetable Consumption Appropriate for Environmental Sustainability? A Review of the Literature. Nutrients, 6(6), 2251-2265. https://doi.org/10.3390/nu6062251; Rivera-Cadavid, L., Manyoma-Velásquez, P. C., & Manotas-Duque, D. F. (2019). Supply Chain Optimization for Energy Cogeneration Using Sugarcane Crop Residues (SCR). Sustainability, 11(23), 6565.; Rosa, W. (Ed.). (2017). Transforming Our World: The 2030 Agenda for Sustainable Development. En A New Era in Global Health. Springer Publishing Company. https://doi.org/10.1891/9780826190123.ap02; Ross, S. M. (2014). Introduction to probability models. Academic press.; Rota, C., Pugliese, P., Hashem, S., & Zanasi, C. (2018). Assessing the level of collaboration in the Egyptian organic and fair trade cotton chain. Journal of Cleaner Production, 170, 1665-1676. https://doi.org/10.1016/j.jclepro.2016.10.011; Sahebi, H., Nickel, S., & Ashayeri, J. (2014). Strategic and tactical mathematical programming models within the crude oil supply chain context—A review. Computers & Chemical Engineering, 68, 56-77. https://doi.org/10.1016/j.compchemeng.2014.05.008; Santibañez-Aguilar, J. E., González-Campos, J. B., Ponce-Ortega, J. M., Serna-González, M., & El-Halwagi, M. M. (2014). Optimal planning and site selection for distributed multiproduct biorefineries involving economic, environmental and social objectives. Journal of Cleaner Production, 65, 270-294. https://doi.org/10.1016/j.jclepro.2013.08.004; Santoro, E., Soler, E. M., & Cherri, A. C. (2017). Route optimization in mechanized sugarcane harvesting. Computers and Electronics in Agriculture, 141, 140-146. https://doi.org/10.1016/j.compag.2017.07.013; Saranwong, S., & Likasiri, C. (2017). Bi-level programming model for solving distribution center problem: A case study in Northern Thailand’s sugarcane management. Computers & Industrial Engineering, 103, 26-39. https://doi.org/10.1016/j.cie.2016.10.031; Sarkar, B., Mridha, B., Pareek, S., Sarkar, M., & Thangavelu, L. (2021). A flexible biofuel and bioenergy production system with transportation disruption under a sustainable supply chain network. Journal of Cleaner Production, 317, 128079. https://doi.org/10.1016/j.jclepro.2021.128079; Sartori, M. M. P., de Oliveira Florentino, H., Basta, C., & Leão, A. L. (2001). Determination of the optimal quantity of crop residues for energy in sugarcane crop management using linear programming in variety selection and planting strategy. Energy, 26(11), 1031-1040.; Scully, M. J., Norris, G. A., Alarcon Falconi, T. M., & MacIntosh, D. L. (2021). Carbon intensity of corn ethanol in the United States: State of the science. Environmental Research Letters, 16(4), 043001. https://doi.org/10.1088/1748-9326/abde08; Semboloni, F. (2006). The CityDev Project: An Interactive Multi-agent Urban Model on the Web. En J. Portugali (Ed.), Complex Artificial Environments (pp. 155-163). Springer-Verlag. https://doi.org/10.1007/3-540-29710-3_10; Semenzato, R. (1995). A simulation study of sugar cane harvesting. Agricultural Systems, 47(4), 427-437. https://doi.org/10.1016/0308-521X(95)92108-I; Seuring, S., & 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.2008.04.020; Shafie, S. M., Othman, Z., & Hami, N. (2020). Optimum location of biomass waste residue power plant in northern region: Economic and environmental assessment. International Journal of Energy Economics and Policy, 10(1), 150.; Shapiro, A. (2003). Monte Carlo Sampling Methods. En Handbooks in Operations Research and Management Science (Vol. 10, pp. 353-425). Elsevier. https://doi.org/10.1016/S0927-0507(03)10006-0; Shukla, M., & Jharkharia, S. (2013). Agri‐fresh produce supply chain management: A state‐of‐the‐art literature review. International Journal of Operations & Production Management, 33(2), 114-158. https://doi.org/10.1108/01443571311295608; Sihombing, L., Latief, Y., Rarasati, A. D., & Wibowo, A. (2018). Utilizing uncertainty management to analyze the uncertainty of toll road land acquisition. International Journal of Civil Engineering and Technology, 9(6), 1221-1228. Scopus.; Simchi-Levi, D., Chen, X., & Bramel, J. (2005). The logic of logistics. Theory, Algorithms, and Applications for Logistics and Supply Chain Management.; Sørensen, C. G., & Bochtis, D. D. (2010). Conceptual model of fleet management in agriculture. Biosystems Engineering, 105(1), 41-50. https://doi.org/10.1016/j.biosystemseng.2009.09.009; Soto-Silva, W. E., González-Araya, M. C., Oliva-Fernández, M. A., & Plà-Aragonés, L. M. (2017). Optimizing fresh food logistics for processing: Application for a large Chilean apple supply chain. Computers and Electronics in Agriculture, 136, 42-57. https://doi.org/10.1016/j.compag.2017.02.020; Soto-Silva, W. E., Nadal-Roig, E., González-Araya, M. C., & Pla-Aragones, L. M. (2016). Operational research models applied to the fresh fruit supply chain. European Journal of Operational Research, 251(2), 345-355. https://doi.org/10.1016/j.ejor.2015.08.046; Sowlati, T. (2016). Modeling of forest and wood residues supply chains for bioenergy and biofuel production. En Biomass Supply Chains