Vehicle routing problem with cross-docking in a sustainable supply chain for perishable products

محورهای موضوعی : Supply chain management and logistics

Fatemeh Shahrabi ¹ , Mohammad Mahdi Nasiri ² , Mohammad Javad Mirzapour ³ , Negin Esmaeelpour ⁴

1 - School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 - School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
3 - Department of Industrial EngineeringIndustrial Production, amirkabir university of technology, tehran iran.
4 - School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

تاریخ دریافت : 1403/05/13 تاریخ پذیرش : 1403/08/01 تاریخ انتشار : 1403/09/08

کلید واژه: Sustainable vehicle routing problem, Cross-docking, Freshness, Job satisfaction, Genetic algorithm, Social responsibility,

چکیده مقاله :

Today's transportation systems, which largely rely on the combustion of fossil fuels, play a significant role in contributing to energy-related greenhouse gas (GHG) emissions, thereby raising serious concerns about sustainability. As awareness of environmental issues grows, incorporating sustainable practices into logistics, particularly in cross-dock scheduling, is becoming increasingly vital. This paper introduces a sustainable vehicle routing problem (VRP) that integrates cross-docking to enhance decision-making within logistics systems. Beyond purely economic considerations, it emphasizes critical aspects like environmental impacts, notably CO2 emissions, and social factors such as equity among drivers and overall customer satisfaction. To tackle these complex challenges, a metaheuristic approach blending Genetic Algorithms (GA) with mixed integer programming (MIP) is proposed as an effective solution strategy. The method's efficacy is validated through various instances of differing sizes, revealing that the GA yields results with minimal deviation from optimal fitness values in smaller instances. Additionally, a comprehensive real case study is conducted to showcase the model's applicability in practical scenarios and finally, some suggestions for further researches are given. This study not only illustrates the operational benefits of the proposed approach but also underscores the importance of sustainable logistics in mitigating environmental impacts while fostering social equity and enhancing customer experience.

چکیده انگلیسی:

Today's transportation systems, which largely rely on the combustion of fossil fuels, play a significant role in contributing to energy-related greenhouse gas (GHG) emissions, thereby raising serious concerns about sustainability. As awareness of environmental issues grows, incorporating sustainable practices into logistics, particularly in cross-dock scheduling, is becoming increasingly vital. This paper introduces a sustainable vehicle routing problem (VRP) that integrates cross-docking to enhance decision-making within logistics systems. Beyond purely economic considerations, it emphasizes critical aspects like environmental impacts, notably CO2 emissions, and social factors such as equity among drivers and overall customer satisfaction. To tackle these complex challenges, a metaheuristic approach blending Genetic Algorithms (GA) with mixed integer programming (MIP) is proposed as an effective solution strategy. The method's efficacy is validated through various instances of differing sizes, revealing that the GA yields results with minimal deviation from optimal fitness values in smaller instances. Additionally, a comprehensive real case study is conducted to showcase the model's applicability in practical scenarios and finally, some suggestions for further researches are given. This study not only illustrates the operational benefits of the proposed approach but also underscores the importance of sustainable logistics in mitigating environmental impacts while fostering social equity and enhancing customer experience.

منابع و مأخذ:

