How to cite this paper
Escobar, J., Peña, W & García-Cáceres, R. (2023). Robust multiobjective scheme for closed-loop supply chains by considering financial criteria and scenarios.International Journal of Industrial Engineering Computations , 14(2), 361-380.
Refrences
Alavi, S. H., & Jabbarzadeh, A. (2018). Supply chain network design using trade credit and bank credit: A robust optimization model with real world application. Computers & Industrial Engineering, 125, 69–86.
https://doi.org/10.1016/j.cie.2018.08.005
Ali, S., Maciejewski, A. A., Siegel, H. J., & Kim, J. K. (2004). Measuring the robustness of a resource allocation. IEEE Transactions on Parallel and Distributed Systems, 15(7), 630–641. https://doi.org/10.1109/TPDS.2004.24
Allaoui, H., Guo, Y., Choudhary, A., & Bloemhof, J. (2018). Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach. Computers & Operations Research, 89, 369–384.
https://doi.org/10.1016/j.cor.2016.10.012
Amin, S. H., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165–4176.
https://doi.org/10.1016/j.apm.2012.09.039
Azaron, A., Brown, K. N., Tarim, S. A., & Modarres, M. (2008). A multi-objective stochastic programming approach for supply chain design considering risk. International Journal of Production Economics, 116(1), 129–138.
https://doi.org/10.1016/j.ijpe.2008.08.002
Bagajewicz, M. J., & Barbaro, A. F. (2003). Financial risk management in planning under uncertainty. Proceedings Foundations of Computer-Aided Process Operations, 27–30.
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
Banasik, A., Kanellopoulos, A., Claassen, G. D. H., Bloemhof-Ruwaard, J. M., & van der Vorst, J. G. (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.2016.08.012
Ben-Tal, A., & Nemirovski, A. (1999). Robust solutions of uncertain linear programs. Operations Research Letters, 25(1), 1–13. https://doi.org/10.1016/S0167-6377(99)00016-4
Buritica, N. C., Escobar, J. W., & Sánchez, L. V. T. (2017). Designing a sustainable supply network by using mathematical programming: a case of fish industry. International Journal of Industrial and Systems Engineering, 27(1), 48–72. https://doi.org/10.1504/IJISE.2017.085754
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
Chen, C. L., & Lee, W. C. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, 28(6–7), 1131–1144.
https://doi.org/10.1016/j.compchemeng.2003.09.014
Delgoshaei, A., Aram, A., & Ali, A. (2019). A robust optimization approach for scheduling a supply chain system considering preventive maintenance and emergency services using a hybrid ant colony optimization and simulated annealing algorithm. Uncertain Supply Chain Management, 7(2), 251–274. https://doi.org/10.5267/j.uscm.2018.10.001
Eppen, G. D., Martin, R. K., & Schrage, L. (1989). OR practice—a scenario approach to capacity planning. Operations Research, 37(4), 517–527. https://doi.org/10.1287/opre.37.4.517
Escobar, J. W. (2009). Modelación y optimización de redes de distribución de productos de consumo masivo con elementos estocásticos. Proceedings of XIV Latin American Summer Workshop on Operations Research (ELAVIO).
Escobar, J.W. (2012). Rediseño de una red de distribución con variabilidad de demanda usando la metodología de escenarios. Revista Facultad De Ingeniería, 21(32), 9–19.
Retrieved from https://revistas.uptc.edu.co/index.php/ingenieria/article/view/1439
Escobar, J.W., Bravo, J. J., & Vidal, C. J. (2013). Optimización de una red de distribución con parámetros estocásticos usando la metodología de aproximación por promedios muéstrales. Revista Científica Ingeniería y Desarrollo, 31(1), 135–160.