for Bioenergy and Biorefining (pp. 167-190). Elsevier. https://doi.org/10.1016/B978-1-78242-366-9.00008-3; Soysal, M., Bloemhof-Ruwaard, J. M., Meuwissen, M. P., & van der Vorst, J. G. (2012). A review on quantitative models for sustainable food logistics management. International Journal on Food System Dynamics, 3(2), 136-155.; Standfield, L., Comans, T., & Scuffham, P. (2014). Markov modeling and discrete event simulation in health care: A systematic comparison. International Journal of Technology Assessment in Health Care, 30(2), 165-172. https://doi.org/10.1017/S0266462314000117; Stray, B. J., van Vuuren, J. H., & Bezuidenhout, C. N. (2012). An optimisation-based seasonal sugarcane harvest scheduling decision support system for commercial growers in South Africa. Computers and Electronics in Agriculture, 83, 21-31. https://doi.org/10.1016/j.compag.2012.01.009; Sun, F., Aguayo, M. M., Ramachandran, R., & Sarin, S. C. (2018). Biomass feedstock supply chain design–a taxonomic review and a decomposition-based methodology. International Journal of Production Research, 56(17), 5626-5659.; Sun, O., & Fan, N. (2020). A Review on Optimization Methods for Biomass Supply Chain: Models and Algorithms, Sustainable Issues, and Challenges and Opportunities. Process Integration and Optimization for Sustainability. https://doi.org/10.1007/s41660-020-00108-9; Teixeira, E. dos S., Rangel, S., Florentino, H. de O., & de Araujo, S. A. (2021). A review of mathematical optimization models applied to the sugarcane supply chain. International Transactions in Operational Research.; Tsolakis, N. K., Keramydas, C. A., Toka, A. K., Aidonis, D. A., & Iakovou, E. T. (2014). Agrifood supply chain management: A comprehensive hierarchical decision-making framework and a critical taxonomy. Biosystems Engineering, 120, 47-64. https://doi.org/10.1016/j.biosystemseng.2013.10.014; UN. (2017). United Nations sustainable development agenda. United Nations Sustainable Development. http://www.un.org/sustainabledevelopment/development-agenda/; Valin, H., Sands, R. D., van der Mensbrugghe, D., Nelson, G. C., Ahammad, H., Blanc, E., Bodirsky, B., Fujimori, S., Hasegawa, T., Havlik, P., Heyhoe, E., Kyle, P., Mason-D’Croz, D., Paltsev, S., Rolinski, S., Tabeau, A., van Meijl, H., von Lampe, M., & Willenbockel, D. (2014). The future of food demand: Understanding differences in global economic models. Agricultural Economics, 45(1), 51-67. https://doi.org/10.1111/agec.12089; van den Wall Bake, J. D., Junginger, M., Faaij, A., Poot, T., & Walter, A. (2009). Explaining the experience curve: Cost reductions of Brazilian ethanol from sugarcane. Biomass and Bioenergy, 33(4), 644-658. https://doi.org/10.1016/j.biombioe.2008.10.006; van Eijck, J., Batidzirai, B., & Faaij, A. (2014). Current and future economic performance of first and second generation biofuels in developing countries. Applied Energy, 135, 115-141. https://doi.org/10.1016/j.apenergy.2014.08.015; Verweij, B., Ahmed, S., Kleywegt, A. J., Nemhauser, G., & Shapiro, A. (2003). The Sample Average Approximation Method Applied to Stochastic Routing Problems: A Computational Study. Computational Optimization and Applications, 24(2), 289-333. https://doi.org/10.1023/A:1021814225969; Will M. Bertrand, J., & Fransoo, J. C. (2002). Operations management research methodologies using quantitative modeling. International Journal of Operations & Production Management, 22(2), 241-264.; Wu, D., Baron, O., & Berman, O. (2009). Bargaining in competing supply chains with uncertainty. European Journal of Operational Research, 197(2), 548-556.; Yue, D., & You, F. (2014). Game-theoretic modeling and optimization of multi-echelon supply chain design and operation under Stackelberg game and market equilibrium. Computers & Chemical Engineering, 71, 347-361. https://doi.org/10.1016/j.compchemeng.2014.08.010; Yue, D., You, F., & Snyder, S. W. (2014). Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges. Computers & Chemical Engineering, 66, 36-56. https://doi.org/10.1016/j.compchemeng.2013.11.016; Zahraee, S. M. (2020). Biomass supply chain environmental and socio-economic analysis: 40-Years comprehensive review of methods, decision issues, sustainability challenges, and the way forward. Biomass and Bioenergy, 33.; Zandi Atashbar, N., Labadie, N., & Prins, C. (2018). Modelling and optimisation of biomass supply chains: A review. International Journal of Production Research, 56(10), 3482-3506. https://doi.org/10.1080/00207543.2017.1343506; Zheng, X.-X., Liu, Z., Li, K. W., Huang, J., & Chen, J. (2019). Cooperative game approaches to coordinating a three-echelon closed-loop supply chain with fairness concerns. International Journal of Production Economics, 212, 92-110. https://doi.org/10.1016/j.ijpe.2019.01.011; Ziolkowska, J. R. (2020). Biofuels technologies: An overview of feedstocks, processes, and technologies. En Biofuels for a More Sustainable Future (pp. 1-19). Elsevier. https://doi.org/10.1016/B978-0-12-815581-3.00001-4; https://repositorio.unal.edu.co/handle/unal/82239; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/