Abstract
The literature review shows research gaps into the food supply chain design. In that context, this paper deals with the design of a sustainable supply chain. A multi-objective mixed-integer linear programming model includes four decisions and three sustainable criteria (economic—total network costs—, environmental—carbon emissions—, and social—work conditions and societal development—). The model aims to determine the optimal location and capacity of processing and distribution facilities, to choose the suppliers from a set of potential candidates, to determine transportation modes between all the actors, and to define the quantity of product, in order to satisfy the demand of dairy products in a set of regions. The applicability of the model is tested in a realistic case in the dairy sector in the central region of Colombia. The results show the existent trade-offs between the three dimensions of sustainability. The unweighted balance results, giving more priority to the social dimension, which obtains the least deviation, affecting the environmental performance of the chain. The analysis carried out in this paper does help decision-makers that will have at hand a set of possible configurations to be chosen in order to comply with environmental and social regulations without neglecting economic performance.
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References
Accorsi, R., Cholette, S., Manzini, R., Pini, C., & Penazzi, S. (2016). The land-network problem: Ecosystem carbon balance in planning sustainable agro-food supply chains. Journal of Cleaner Production, 112, 158–171. https://doi.org/10.1016/j.jclepro.2015.06.082
Afshari, H., Sharafi, M., ElMekkawy, T., & Peng, Q. (2016). Multi-objective optimisation of facility location decisions within integrated forward/reverse logistics under uncertainty. International Journal of Business Performance and Supply Chain Modelling, 8(3), 250–276.
Alpina. (2018). Informe de Sostenibilidad Alpina 2018. https://www.alpina.com/Portals/_default/Sostenibilidad/Informes-sostenibilidad/Informe-de-Sostenibilidad-2018.pdf
Anvari, S., & Turkay, M. (2017). The facility location problem from the perspective of triple bottom line accounting of sustainability. International Journal of Production Research, 55(21), 6266–6287. https://doi.org/10.1080/00207543.2017.1341064
Asadi, E., Habibi, F., Nickel, S., & Sahebi, H. (2018). A bi-objective stochastic location-inventory-routing model for microalgae-based biofuel supply chain. Applied Energy, 228(June), 2235–2261. https://doi.org/10.1016/j.apenergy.2018.07.067
Barbosa-Póvoa, A. P., da Silva, C., & Carvalho, A. (2018). Opportunities and challenges in sustainable supply chain: An operations research perspective. European Journal of Operational Research, 268(2), 399–431. https://doi.org/10.1016/j.ejor.2017.10.036
Beske, P., Land, A., & Seuring, S. (2014). Sustainable supply chain management practices and dynamic capabilities in the food industry: A critical analysis of the literature. International Journal of Production Economics, 152, 131–143.
Brandenburg, M. (2015). Low carbon supply chain configuration for a new product—A goal programming approach. International Journal of Production Research, 53(21), 6588–6610. https://doi.org/10.1080/00207543.2015.1005761
Brandenburg, M., Hahn, G. J., & Rebs, T. (2018). Sustainable supply chains: Recent developments and future trends. In M. Brandenburg, G. Hahn, & T. Rebs (Eds.), Social and environmental dimensions of organizations and supply chains. Greening of Industry Networks Studies. (Vol. 5). Springer.
Cambero, C., & Sowlati, T. (2016). Incorporating social benefits in multi-objective optimization of forest-based bioenergy and biofuel supply chains. Applied Energy, 178, 721–735. https://doi.org/10.1016/j.apenergy.2016.06.079
Cambero, C., Sowlati, T., & Pavel, M. (2016). Economic and life cycle environmental optimization of forest-based biorefinery supply chains for bioenergy and biofuel production. Chemical Engineering Research and Design, 107, 218–235. https://doi.org/10.1016/j.cherd.2015.10.040
Chardine-Baumann, E., & Botta-Genoulaz, V. (2014). A framework for sustainable performance assessment of supply chain management practices. Computers and Industrial Engineering, 76(1), 138–147. https://doi.org/10.1016/j.cie.2014.07.029
Chiandussi, G., Codegone, M., Ferrero, S., & Varesio, F. E. (2012). Comparison of multi-objective optimization methodologies for engineering applications. Computers & Mathematics with Applications, 63(5), 912–942. https://doi.org/10.1016/j.camwa.2011.11.057
Colicchia, C., Creazza, A., Dallari, F., & Melacini, M. (2016). Eco-efficient supply chain networks: Development of a design framework and application to a real case study. Production Planning and Control, 27(3), 157–168. https://doi.org/10.1080/09537287.2015.1090030
CONPES. (2010). CONPES 3675. Política Nacional Para Mejorar La Competitividad Del Sector Lácteo Colombiano.