Abad, H. K. E., Vahdani, B., Sharifi, M., & Etebari, F. (2018). A bi-objective model for pickup and delivery pollution-routing problem with integration and consolidation shipments in cross-docking system. Journal of Cleaner Production.
Abdullahi, H., Reyes-Rubiano, L., Ouelhadj, D., Faulin, J., & Juan, A. A. (2021). Modelling and multi-criteria analysis of the sustainability dimensions for the green vehicle routing problem. European Journal of Operational Research, 292(1), 143-154.
Afshar-Bakeshloo, M., Mehrabi, A., Safari, H., Maleki, M., & Jolai, F. (2016). A green vehicle routing problem with customer satisfaction criteria. Journal of Industrial Engineering International, 12(4), 529-544.
Alamatsaz, K., Ahmadi, A., & Mirzapour Al-e-hashem, S. M. J. (2021). A multiobjective model for the green capacitated location-routing problem considering drivers’ satisfaction and time window with uncertain demand. Environmental Science and Pollution Research, 1-20.
Arbabi, H., Nasiri, M. M., & Bozorgi-Amiri, A. (2021). A hub-and-spoke architecture for a parcel delivery system using the cross-docking distribution strategy. Engineering Optimization, 53(9), 1593-1612. doi:10.1080/0305215X.2020.1808973
Bandeira, J. M., Guarnaccia, C., Fernandes, P., & Coelho, M. C. (2018). Advanced impact integration platform for cooperative road use. International journal of intelligent transportation systems research, 16(1), 1-15.
Bektaş, T., & Laporte, G. (2011). The Pollution-Routing Problem. Transportation Research Part B: Methodological, 45(8), 1232-1250. doi:https://doi.org/10.1016/j.trb.2011.02.004
Benjaafar, S., Li, Y., & Daskin, M. (2012). Carbon footprint and the management of supply chains: Insights from simple models. IEEE transactions on automation science and engineering, 10(1), 99-116.
Brundland, G. (1987). World Commission on Environment and Development. Our Common Future Oxford. In: University Press.. Oxford.
Chan, F. T., Wang, Z., Goswami, A., Singhania, A., & Tiwari, M. K. (2020). Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations. International Journal of Production Research, 58(17), 5155-5174.
Chargui, T., Bekrar, A., Reghioui, M., & Trentesaux, D. (2020). Scheduling trucks and storage operations in a multiple-door cross-docking terminal considering multiple storage zones. International Journal of Production Research, 1-25. doi:10.1080/00207543.2020.1853843
Ćirović, G., Pamučar, D., & Božanić, D. (2014). Green logistic vehicle routing problem: Routing light delivery vehicles in urban areas using a neuro-fuzzy model. Expert Systems with Applications, 41(9), 4245-4258.
Galindres, L. F., Guimarães, F. G., & Gallego-Rendón, R. A. (2023). Multi-objective sustainable capacitated location routing problem formulation in sustainable supply-chain management. Engineering Optimization, 55(3), 526-541.
Govindan, K., Jafarian, A., & Nourbakhsh, V. (2015). Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic. Computers & Operations Research, 62, 112-130.
Govindan, K., Jafarian, A., & Nourbakhsh, V. (2019). Designing a sustainable supply chain network integrated with vehicle routing: A comparison of hybrid swarm intelligence metaheuristics. Computers & Operations Research, 110, 220-235.
Hamedirostami, A., Goli, A., & Gholipour-Kanani, Y. (2022). Green cross-dock based supply chain network design under demand uncertainty using new metaheuristic algorithms. Journal of Industrial and Management Optimization, 18(5), 3103-3131.
Hosseini-Nasab, H., & Lotfalian, P. (2017). Green routing for trucking systems with classification of path types. Journal of Cleaner Production, 146, 228-233.
Nasiri, M. M., Rahbari, A., Werner, F., & Karimi, R. (2018). Incorporating supplier selection and order allocation into the vehicle routing and multi-cross-dock scheduling problem. International Journal of Production Research, 56(19), 6527-6552.
Peng, X.-s., Ji, S.-f., & Ji, T.-t. (2020). Promoting sustainability of the integrated production-inventory-distribution system through the Physical Internet. International Journal of Production Research, 58(22), 6985-7004.