Escobar, J.W. (2017). Supply chain optimization with variable demand by considering financial criteria and scenarios. Revista Facultad De Ingeniería, 26(44), 23–34. https://doi.org/10.19053/01211129.v26.n44.2017.5769
Escobar, J.W., Marin, A. A., & Lince, J. D. (2020). Multi-objective mathematical model for the redesign of supply chains considering financial criteria optimisation and scenarios. International Journal of Mathematics in Operational Research, 16(2), 238–256. https://doi.org/10.1504/IJMOR.2020.105903
García-Cáceres, R. G., & Escobar, J. W. (2016). Characterization of supply chain problems. Dyna, 83(198), 68–78. http://dx.doi.org/10.15446/dyna.v83n198.44532.
Guerriero, F., Pezzella, F., Pisacane, O., & Trollini, L. (2014). Multi-objective optimization in dial-a-ride public transportation. Transportation Research Procedia, 3, 299–308. https://doi.org/10.1016/j.trpro.2014.10.009
Guillén, G., Mele, F. D., Bagajewicz, M. J., Espuna, A., & Puigjaner, L. (2005). Multiobjective supply chain design under uncertainty. Chemical Engineering Science, 60(6), 1535–1553. https://doi.org/10.1016/j.ces.2004.10.023
Habibi, F., Asadi, E., Sadjadi, S. J., & Barzinpour, F. (2017). A multi-objective robust optimization model for site-selection and capacity allocation of municipal solid waste facilities: A case study in Tehran. Journal of Cleaner Production, 166, 816–834. https://doi.org/10.1016/j.jclepro.2017.08.063
Heidari-Fathian, H., & Pasandideh, S. H. R. (2018). Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation. Computers & Industrial Engineering, 122, 95–105.
https://doi.org/10.1016/j.cie.2018.05.051
Jabbarzadeh, A., Fahimnia, B., & Rastegar, S. (2017). Green and resilient design of electricity supply chain networks: a multiobjective robust optimization approach. IEEE Transactions on Engineering Management, 66(1), 52–72.
https://doi.org/10.1109/TEM.2017.2749638
Juhász, L. (2011). Net present value versus internal rate of return. Economics & Sociology, 4(1), 46–53.
https://doi.org/10.14254/2071-789X.2011/4-1/5
Kim, J., Do Chung, B., Kang, Y., & Jeong, B. (2018). Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. Journal of Cleaner Production, 196, 1314–1328.
https://doi.org/10.1016/j.jclepro.2018.06.157
Klibi, W., & Martel, A. (2012). Scenario-based supply chain network risk modeling. European Journal of Operational Research, 223(3), 644–658. https://doi.org/10.1016/j.ejor.2012.06.027
Kovačić, D., & Bogataj, M. (2017). Net present value evaluation of energy production and consumption in repeated reverse logistics. Technological and economic development of economy, 23(6), 877–894.
https://doi.org/10.3846/20294913.2015.1065455
Laumanns, M., Thiele, L., & Zitzler, E. (2006). An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method. European Journal of Operational Research, 169(3), 932–942.
https://doi.org/10.1016/j.ejor.2004.08.029
Mafla, I., & Escobar, J. W. (2015). Rediseño de una red de distribución para un grupo de empresas que pertenecen a un holding multinacional considerando variabilidad en la demanda. Revista de la Facultad de Ingeniería Universidad Central de Venezuela, 30(1), 37–48.
Monostori, J. (2018). Supply chains robustness: Challenges and opportunities. Procedia CIRP, 67, 110–115. https://doi.org/10.1016/j.procir.2017.12.185
Muchiri, P., & Pintelon, L. (2008). Performance measurement using overall equipment effectiveness (OEE): literature review and practical application discussion. International Journal of Production Research, 46(13), 3517–3535.
https://doi.org/10.1080/00207540601142645
Paz, J., Orozco, J., Salinas, J., Buriticá, N., & Escobar, J. (2015). Redesign of a supply network by considering stochastic demand. International Journal of Industrial Engineering Computations, 6(4), 521–528.