Cuong, T. N., Kim, H. S., Nguyen, D. A., & You, S. S. (2021). Nonlinear analysis and active management of production-distribution in nonlinear supply chain model using sliding mode control theory. Applied Mathematical Modelling, 97, 418–437.
DANE. (2019). Comportamiento de los precios de la leche cruda en finca durante el mes de agosto 2019. Boletin mensual leche cruda en finca. DANE. https://www.dane.gov.co/files/investigaciones/agropecuario/sipsa/BolSipsaLeche_ago_2019.pdf. Accessed May 3, 2021.
Dolgui, A., Ivanov, D., Sethi, S. P., & Sokolov, B. (2019). Scheduling in production, supply chain and Industry 4.0 systems by optimal control: Fundamentals, state-of-the-art and applications. International Journal of Production Research, 57(2), 411–432.
Dubey, R., et al. (2017). World class sustainable supply chain management: Critical review and further research directions. The International Journal of Logistics Management, 28(2), 332–362. https://doi.org/10.1108/IJLM-07-2015-0112
Escobar, J. W., Sánchez, L., & Buritica, N. (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. https://doi.org/10.1504/ijise.2017.10006218
Eskandarpour, M., Dejax, P., Miemczyk, J., & Péton, O. (2015). Sustainable supply chain network design: An optimization-oriented review. Omega (united Kingdom), 54, 11–32. https://doi.org/10.1016/j.omega.2015.01.006
Ehrgott, M., & Wiecek, M. M. (2005). Mutiobjective programming. In J. Figueira, S. Greco, & M. Ehrogott (Eds.), Multiple criteria decision analysis: State of the art surveys (pp. 667–708). Springer. https://doi.org/10.1007/0-387-23081-5_17
Fahimnia, B., Sarkis, J., Gunasekaran, A., & Farahani, R. (2017). Decision models for sustainable supply chain design and management. Annals of Operations Research, 250, 277–278.
Fang, Y., Jiang, Y., Sun, L., & Han, X. (2018). Design of green cold chain networks for imported fresh agri-products in belt and road development. Sustainability (switzerland), 10(5), 1572. https://doi.org/10.3390/su10051572
Gargalo, C. L., Carvalho, A., Gernaey, K. V., & Sin, G. (2017). Optimal design and planning of glycerol-based biorefinery supply chains under uncertainty. Industrial & Engineering Chemistry Research, 56(41), 11870–11893. https://doi.org/10.1021/acs.iecr.7b02882
Guarnieri, P., & Trojan, F. (2019). Decision making on supplier selection based on social, ethical, and environmental criteria: A study in the textile industry. Resources, Conservation and Recycling, 141, 347–361.
Ghufran, S., Khowaja, S., & Ahsan, M. J. (2015). Optimum multivariate stratified double sampling design: Chebyshev’s goal programming approach. Journal of Applied Statistics, 42(5), 1032–1042. https://doi.org/10.1080/02664763.2014.995603
Govindan, K., Jha, P. C., & Garg, K. (2016). Product recovery optimization in closed-loop supply chain to improve sustainability in manufacturing. International Journal of Production Research, 54(5), 1463–1486. https://doi.org/10.1080/00207543.2015.1083625
Hartikainen, M., Miettinen, K., & Wiecek, M. M. (2011). Constructing a Pareto front approximation for decision making. Mathematical Methods of Operations Research, 73(2), 209–234. https://doi.org/10.1007/s00186-010-0343-0
Khan, A. S., Pruncu, C. I., Khan, R., Naeem, K., Ghaffar, A., Ashraf, P., & Room, S. (2020). A trade-off analysis of economic and environmental aspects of a disruption based closed-loop supply chain network. Sustainability, 12(17), 7056. https://doi.org/10.3390/su12177056
Kucukvar, M., Onat, N. C., Abdella, G. M., et al. (2019). Assessing regional and global environmental footprints and value added of the largest food producers in the world. Resources, Conservation and Recycling, 144, 187–197.