Qiao, Q., Tao, F., Wu, H., Yu, X., & Zhang, M. (2020). Optimization of a capacitated vehicle routing problem for sustainable municipal solid waste collection management using the PSO-TS algorithm. International journal of environmental research and public health, 17(6), 2163.
Rahbari, A., Nasiri, M. M., Werner, F., Musavi, M., & Jolai, F. (2019). The vehicle routing and scheduling problem with cross-docking for perishable products under uncertainty: Two robust bi-objective models. Applied Mathematical Modelling, 70, 605-625.
Rahimi, M., Baboli, A., & Rekik, Y. (2016). Sustainable inventory routing problem for perishable products by considering reverse logistic. IFAC-PapersOnLine, 49(12), 949-954.
Ramos, T. R. P., Gomes, M. I., & Barbosa-Póvoa, A. P. (2014). Planning a sustainable reverse logistics system: Balancing costs with environmental and social concerns. Omega, 48, 60-74.
Reyes-Rubiano, L., Calvet, L., Juan, A. A., Faulin, J., & Bové, L. (2018). A biased-randomized variable neighborhood search for sustainable multi-depot vehicle routing problems. Journal of Heuristics, 1-22.
Rezaei, S., & Kheirkhah, A. (2018). A comprehensive approach in designing a sustainable closed-loop supply chain network using cross-docking operations. Computational and Mathematical Organization Theory, 24(1), 51-98.
Santos, M. J., Martins, S., Amorim, P., & Almada-Lobo, B. (2021). A green lateral collaborative problem under different transportation strategies and profit allocation methods. Journal of Cleaner Production, 288, 125678.
Shahabi-Shahmiri, R., Asian, S., Tavakkoli-Moghaddam, R., Mousavi, S. M., & Rajabzadeh, M. (2021). A routing and scheduling problem for cross-docking networks with perishable products, heterogeneous vehicles and split delivery. Computers & Industrial Engineering, 157, 107299.
Shahedi, A., Nasiri, M. M., Sangari, M. S., Werner, F., & Jolai, F. (2021). A Stochastic Multi-Objective Model for a Sustainable Closed-Loop Supply Chain Network Design in the Automotive Industry. Process Integration and Optimization for Sustainability, 1-21.
Shahrabi, F., Tavakkoli-Moghaddam, R., Triki, C., Pahlevani, M., & Rahimi, Y. (2022). Modelling and solving the bi-objective production–transportation problem with time windows and social sustainability. IMA Journal of Management Mathematics, 33(4), 637-662.
Song, B. D., & Ko, Y. D. (2016). A vehicle routing problem of both refrigerated-and general-type vehicles for perishable food products delivery. Journal of food engineering, 169, 61-71.
Tabatabaei, S. M., Safi, M., & Shafiei Nikabadi, M. (2021). A mathematical model for scheduling of transportation, routing, and cross-docking in the reverse logistics network of the green supply chain. International Journal of Nonlinear Analysis and Applications, 12(2), 1909-1927.
Tavakkoli-Moghaddam, R., & Raziei, Z. (2016). A new bi-objective location-routing-inventory problem with fuzzy demands. IFAC-PapersOnLine, 49(12), 1116-1121.
Theophilus, O., Dulebenets, M. A., Pasha, J., Lau, Y.-y., Fathollahi-Fard, A. M., & Mazaheri, A. (2021). Truck scheduling optimization at a cold-chain cross-docking terminal with product perishability considerations. Computers & Industrial Engineering, 156, 107240.
Tirkolaee, E. B., Goli, A., Faridnia, A., Soltani, M., & Weber, G.-W. (2020). Multi-objective optimization for the reliable pollution-routing problem with cross-dock selection using Pareto-based algorithms. Journal of Cleaner Production, 276, 122927.
Yin, P.-Y., & Chuang, Y.-L. (2016). Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking. Applied Mathematical Modelling, 40(21), 9302-9315. doi:https://doi.org/10.1016/j.apm.2016.06.013
Yin, P.-Y., Lyu, S.-R., & Chuang, Y.-L. (2016). Cooperative coevolutionary approach for integrated vehicle routing and scheduling using cross-dock buffering. Engineering Applications of Artificial Intelligence, 52, 40–53.
Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M. (2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 89, 182-214.
Zhu, L., & Hu, D. (2019). Study on the vehicle routing problem considering congestion and emission factors. International Journal of Production Research, 57(19), 6115-6129.