Modelo de decisión para determinar fuentes de retorno, incentivos y capacidad en un sistema de remanufactura

Autores/as

DOI:

https://doi.org/10.36561/ING.19.2

Palabras clave:

Cadena de suministro, Ciclo cerrado, Logística inversa, Incentivos, Remanufactura, Proveedores

Resumen

Esta investigación propone un modelo de decisión para gestionar la compra de componentes nuevos y usados en un sistema de manufactura. Los componentes nuevos se adquieren de un proveedor el cual ofrece descuentos que dependen de la capacidad reservada. Los componentes usados provienen de varias fuentes, donde la cantidad que se recibirá de cada fuente depende del incentivo suministrado. El modelo considera dos elementos estocásticos, el nivel de retorno por fuente (para componentes usados) y la posibilidad de que no se reciba el envío del proveedor de componentes nuevos. Se realiza un análisis de sensibilidad basándose en un ejemplo numérico. Los resultados demuestran relaciones significativas entre los costos de operación de las fuentes de retorno, la probabilidad de retornos bajos y el número de fuentes usados, y el nivel de incentivo asignado por fuente. Los experimentos también indican como los incentivos y la capacidad reservada dependen de la probabilidad de que no se reciban las unidades nuevas. La aplicación en la práctica del modelo y de los resultados apoyaría a que las empresas con sistemas de remanufactura de componentes seleccionen el mejor nivel de incentivos a ofrecer, determinar su capacidad y decidir cuales fuentes de retornos usar de manera minimicen sus costos totales.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Govindan, K., Soleimani, H., 2017. A review of reverse logistics and closed-loop supply chains: a Journal of Cleaner Production focus. Journal of cleaner production 142, 371-384. https://doi.org/10.1016/j.jclepro.2016.03.126

Pishvaee, M. S., Jolai, F., Razmi, J., 2009. A stochastic optimization model for integrated forward/reverse logistics network design. Journal of Manufacturing Systems. 28(4), 107-114. https://doi.org/10.1016/j.jmsy.2010.05.001

Zeballos, L. J., Méndez, C. A., Barbosa-Povoa, A. P., 2016. Design and planning of closed-loop supply chains: A risk-averse multistage stochastic approach. Industrial & Engineering Chemistry Research. 55(21), 6236-6249. http://dx.doi.org/10.1021/acs.iecr.5b03647

Coenen, J., Van der Heijden, R. E., van Riel, A. C., 2018. Understanding approaches to complexity and uncertainty in closed-loop supply chain management: Past findings and future directions. Journal of cleaner production 201, 1-13. https://doi.org/10.1016/j.jclepro.2018.07.216

Qin, Z., Ji, X., 2010. Logistics network design for product recovery in fuzzy environment. Eur. J. of Op. Res. 202(2), 479-490. https://doi.org/10.1016/j.ejor.2009.05.036

Agrawal, S., Singh, R. K., Murtaza, Q., 2015. A literature review and perspectives in reverse logistics. Resources, Conservation and Recycling 97, 76-92.https://doi.org/10.1016/j.resconrec.2015.02.009

Masoudipour, E., Amirian, H., Sahraeian, R., 2017. A novel closed-loop supply chain based on the quality of returned products. Journal of cleaner production. 151, 344-355. https://doi.org/10.1016/j.jclepro.2017.03.067

Maiti, T., Giri, B. C., 2015. A closed loop supply chain under retail price and product quality dependent demand. J. of Manuf. Syst. 37, 624-637. https://doi.org/10.1016/j.jmsy.2014.09.009

Liu, L., Wang, Z., Xu, L., Hong, X., & Govindan, K., 2017. Collection effort and reverse channel choices in a closed-loop supply chain. Journal of cleaner production 144, 492-500. https://doi.org/10.1016/j.jclepro.2016.12.126

Taleizadeh, A. A., Haghighi, F., Niaki, S. T. A., 2019. Modeling and solving a sustainable closed loop supply chain problem with pricing decisions and discounts on returned products. Journal of cleaner production. 207, 163-181. https://doi.org/10.1016/j.jclepro.2018.09.198.

Tanco, M., Jurburg, D., & Escuder, M. (2015). Main difficulties hindering supply chain performance: an exploratory analysis at Uruguayan SMEs. Supply Chain Management: An International Journal, 20(1), 11-23.

Klausner, M., & Hendrickson, C. T. (2000). Reverse-logistics strategy for product take-back. Interfaces, 30(3), 156-165.

https://doi.org/10.1287/inte.30.3.156.11657

Heese, H. S., Cattani, K., Ferrer, G., Gilland, W., Roth, A. V., 2005. Competitive advantage through take-back of used products. Eur. J. of Op. Res., 164(1), 143-157. https://doi.org/10.1016/j.ejor.2003.11.008

Matsumoto, M., Umeda, Y., 2011. An analysis of remanufacturing practices in Japan. Journal of Remanufacturing. 1(1), 2. 10.1186/2210-4690-1-2

Atasu, A., Sarvary, M., & Van Wassenhove, L. N. (2008). Remanufacturing as a marketing strategy. Management science, 54(10), 1731-1746.

https://doi.org/10.1287/mnsc.1080.0893

De Brito, M. P., Dekker, R., & Flapper, S. D. P. (2005). Reverse logistics: a review of case studies. In Distribution Logistics (pp. 243-281). Springer, Berlin, Heidelberg.

