Abstract
Literature research urban logistics has shown us the growing interest in sustainable supply chain serving the population, which must necessarily be based on location and routing of vehicles in order, using the integer linear programming methodology, to model these cases. This study presents a sustainable four-level bi-objective model to optimize economic costs and measure the amount of carbon dioxide generated by the transportation process. The process consists of a provider that supplies a distribution center or warehouse, which also serves a group of facilities located by a group of customers who expect to receive attention to their two orders, one for the purchase of the acquired product and the other for the delivery of the waste generated. Finally, the waste is transported to specialized centers. The model implemented with GLPK obtained optimal results for small and medium-sized instances. For scenarios with more than 40 customers, it was not possible to find solutions, as the computational processing time limit was exceeded by 7 200 s, for these scenarios it is recommended to solve the model using metaheuristics.
Our gratitude to the Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica - Fondecyt, for financing the research in progress.
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References
Akyüz, M.H., Öncan, T., Altınel, İK.: Branch and bound algorithms for solving the multi-commodity capacitated multi-facility Weber problem. Ann. Oper. Res., 1–42 (2018). https://doi.org/10.1007/s10479-018-3026-5
Bouchery, Y., Corbett, C.J., Fransoo, J.C., Tan, T. (eds.): Sustainable Supply Chains. SSSCM, vol. 4. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-29791-0
Bugliarello, G.: Urban sustainability: Dilemas, challenges and paradigms. Technol. Soc. 28(1–2), 19–26 (2006). https://doi.org/10.1016/j.techsoc.2005.10.018
Chen, L., Olhager, J., Tang, O.: Manufacturing facility location and sustainability: a literature review and research agenda. Int. J. Prod. Econ. 149, 154–163 (2014). https://doi.org/10.1016/j.ijpe.2013.05.013
Daskin, M. S., Snyder, L V., Berger, R. T.: Facility Location in Supply Chain Design, chapter 2. Logistics systems: Design and optimization. In: Langevin , A., Riopel, D. (eds.) GERAD & École Polytechnique de Montréal Montréal Canada. Springer, Boston, MA (2005). https://doi.org/10.1007/0-387-24977-X_2
Eguia, I., Racero, J., Molina, J. C., Guerrero, F.: Environmental Issues in Vehicle Routing Problems. In: Erechtchoukova M., Khaiter P., Golinska P. (eds) Sustainability Appraisal: Quantitative Methods and Mathematical Techniques for Environmental Performance Evaluation. EcoProduction (Environmental Issues in Logistics and Manufacturing). Springer, Hidelberg (2013). https://doi.org/10.1007/978-3-642-32081-1_10
Fan, D., Lo, C.K.Y., Zhou, Y.: Sustainability risk in supply bases: the role of complexity and coupling. Transp. Res. Part E 145(2021) (2021). https://doi.org/10.1016/j.tre.2020.102175
Gonzales-Feliu, J., Morana, J.: Are city logistics solutions sustainable? the cityporto case. J. Land Use Mob. Environ. 3(2), 55–64 (2010)
Li, F., Golden, B., Wasil, E.: The open vehicle routing problema: algorithms, large-scale test problems, and computational results. Comput. Oper. Res. 34(10), 2918–2930 (2007). https://doi.org/10.1016/j.cor.2005.11.018
Liu, W., Kong, N., Wang, M., Zhang, L.: Sustainable multi-commodity capacitated facility location problem with complementary demand functions. Transp. Res. Part E 145(2) (2021). https://doi.org/10.1016/j.tre.2020.102165
Okewu, E., Misra, S., Maskeliūnas, R., Damaševičius, R., Fernandez-Sanz, L.: Optimizing green computing awareness for environmental sustainability and economic security as a stochastic optimization problem. Sustainability, MDPI 9(10) (2017). https://doi.org/10.3390/su9101857
Okewu, E., Ananya, M., Misra, S., Koyuncu, M.: A deep neural network-based advisory framework for attainment of sustainable development goals 1–6. Sustainability, MDPI 12(24) (2020). https://doi.org/10.3390/su122410524
Ombuki, B., Ross, B., Hanshar, F.T.: Multi-objetive genetic algorithms for vehicle routing problem with time windows. Appl. Intell. 24(1), 17–30 (2006). https://doi.org/10.1007/s10489-006-6926-z
Ouhader, H., El Kyal, M.: Combining facility location and routing decisions in sustainable urban freight distribution under horizontal collaboration: how can shippers be benefited? Math. Probl. Eng. Hindawi 2017, 1–18 (2017). https://doi.org/10.1155/2017/8687515
Peng, B., Wu, L., Yi, Y., Chen, X.: Solving the multi-depot Green vehicle routing problem by a hybrid evolutionary algorithm. Sustainability, MDPI 12(5) (2020). https://doi.org/10.3390/su12052127
Prodhon, C., Prins, C.: A survey of recent research on location-routing problems. Europ. J. Oper. Res. 238(1), 1–17 (2014). https://doi.org/10.1016/j.ejor.2014.01.005
Rabbani, M., Navazi, F., Farrokhi-Asl, H., Balali, M.H.: A sustainable transportation-location-routing problem with soft time windows for distribution systems. Uncertain Supply Chain Management, Publishers of distinguished academic and professional journals 6(3), 229–254 (2018). https://doi.org/10.5267/j.uscm.2017.12.002
Rabbani, M., Taghi-Molla, A., Farrokhi-Asl, H., Mobini, M.: Sustainable vehicle-routing problem with time Windows by heterogeneous fleet of vehicle and separated compartments: application in waste collection problem. Int. J. Transp. Eng. 7(2), 195–216 (2019). https://doi.org/10.22119/IJTE.2019.94586.1361
Santibañez, E. D. R., Mateus, G. R., Luna, H. P. L.: Solving a public sector sustainable supply chain problem: a genetic algorithm approach. In: WCAMA Brazilian Computer Society Proceedings, pp. 19–22. Publisher, Natal, RN, Brazil (2011)
Subramanian, A., Uchoa, E., Ochi, L.S.: New lower bounds for the vehicle routing problem with simultaneous pickup and delivery. In: Festa, P. (ed.) SEA 2010. LNCS, vol. 6049, pp. 276–287. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13193-6_24
Sungur, I., Ordoñez, F., Dessouky, M.: A robust optimization approach for the capacitated vehicle routing problem with demand uncertainty. IIE Trans. 40(5), 509–523 (2008). https://doi.org/10.1080/07408170701745378
Tanguay, G.A., Rajaonson, J., Lefevre, J., Lanoie, P.: Measuring the sustainability of cities: an analysis of the use of local indicators. Ecological Indiators 10(2), 407–418 (2010). https://doi.org/10.1016/j.ecolind.2009.07.013
Tang, J., Ji, S., Jiang, L.: The design of a sustainable location-routing-inventory model considering consumer environmental behavior. Sustainability, MDPI 8(3) (2016). https://doi.org/10.3390/su8030211
Tsao, Y., Thanh, V.: A multi-objective mixed robust possibilistic flexible programming approach for sustainable seaport-dry port network design under an uncertain environment. Transp. Res. Part E: Logistics Transp. Rev. 124, 13–39 (2019). https://doi.org/10.1016/j.tre.2019.02.006
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Rodriguez-Melquiades, J., Lujan, E., Segura, F.G. (2021). Sustainable Optimization Model for Routing the Process of Distribution of Products, Pickup and Transport of Waste in the Context of Urban Logistics. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12952. Springer, Cham. https://doi.org/10.1007/978-3-030-86973-1_7
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