Designing a Green Routing Network with an Optimized Heterogeneous Fleet through Constrained Clustering: A Case Study in the Food Industry

Document Type : Research Paper

Authors

1 M.A. in Industrial Management, Production and Operation Management, Department of Management, Faculty of Management and Financial Sciences, Khatam University, Tehran, Iran.

2 Associate Prof., Department of Management, Faculty of Management and Financial Sciences, Khatam University, Tehran, Iran.

3 Assistant Prof., Department of Industrial Engineering, Faculty of Engineering, Khatam University, Tehran, Iran.

Abstract

Objective: This research aims to propose a large-scale vehicle routing model for the distribution network of a food industry product and apply the model in a real-world case study.
Methods: A mathematical model is formulated to minimize the total variable transportation costs.  Considering the complexity of the model, a constrained clustering algorithm is used to decompose the problem. Then, vehicles are assigned to demand clusters according to their capacity. Finally, each cluster's symmetric traveling salesman problem (TSP) is solved using a genetic algorithm. The parameters of the proposed genetic algorithm were calibrated based on its widespread application in solving symmetric TSPs. A conservative approach was adopted to ensure the solution's validity by evaluating a worst-case scenario considering the highest node demands. 
Results: By applying the proposed algorithm to the case study, over 2,000 demand nodes across Tehran were grouped into 91 clusters. Then, based on the demand level of each cluster, the vehicles are assigned, consisting of 26 small and 65 large cars. Within each cluster, the assigned vehicle followed an optimized route among the nodes, designed based on the optimal tour generated by solving the cluster-specific TSP using the genetic algorithm, and then returned to the central warehouse.
Conclusion: Comparing the results with the current situation, the size of the proposed transportation fleet showed a 40% reduction. Additionally, reducing fleet size and optimizing the routes improved the total distribution network costs by 25%. Given the model's computational efficiency, this improvement is considered satisfactory.