Ruiz-Torres, A.J., Mahmoodi, F., Ohmori, S., 2019. Joint determination of supplier capacity and returner incentives in a closed-loop supply chain. Journal of cleaner production. 215, 1351-1361.

Govindan, K., Soleimani, H., Kannan, D., 2015. Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European journal of operational research 240(3), 603-626. https://doi.org/10.1016/j.ejor.2014.07.012

Braz, A.C., De Mello, A.M., de Vasconcelos Gomes, L.A., de Souza Nascimento, P.T., 2018. The bullwhip effect in closed-loop supply chains: A systematic literature review. Journal of cleaner production 202, 376-389. https://doi.org/10.1016/j.jclepro.2018.08.042

De Giovanni, P., & Zaccour, G. (2019). A selective survey of game-theoretic models of closed-loop supply chains. 4OR, 17(1), 1-44. https://doi.org/10.1007/s1028

El-Sayed, M., Afia, N., El-Kharbotly, A., 2010. A stochastic model for forward–reverse logistics network design under risk. Computers & Industrial Engineering 58(3), 423-431. https://doi.org/10.1016/j.cie.2008.09.040

Zeballos, L. J., Gomes, M. I., Barbosa-Povoa, A. P., Novais, A. Q., 2012. Addressing the uncertain quality and quantity of returns in closed-loop supply chains. Computers & Chemical Engineering. 47, 237-247. https://doi.org/10.1016/j.compchemeng.2012.06.034

Benedito, E., Corominas, A., 2013. Optimal manufacturing policy in a reverse logistic system with dependent stochastic returns and limited capacities. International Journal of Production Research 51(1), 189-201.https://doi.org/10.1080/00207543.2012.655863

Cardoso, S. R., Barbosa-Póvoa, A. P. F., Relvas, S., 2013. Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty. European Journal of Operational Research 226(3), 436-451. https://doi.org/10.1016/j.ejor.2012.11.035

Zeballos, L. J., Méndez, C. A., Barbosa-Povoa, A. P., Novais, A. Q., 2014. Multi-period design and planning of closed-loop supply chains with uncertain supply and demand. Computers & Chemical Engineering. 66, 151-164. https://doi.org/10.1016/j.compchemeng.2014.02.027

Khatami, M., Mahootchi, M., Farahani, R. Z., 2015. Benders’ decomposition for concurrent redesign of forward and closed-loop supply chain network with demand and return uncertainties. Transportation Research Part E: Logistics and Transportation Review 79, 1-21. https://doi.org/10.1016/j.tre.2015.03.003

Giri, B. C., & Sharma, S., 2016. Optimal production policy for a closed-loop hybrid system with uncertain demand and return under supply disruption. Journal of cleaner production 112, 2015-2028. https://doi.org/10.1016/j.jclepro.2015.06.147

Keyvanshokooh, E., Ryan, S. M., Kabir, E., 2016. Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition. European journal of operational research 249(1), 76-92. https://doi.org/10.1016/j.ejor.2015.08.028

Hosoda, T., S. M. Disney., 2018. A unified theory of the dynamics of closed-loop supply chains. European journal of operational research 269(1), 313-326. https://doi.org/10.1016/j.ejor.2017.07.020

Zeballos, L.J., Méndez, C.A. and Barbosa-Povoa, A.P., 2018. Integrating decisions of product and closed-loop supply chain design under uncertain return flows. Computers & Chemical Engineering. 112, 211-238. https://doi.org/10.1016/j.compchemeng.2018.02.011

Wu, G.H., Chang, C.K. and Hsu, L.M., 2018. Comparisons of Interactive Fuzzy Programming Approaches for Closed-loop Supply Chain Network Design under Uncertainty. Computers & Industrial Engineering 68. https://doi.org/10.1016/j.cie.2018.09.022.

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

Ponte, B., M.M. Naim, A. A. Syntetos. (Forthcoming). The effect of returns volume uncertainty on the dynamic performance of closed-loop supply chains. Journal of Remanufacturing (2019). https://doi.org/10.1007/s13243-019-00070-x

Aras, N., Aksen, D., 2008. Locating collection centers for distance-and incentive-dependent returns. International Journal of Production Economics 111(2), 316-333. https://doi.org/10.1016/j.ijpe.2007.01.015

Aras, N., Aksen, D., Tanuğur, A. G., 2008. Locating collection centers for incentive-dependent returns under a pick-up policy with capacitated vehicles. European Journal of Operational Research 191(3), 1223-1240. https://doi.org/10.1016/j.ejor.2007.08.002