https://doi.org/10.5267/j.ijiec.2015.5.001
Paz, J. C., & Escobar, J. W. (2019). An optimisation framework of a global supply chain considering transfer pricing for a Colombian multinational company. International Journal of Industrial and Systems Engineering, 33(4), 435–449. https://doi.org/10.1504/IJISE.2019.104273
Pérez‐Cañedo, B., Verdegay, J. L., & Miranda Pérez, R. (2020). An epsilon‐constraint method for fully fuzzy multiobjective linear programming. International Journal of Intelligent Systems, 35(4), 600–624. https://doi.org/10.1002/int.22219
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637–649. https://doi.org/10.1016/j.apm.2010.07.013
Polo, A., Peña, N., Muñoz, D., Cañón, A., & Escobar, J. W. (2019). Robust design of a closed-loop supply chain under uncertainty conditions integrating financial criteria. Omega, 88, 110–132. https://doi.org/10.1016/j.omega.2018.09.003
Polo, A., Muñoz, D., Cañon, A., & Escobar, J. W. (2020). Methodology for robustness analysis to supply chain disruptions. International Journal of Logistics Systems and Management, 36(4), 547–588.
https://doi.org/10.1504/IJLSM.2020.108952
Rodado, D., Escobar, J., García-Cáceres, R., & Atencio, F. (2017). A mathematical model for the product mixing and lot-sizing problem by considering stochastic demand. International Journal of Industrial Engineering Computations, 8(2), 237–250. https://doi.org/10.5267/j.ijiec.2016.9.003
Ruimin, M. A., Lifei, Y. A. O., Maozhu, J. I. N., Peiyu, R. E. N., & Zhihan, L. V. (2016). Robust environmental closed-loop supply chain design under uncertainty. Chaos, Solitons & Fractals, 89, 195–202.
https://doi.org/10.1016/j.chaos.2015.10.028
Srivastava, S. K. (2007). Green supply‐chain management: a state‐of‐the‐art literature review. International Journal of Management Reviews, 9(1), 53–80. https://doi.org/10.1111/j.1468-2370.2007.00202.x
Tordecilla-Madera, R., Roa, A. P., Escobar, J. W., & Buriticá, N. C. (2018). A mathematical model for collecting and distributing perishable products by considering costs minimisation and CO2 emissions. International Journal of Services and Operations Management, 31(2), 207-234. https://doi.org/10.1504/IJSOM.2018.094752
https://doi.org/10.1016/j.cie.2018.08.005
Ali, S., Maciejewski, A. A., Siegel, H. J., & Kim, J. K. (2004). Measuring the robustness of a resource allocation. IEEE Transactions on Parallel and Distributed Systems, 15(7), 630–641. https://doi.org/10.1109/TPDS.2004.24
Allaoui, H., Guo, Y., Choudhary, A., & Bloemhof, J. (2018). Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach. Computers & Operations Research, 89, 369–384.
https://doi.org/10.1016/j.cor.2016.10.012
Amin, S. H., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165–4176.
https://doi.org/10.1016/j.apm.2012.09.039
Azaron, A., Brown, K. N., Tarim, S. A., & Modarres, M. (2008). A multi-objective stochastic programming approach for supply chain design considering risk. International Journal of Production Economics, 116(1), 129–138.
https://doi.org/10.1016/j.ijpe.2008.08.002
Bagajewicz, M. J., & Barbaro, A. F. (2003). Financial risk management in planning under uncertainty. Proceedings Foundations of Computer-Aided Process Operations, 27–30.
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
Banasik, A., Kanellopoulos, A., Claassen, G. D. H., Bloemhof-Ruwaard, J. M., & van der Vorst, J. G. (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.2016.08.012
Ben-Tal, A., & Nemirovski, A. (1999). Robust solutions of uncertain linear programs. Operations Research Letters, 25(1), 1–13. https://doi.org/10.1016/S0167-6377(99)00016-4
Buritica, N. C., Escobar, J. W., & Sánchez, L. V. T. (2017). Designing a sustainable supply network by using mathematical programming: a case of fish industry. International Journal of Industrial and Systems Engineering, 27(1), 48–72. https://doi.org/10.1504/IJISE.2017.085754
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
Chen, C. L., & Lee, W. C. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, 28(6–7), 1131–1144.