Jones, D., & Tamiz, M. (2010). Goal programming variants (pp. 11–22). Springer. https://doi.org/10.1007/978-1-4419-5771-9_2
Jouzdani, J., & Govindan, K. (2021). On the sustainable perishable food supply chain network design: A dairy products case to achieve sustainable development goals. Journal of Cleaner Production, 278, 123060.
MADR. (2018). Cartilla informativa para liquidacion del litro de leche al productor segun resolucion 017 de 2012. http://uspleche.minagricultura.gov.co/assets/cartilla_informativa_2018-2019.pdf.
Ministerio de Transporte de Colombia, “SICETAC.” Bogotá D.C., Colombia. (2016). https://plc.mintransporte.gov.co/Runtime/empresa/ctl/SiceTAC/mid/417.
Miranda-Ackerman, M. A., Azzaro-Pantel, C., & Aguilar-Lasserre, A. A. (2017). A green supply chain network design framework for the processed food industry: Application to the orange juice agrofood cluster. Computers and Industrial Engineering, 109, 369–389. https://doi.org/10.1016/j.cie.2017.04.031
Miret, C., Chazara, P., Montastruc, L., Negny, S., & Domenech, S. (2016). Design of bioethanol green supply chain: Comparison between first and second generation biomass concerning economic, environmental and social criteria. Computers and Chemical Engineering, 85, 16–35. https://doi.org/10.1016/j.compchemeng.2015.10.008
Modgil, S., Shivam, G., & Bharat, B. (2020). Building a living economy through modern information decision support systems and UN sustainable development goals. Production Planning & Control, 31(11–12), 967–987.
Montoya-Torres, J. (2015). Designing sustainable supply chains based on the triple bottom line approach. In 2015 4th international conference on advanced logistics and transport (ICALT), 1–6. IEEE. https://doi.org/10.1109/ICAdLT.2015.7136581.
Montoya-Torres, J. R., Gutierrez-Franco, E., & Blanco, E. E. (2015). Conceptual framework for measuring carbon footprint in supply chains. Production Planning and Control, 26(4), 265–279.
Moreno-Camacho, C., Montoya-Torres, J., Jaegler, A., & Gondran, N. (2019). Sustainability metrics for real case applications of the supply chain network design problem: A systematic literature review. Journal of Cleaner Production, 231, 600–618. https://doi.org/10.1016/j.jclepro.2019.05.278
Mota, B., Gomes, M. I., Carvalho, A., & Barbosa Póvoa, A. (2018). Sustainable supply chains: An integrated modeling approach under uncertainty. Omega (united Kingdom), 77, 32–57. https://doi.org/10.1016/j.omega.2017.05.006
Murillo-Alvarado, P. E., Guillén-Gosálbez, G., Ponce-Ortega, J. M., Castro-Montoya, A. J., Serna-González, M., & Jiménez, L. (2015). Multi-objective optimization of the supply chain of biofuels from residues of the tequila Industry in Mexico. Journal of Cleaner Production, 108, 422–441. https://doi.org/10.1016/j.jclepro.2015.08.052
Naik, G., & Suresh, D. N. (2018). Challenges of creating sustainable agri-retail supply chains. IIMB Management Review, 30(3), 270–282. https://doi.org/10.1016/j.iimb.2018.04.001
Ngoc, C. T., Xu, X., Kim, H. S., Nguyen, D. A., & You, S. S. (2021). Container port throughput analysis and active management using control theory. Proceedings of the Institution of Mechanical Engineers, Part m: Journal of Engineering for the Maritime Environment. https://doi.org/10.1177/14750902211020875
Nodooshan, K. G., Moraga, R. J., Chen, S. J. G., Nguyen, C., Wang, Z., & Mohseni, S. (2018). Environmental and economic optimization of algal biofuel supply chain with multiple technological pathways. Industrial and Engineering Chemistry Research, 57(20), 6910–6925. https://doi.org/10.1021/acs.iecr.7b02956
Observatorio Sabana Centro. (2019). Informe de calidad de vida 2019. http://sabanacentrocomovamos.org/home/wp-content/uploads/2020/11/Informe-de-Calidad-de-Vida_Sabana-Centro-2019.pdf.