Keywords

References

Accorsi, L., & Vigo, D. (2024). A Fast and Scalable Heuristic for the Solution of Large-Scale Capacitated Vehicle Routing Problems. Transportation Science, 55(4), 832–856. https://doi.org/10.1287/trsc.2021.1059
Alesiani, F., Ermis, G., & Gkiotsalitis, K. (2022). Constrained clustering for the capacitated vehicle routing problem (CC-CVRP). Applied Artificial Intelligence, 36(1), 1995658. https://doi.org/10.1080/08839514.2021.1995658
Alzate, P., Londoño, G.A.M., Carrasco, J.M.S., Isaza, G.A., Toro, E.M., & Jaramillo-Garzon, J.A. (2024). Scientific mapping and research perspectives of the vehicle routing problem: An approach from sustainability strategies. Sustainable Futures, 8, 100390. https://doi.org/10.1016/j.sftr.2024.100390.
Andrejić, M., Kilibarda, M., & Pajić, V. (2018). A framework for assessing logistics costs. International Journal of Logistics Management, 27(3), 770-794.
Asgari, A., & Salehi, S. (2023). The role of food industry principles and standards in sustainable development. 7th International Conference on Agricultural Engineering and Environment with a Development Approach, March 15, Tehran, Iran. (in Persian)
Asghari-Zadeh, E., Jafarnejad, A., Zandieh, M., Joybar, S. (2017). Explanation of Traffic Modeling Pattern in Vehicle Routing Problems Based on Green Transportation Paradigm (Case Study: Zamzam Company). Industrial Management Journal, 9(2), 217–244. https://doi.org/10.22059/imj.2017.228099.1007197 (In Persian).
Baker, B.M., & Ayechew, M.A. (2003). A genetic algorithm for the vehicle routing problem. Computers & Operations Research, 30(5), 787-800. https://doi.org/10.1016/S0305-0548(02)00051-5.
Braekers, K., Ramaekers, K., Van Nieuwenhuyse, I. (2016). The vehicle routing problem: state of the art classification and review. Computers & Industrial Engineering, 99, 300-313. https://doi.org/10.1016/j.cie.2015.12.007.
Campbell, A. M., & Wilson, J. H. (2014). Forty years of periodic vehicle routing. Networks, 63(1), 2–15.
https://doi.org/10.1002/net.21527.
Cavaliere, F., Accorsi, L., Laganà, D., Musamanno, R., Vigo, D. (2024). An efficient heuristic for very large-scale vehicle routing problems with simultaneous pickup and delivery. Transportation Research Part E: Logistics and Transportation Review, 186, 103550. https://doi.org/10.1016/j.tre.2024.103550.
Chen, J., Dan, B., & Shi, J. (2020). A variable neighborhood search approach for the multi-compartment vehicle routing problem with time windows considering carbon emission. Journal of Cleaner Production, 277, 123932. https://doi.org/10.1016/j.jclepro.2020.123932.
Eksioglu, B., Vural, A.V., Resiman, A. (2009). The vehicle routing problem: A taxonomic review. Computers & Industrial Engineering, 557(4), 1472-1483. https://doi.org/10.1016/j.cie.2009.05.009.
European Commission report (2023). Field to fork: Global food miles generate nearly 20% of all CO2 emissions from food. Available Online at: https://environment.ec.europa.eu/news/field-fork-global-food-miles-generate-nearly-20-all-co2-emissions-food-2023-01 25_en#:~:text=This%20indicates%20that%20transport%20accounts,GHG)%20released%20during%20their%20production
Ganji, M., Kazemipoor, M., Haji Molana, S.M., & Sajadi, S.M. (2020). Development of Integrated Multi-objective Green Supply Chain Scheduling Model: Production, Distribution and Heterogeneous Vehicle Routing with Customer Time Windows. Industrial Management Journal, 12(1), 47 – 81. https://doi.org/10.22059/imj.2020.294629.1007697 (in Persian).
Gansterer, M., Hartl, R.F. (2018). Collaborative vehicle routing: a survey. European Journal of Operational Research, 268(1), 1–12. https://doi.org/10.1016/j.ejor.2017.10.023.
Giallanza, A., & Puma, G. L. (2020). Fuzzy green vehicle routing problem for designing a three-echelon supply chain. Journal of Cleaner Production, 259, 120774. https://doi.org/10.1016/j.jclepro.2020.120774.
Han, X. (2023). Path Planning Algorithm for the Multiple Depot Vehicle Routing Problem Based on Parallel Clustering. Scientific Programming, 1, 7588595. https://doi.org/10.1155/2023/7588595.
Haripriya, K., Ganesan, V.K. (2022). Solving Large-Scale Vehicle Routing Problems with Hard Time Windows under Travel Time Uncertainty. IFAC-Papers Online, 55(10), 233-238. https://doi.org/10.1016/j.ifacol.2022.09.394.
Hou, Q., Yang, J., Su, Y., ang, X., Deng, Y. (2023). Generalize Learned Heuristics to Solve Large-scale Vehicle Routing Problems in Real-time. The Eleventh International Conference on Learning Representation, 1-37.
Kazemi, M., Mohamadi Zanjirani, D., & Esmaeilian, M. (2021). The Multi-Objective Locating Model for Cross-Docking Centers and Vehicle Routing Scheduling with Split Demands for Perishable Products. Industrial Management Journal, 13(4), 606–633. https://doi.org/10.22059/imj.2022.333499.1007883 (in Persian).
Koç, C., Laporte, G., and Tükenmez, I. (2020). A review of vehicle routing with simultaneous pickup and delivery. Computers & Operations Research, 122, 104987. https://doi.org/10.1016/j.cor.2020.104987.
Konstantakopoulos, G.D., Gayialis, S.P., & Kechagias, E.P. (2022). Vehicle routing problem and related algorithms for logistics distribution: a literature review and classification. Operational Research, 22, 2033-2062. https://doi.org/10.1007/s12351-020-00600-7.
Li, J., Fan, L., Zhang, J., & Ma, H. (2022). A three-phase framework for large-scale vehicle routing problems with real-world applications. Computers & Industrial Engineering, 165, 107938. https://doi.org/10.1016/j.cie.2022.107938.
Li, M., Jia, N., Lenzen, M., Malik, A., Wei, L., Jin, Y. & Raubenheimer, D. (2022). Global food miles account for nearly 20% of total food-system emissions. Nature Food, 3(6): 445–453.  https://doi.org/10.1038/s43016-022-00531-w