https://doi.org/10.1016/j.compchemeng.2003.09.014
Delgoshaei, A., Aram, A., & Ali, A. (2019). A robust optimization approach for scheduling a supply chain system considering preventive maintenance and emergency services using a hybrid ant colony optimization and simulated annealing algorithm. Uncertain Supply Chain Management, 7(2), 251–274. https://doi.org/10.5267/j.uscm.2018.10.001
Eppen, G. D., Martin, R. K., & Schrage, L. (1989). OR practice—a scenario approach to capacity planning. Operations Research, 37(4), 517–527. https://doi.org/10.1287/opre.37.4.517
Escobar, J. W. (2009). Modelación y optimización de redes de distribución de productos de consumo masivo con elementos estocásticos. Proceedings of XIV Latin American Summer Workshop on Operations Research (ELAVIO).
Escobar, J.W. (2012). Rediseño de una red de distribución con variabilidad de demanda usando la metodología de escenarios. Revista Facultad De Ingeniería, 21(32), 9–19.
Retrieved from https://revistas.uptc.edu.co/index.php/ingenieria/article/view/1439
Escobar, J.W., Bravo, J. J., & Vidal, C. J. (2013). Optimización de una red de distribución con parámetros estocásticos usando la metodología de aproximación por promedios muéstrales. Revista Científica Ingeniería y Desarrollo, 31(1), 135–160.
Escobar, J.W. (2017). Supply chain optimization with variable demand by considering financial criteria and scenarios. Revista Facultad De Ingeniería, 26(44), 23–34. https://doi.org/10.19053/01211129.v26.n44.2017.5769
Escobar, J.W., Marin, A. A., & Lince, J. D. (2020). Multi-objective mathematical model for the redesign of supply chains considering financial criteria optimisation and scenarios. International Journal of Mathematics in Operational Research, 16(2), 238–256. https://doi.org/10.1504/IJMOR.2020.105903
García-Cáceres, R. G., & Escobar, J. W. (2016). Characterization of supply chain problems. Dyna, 83(198), 68–78. http://dx.doi.org/10.15446/dyna.v83n198.44532.
Guerriero, F., Pezzella, F., Pisacane, O., & Trollini, L. (2014). Multi-objective optimization in dial-a-ride public transportation. Transportation Research Procedia, 3, 299–308. https://doi.org/10.1016/j.trpro.2014.10.009
Guillén, G., Mele, F. D., Bagajewicz, M. J., Espuna, A., & Puigjaner, L. (2005). Multiobjective supply chain design under uncertainty. Chemical Engineering Science, 60(6), 1535–1553. https://doi.org/10.1016/j.ces.2004.10.023
Habibi, F., Asadi, E., Sadjadi, S. J., & Barzinpour, F. (2017). A multi-objective robust optimization model for site-selection and capacity allocation of municipal solid waste facilities: A case study in Tehran. Journal of Cleaner Production, 166, 816–834. https://doi.org/10.1016/j.jclepro.2017.08.063
Heidari-Fathian, H., & Pasandideh, S. H. R. (2018). Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation. Computers & Industrial Engineering, 122, 95–105.
https://doi.org/10.1016/j.cie.2018.05.051
Jabbarzadeh, A., Fahimnia, B., & Rastegar, S. (2017). Green and resilient design of electricity supply chain networks: a multiobjective robust optimization approach. IEEE Transactions on Engineering Management, 66(1), 52–72.
https://doi.org/10.1109/TEM.2017.2749638
Juhász, L. (2011). Net present value versus internal rate of return. Economics & Sociology, 4(1), 46–53.
https://doi.org/10.14254/2071-789X.2011/4-1/5
Kim, J., Do Chung, B., Kang, Y., & Jeong, B. (2018). Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. Journal of Cleaner Production, 196, 1314–1328.