Osmani, A., & Zhang, J. (2017). Multi-period stichastic optimization of a sustainable multi-feedstock second generation bioethanol supply chain—A logistic case study in Midwestern United States. Land Use Policy, 61, 420–450. https://doi.org/10.1016/j.landusepol.2016.10.028
Rabbani, M., Saravi, N. A., Farrokhi-Asl, H., Lim, S. F. W. T., & Tahaei, Z. (2018). Developing a sustainable supply chain optimization model for switchgrass-based bioenergy production: A case study. Journal of Cleaner Production, 200, 827–843. https://doi.org/10.1016/j.jclepro.2018.07.226
Rajeev, A., et al. (2017). Evolution of sustainability in supply chain management: A literature review. Journal of Cleaner Production, 162, 299–314.
Rohmer, S., Gerdessen, J. C., & Claassen, G. D. H. (2019). Sustainable supply chain design in the food system with dietary considerations: A multi-objective analysis. European Journal of Operational Research, 273(3), 1149–1164. https://doi.org/10.1016/j.ejor.2018.09.006
Sellitto, M. A., Hermann, F., Blezs, A. E., Jr., & Barbosa-Póvoa, A. P. (2019). Describing and organizing green practices in the context of green supply chain management: Case studies. Resources, Conservation and Recycling, 145, 1–10.
Stindt, D., Sahamie, R., Nuss, C., & Tuma, A. (2016). How transdisciplinarity can help to improve operations research on sustainable supply chains—A transdisciplinary modeling framework. Journal of Business Logistics, 37(2), 113–131. https://doi.org/10.1111/jbl.12127
Tordecilla-Madera, R., Polo, A., Muñoz, D., & González-Rodríguez, L. (2017). A robust design for a Colombian Dairy Cooperative’s milk storage and refrigeration logistics system using binary programming. International Journal of Production Economics, 183, 710–720. https://doi.org/10.1016/j.ijpe.2016.09.019
Touboulic, A., & Walker, H. (2015). Theories in sustainable supply chain management: A structured literature review. International Journal of Physical Distribution & Logistics Management. https://doi.org/10.1108/IJPDLM-05-2013-0106.
UPME. (2016). Calculadora FECOC 2016 (No. 2016). http://www.upme.gov.co/calculadora_emisiones/aplicacion/calculadora.html.
Urata, T., Yamada, T., Itsubo, N., & Inoue, M. (2017). Global supply chain network design and Asian analysis with material-based carbon emissions and tax. Computers and Industrial Engineering, 113, 779–792. https://doi.org/10.1016/j.cie.2017.07.032
Varsei, M., & Polyakovskiy, S. (2017). Sustainable supply chain network design: A case of the wine industry in Australia. Omega (united Kingdom), 66, 236–247. https://doi.org/10.1016/j.omega.2015.11.009
Wang, C., Li, Y., & Wang, Z. (2018). Supply chain network optimization with consideration of raw material and final product substitutions driven by price and carbon emissions. Kybernetes, 47(8), 1585–1603. https://doi.org/10.1108/K-10-2017-0386
Yakavenka, V., Mallidis, I., Vlachos, D., Iakovou, E., & Eleni, Z. (2019). Development of a multi-objective model for the design of sustainable supply chains: The case of perishable food products. Annals of Operations Research, 294(1), 593–621.
Zakeri, A., Dehghanian, F., Fahimnia, B., & Sarkis, J. (2015). Carbon pricing versus emissions trading: A supply chain planning perspective. International Journal of Production Economics, 164, 197–205. https://doi.org/10.1016/J.IJPE.2014.11.012
Zhang, Y., Jiang, Y., Zhong, M., Geng, N., & Chen, D. (2016). Robust optimization on regional WCO-for-biodiesel supply chain under supply and demand uncertainties. Scientific Programming, 2016, 1–15.
Acknowledgements
This work was supported by a joint doctoral scholarship from Universidad de La Sabana (Grant INGPHD-15-2019) and Kedge Business School, France.
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This work was supported by research grant INGPhD-15-2019 by Universidad de La Sabana, Colombia.
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Moreno-Camacho, C.A., Montoya-Torres, J.R. & Jaegler, A. Sustainable supply chain network design: a study of the Colombian dairy sector. Ann Oper Res 324, 573–599 (2023). https://doi.org/10.1007/s10479-021-04463-9
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DOI: https://doi.org/10.1007/s10479-021-04463-9