Liu, S., Cavalcanti Costa, J.G., Mei, Y., Zhang, M. (2024). GPGLS: Genetic Programming Guided Local Search for Large-Scale Vehicle Routing Problems. In: Affenzeller, M., Winkler, S.M., Konova, A.V., Trautmann, H., Tusar, T., Machado, P., Back, T. (eds.). Parallel Problem Solving from Nature – PPSN XVIII. PPSN 2024. Lecture Notes in Computer Science, Vol 15148, Springer, Cham. https://doi.org/10.1007/978-3-031-70055-2_3.
Mangukiya, R.D., Sklarew, D.M. (2023). Analyzing the three pillars of sustainable development goals at sub-national scales within the USA. World Development Sustainability, 2, 100058. https://doi.org/10.1016/j.wds.2023.100058.
Mensah, J., Casadevall, S.R. 2019. Sustainable development: Meaning, history, principles, pillars, and implications for human action: Literature review. Cogent Social Sciences, 5(1), 1-21. https://doi.org/10.1080/23311886.2019.1653531.
Pandey, P.C., Pandey, M. (2023). Highlighting the role of agriculture and geospatial technology in food security and the Sustainable Development Goals. Sustainable Development, 31(5), 3175–3195. https://doi.org/10.1002/sd.2600.
Poore, J., & Nemecek, T. (2018). Reducing food’s environmental impacts through producers and consumers. Science, 360(6392), 987-992. https://doi.org/10.1126/science.aaq0216.
Prasanna, S., Verma, P., Bodh, S. (2024). The role of food industries in sustainability transition: a review. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-024-04642-1.
Qin, W., Zhuang, Z., Huang, Z., & Huang, H. (2021). A novel reinforcement learning-based hyper-heuristic for the heterogeneous vehicle routing problem. Computers & Industrial Engineering, 156, 107252. https://doi.org/10.1016/j.cie.2021.107252.
Rajaei, M., Moslehi, G., & Reisi-Nafchi, M. (2022). The split heterogeneous vehicle routing problem with three-dimensional loading constraints on a large scale. European Journal of Operational Research, 299(2), 706–721. https://doi.org/10.1016/j.ejor.2021.08.025.
Ruggerio, C.A. (2021). Sustainability and sustainable development: A review of principles and definitions. Science of the Total Environment, 786, 147481. https://doi.org/10.1016/j.scitotenv.2021.147481.
Sar, K., Ghadimi, P. 2023. A systematic literature review of the vehicle routing problem in reverse logistics operations. Computers & Industrial Engineering, 177, 109011. https://doi.org/10.1016/j.cie.2023.109011.
Selvan, T., Panmei, L., Murasing, K.K., Guleria, V., Ramesh, K.R., Bhardwaj, D.R., Thakur, C.L., Kumar, D., Sharma, P., Umedsinh, R.D., Kayalvizhi, D., Deshmukh, H.K. (2023). Circular economy in agriculture: unleashing the potential of integrated organic farming for food security and sustainable development.  Frontiers in Sustainable Food Systems, 7, 01–17. https://doi.org/10.3389/fsufs.2023.1170380.
Sevaux, M, Sörensen. K. (2008). Hamiltonian paths in significantly clustered routing problems. Proceedings of the EU/MEeting 2008 workshop on Metaheuristics for Logistics and Vehicle Routing, EU/ME, 8.
Shi, J. (2024). Optimization of frozen goods distribution logistics network based on k-means algorithm and priority classification. Scientific Reports, 14(1), 22477. https://doi.org/10.1038/s41598-024-72723-2.
Sommerauerova, D., Chocholac, J., Polak, M. 2018. Sustainable distribution logistics of retail chains. Proceedings of the fourth international conference on traffic and transport engineering, 454-459.
Stavropolo, F. 2022. The Consistent Vehicle Routing Problem with the Heterogeneous Fleet. Computers & Operations Research, 140, 105644. https://doi.org/10.1016/j.cor.2021.105644.
Tao, Y., Lin, C., & Wei, L. (2022). Metaheuristics for a Large-Scale Vehicle Routing Problem of Same-Day Delivery in E-Commerce Logistics System. Journal of Advanced Transportation, 2022, 1-15. https://doi.org/10.1155/2022/8253175.
Torabzadeh, S. A., Nejati, E., Aghsami, A., & Rabbani, M. (2022). A dynamic multi-objective green supply chain network design for perishable products in uncertain environments, the coffee industry case study. International Journal of Management Science and Engineering Management, 17(3), 220–237. https://doi.org/10.1080/17509653.2022.2055672.
Toth, P., & Vigo, D. (Eds.). (2002). The vehicle routing problem. Philadelphia: Society for Industrial and Applied Mathematics.
Toth, P., & Vigo, D. (Eds.). (2014). Vehicle routing: problems, methods, and applications. Philadelphia: Society for industrial and applied mathematics.
Valriberas, D. O., Lanzi, E., Klimont, Z., & Van Dingenen, R. (2023). The economic consequences of air pollution policies in Arctic Council countries: A sectoral analysis. Climate and Clean Air Coalition.
Ye, H., Wang, J., Liang, H., Cao, Z., Li, Y. Li, F. 2024. GLOP: Learning Global Partition and Local Construction for Solving Large-Scale Routing Problems in Real-Time. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 20284-20292. https://doi.org/10.1609/aaai.v38i18.30009.
Yağmur, E., Kesen, S.E. 2021. Multi-trip heterogeneous vehicle routing problem coordinated with production scheduling: Memetic algorithm and simulated annealing approaches. Computers & Industrial Engineering, 161, 107649. https://doi.org/10.1016/j.cie.2021.107649
Zhang, M., Pratap, S., Zhao, Z., Prajapati, D., & Huang, G. Q. (2021). Forward and reverse logistics vehicle routing problems with time horizons in B2C e-commerce logistics. International Journal of Production Research, 59(20), 6291–6310. https://doi.org/10.1080/00207543.2020.1812749.
Industrial Management Journal
Volume 17, Issue 2
2025
Pages 175-197

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