https://doi.org/10.1016/j.jclepro.2018.06.157
Klibi, W., & Martel, A. (2012). Scenario-based supply chain network risk modeling. European Journal of Operational Research, 223(3), 644–658. https://doi.org/10.1016/j.ejor.2012.06.027
Kovačić, D., & Bogataj, M. (2017). Net present value evaluation of energy production and consumption in repeated reverse logistics. Technological and economic development of economy, 23(6), 877–894.
https://doi.org/10.3846/20294913.2015.1065455
Laumanns, M., Thiele, L., & Zitzler, E. (2006). An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method. European Journal of Operational Research, 169(3), 932–942.
https://doi.org/10.1016/j.ejor.2004.08.029
Mafla, I., & Escobar, J. W. (2015). Rediseño de una red de distribución para un grupo de empresas que pertenecen a un holding multinacional considerando variabilidad en la demanda. Revista de la Facultad de Ingeniería Universidad Central de Venezuela, 30(1), 37–48.
Monostori, J. (2018). Supply chains robustness: Challenges and opportunities. Procedia CIRP, 67, 110–115. https://doi.org/10.1016/j.procir.2017.12.185
Muchiri, P., & Pintelon, L. (2008). Performance measurement using overall equipment effectiveness (OEE): literature review and practical application discussion. International Journal of Production Research, 46(13), 3517–3535.
https://doi.org/10.1080/00207540601142645
Paz, J., Orozco, J., Salinas, J., Buriticá, N., & Escobar, J. (2015). Redesign of a supply network by considering stochastic demand. International Journal of Industrial Engineering Computations, 6(4), 521–528.
https://doi.org/10.5267/j.ijiec.2015.5.001
Paz, J. C., & Escobar, J. W. (2019). An optimisation framework of a global supply chain considering transfer pricing for a Colombian multinational company. International Journal of Industrial and Systems Engineering, 33(4), 435–449. https://doi.org/10.1504/IJISE.2019.104273
Pérez‐Cañedo, B., Verdegay, J. L., & Miranda Pérez, R. (2020). An epsilon‐constraint method for fully fuzzy multiobjective linear programming. International Journal of Intelligent Systems, 35(4), 600–624. https://doi.org/10.1002/int.22219
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637–649. https://doi.org/10.1016/j.apm.2010.07.013
Polo, A., Peña, N., Muñoz, D., Cañón, A., & Escobar, J. W. (2019). Robust design of a closed-loop supply chain under uncertainty conditions integrating financial criteria. Omega, 88, 110–132. https://doi.org/10.1016/j.omega.2018.09.003
Polo, A., Muñoz, D., Cañon, A., & Escobar, J. W. (2020). Methodology for robustness analysis to supply chain disruptions. International Journal of Logistics Systems and Management, 36(4), 547–588.
https://doi.org/10.1504/IJLSM.2020.108952
Rodado, D., Escobar, J., García-Cáceres, R., & Atencio, F. (2017). A mathematical model for the product mixing and lot-sizing problem by considering stochastic demand. International Journal of Industrial Engineering Computations, 8(2), 237–250. https://doi.org/10.5267/j.ijiec.2016.9.003
Ruimin, M. A., Lifei, Y. A. O., Maozhu, J. I. N., Peiyu, R. E. N., & Zhihan, L. V. (2016). Robust environmental closed-loop supply chain design under uncertainty. Chaos, Solitons & Fractals, 89, 195–202.
https://doi.org/10.1016/j.chaos.2015.10.028
Srivastava, S. K. (2007). Green supply‐chain management: a state‐of‐the‐art literature review. International Journal of Management Reviews, 9(1), 53–80. https://doi.org/10.1111/j.1468-2370.2007.00202.x
Tordecilla-Madera, R., Roa, A. P., Escobar, J. W., & Buriticá, N. C. (2018). A mathematical model for collecting and distributing perishable products by considering costs minimisation and CO2 emissions. International Journal of Services and Operations Management, 31(2), 207-234. https://doi.org/10.1504/IJSOM.2018.094752