He, Y., 2015. Acquisition pricing and remanufacturing decisions in a closed-loop supply chain. International Journal of Production Economics 163, 48-60. https://doi.org/10.1016/j.ijpe.2015.02.002

De Giovanni, P., Reddy, P. V., Zaccour, G., 2016. Incentive strategies for an optimal recovery program in a closed-loop supply chain. European Journal of Operational Research 249(2), 605-617. https://doi.org/10.1016/j.ejor.2015.09.021

Bhattacharya, R., Kaur, A., & Amit, R. K., 2018. Price optimization of multi-stage remanufacturing in a closed loop supply chain. Journal of cleaner production 186, 943-962. https://doi.org/10.1016/j.jclepro.2018.02.222

Modak, N. M., Modak, N., Panda, S., Sana, S. S., 2018. Analyzing structure of two-echelon closed-loop supply chain for pricing, quality and recycling management. Journal of cleaner production. 171, 512-528. https://doi.org/10.1016/j.jclepro.2017.10.033

Aissaoui N, Haouari M, Hassini E. 2007, Supplier selection and order lot sizing modeling: A review. Computers & operations research 34, 3516–3540.

Ho, W., Xu, X., Dey, P.K., 2010. Multi-criteria decision-making approaches for supplier evaluation and selection: A literature review. European journal of operational research 202, 16–24.

Sawik, T., 2013, Selection of resilient supply portfolio under disruption risks. Omega. 41, 259-269.

Berger, P.D., Gerstenfeld, A., Zeng, A.Z., 2004, How many suppliers are best? A decision making approach. Omega. 32(1), 9-15.

Ruiz-Torres, A.J., Mahmoodi, F., 2006, A supplier allocation model considering delivery failure, maintenance and supplier cycle costs. International Journal of Production Economics. 103(2), 755-766.

Moritz, S., & Pibernik, R. (2008). The optimal number of suppliers in the presence of volume discounts and different compensation potentials-an analytical and numerical analysis. European Business School Research Paper, (09-03). https://dx.doi.org/10.2139/ssrn.1358037

Sarkar A., Mohapatra, P.K.J., 2009, Determining the optimal size of supply base with the consideration of risks of supply disruptions. International Journal of Production Economics. 119(1), 122-135.

Meena, P.L., Sarmah, S.P., Sarkar, A., 2011. Sourcing decisions under risks of catastrophic event disruptions. Transportation Research Part E: Logistics and Transportation Review. 47(6), 1058-1074.

Sawik, T., 2011, Supplier selection in make-to-order environment with risks. Mathematical and Computer Modelling. 53(9-10), 1670-1679.

Meena, P.L., Sarmah, S.P., 2013, Multiple sourcing under supplier failure risk and quantity discount: A genetic algorithm approach. Transportation Research Part E: Logistics and Transportation Review. 50 (C), 84-97.

Ruiz-Torres, A.J., Mahmoodi, F., Zeng, A.Z., 2013, Supplier selection model with contingency planning for supplier failures. Computers & Industrial Engineering. 66(2), 374-382.

Torabi, S.A., Baghersad, M., Mansouri, S.A., 2015, Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review. 79, 22-48.

Kamalahmadi, M., & Mellat-Parast, M. (2016). Developing a resilient supply chain through supplier flexibility and reliability assessment. International Journal of Production Research, 54(1), 302-321. https://doi.org/10.1080/00207543.2015.1088971

Meena, P.L., Sarmah, S.P., 2016, Supplier selection and demand allocation under supply disruption risks The International Journal of Advanced Manufacturing Technology 83, 265-274.

Sawik, T., 2018, Selection of a dynamic supply portfolio under delay and disruption risks. International Journal of Production Research. 56(1-2), 760-782. https://www.tandfonline.com/doi/full/10.1080/00207543.2017.1401238

Esmaeili-Najafabadi, E., Nezhad, M. S. F., Pourmohammadi, H., Honarvar, M., & Vahdatzad, M. A. (2019). A joint supplier selection and order allocation model with disruption risks in centralized supply chain. Computers & Industrial Engineering 127, 734-748. https://doi.org/10.1016/j.cie.2018.11.017

Lücker, F., Seifert, R. W., & Biçer, I. (2019). Roles of inventory and reserve capacity in mitigating supply chain disruption risk. European Journal of Operational Research 57(4), 1238-1249. https://doi.org/10.1080/00207543.2018.1504173

Hosseini, S., Morshedlou, N., Ivanov, D., Sarder, M.D., Barker, K, Al Khaled, A., 2019. Resilient supplier selection and optimal order allocation under disruption risks. International Journal of Production Economics. 213, 124-137.

Descargas

Publicado

2020-12-16

Cómo citar

[1]
A. J. Ruíz-Torres, D. Jurburg, y Y. López-Correa, «Modelo de decisión para determinar fuentes de retorno, incentivos y capacidad en un sistema de remanufactura», Memoria investig. ing. (Facultad Ing., Univ. Montev.), n.º 19, pp. 2–24, dic. 2020.

Número

Sección

Artículos