Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Integrating Machine Learning and Optimization for Cross-Dock Scheduling in an Online Retail Vehicle Routing Problem

Document Type : Research Paper

Authors
Parastoo Heidarifar 1
Mohammad Mahdi Nasiri 2
Fariborz Jolai 2
Matineh Ziari 2
1 Ph.D. Candidate, Department of Industrial Engineering, Kish International Campus, University of Tehran, Tehran, Iran
2 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Abstract
The advancements in technology coupled with swift changes in modern lifestyles and consumer habits, have resulted in a marked rise in online shopping, that these days represents a considerable share of the global market. Responding to the increasing online demands of the present era, online-retailers are seeking a flexible and cost-effective supply chain which can be optimized by the use of cross-dock facilities. This study investigates the implementation of vehicle routing problem to schedule order delivery for an online-retailer by ensuring timely and precise order fulfillment to improve responsiveness and effectiveness. The applications of machine learning techniques as a cutting-edge technology have improved optimization processes in dynamic real-time conditions. The data used in this research pertains to an online retailer in large-size, in the first phase, by employing machine learning techniques a distance matrix of all nodes is obtained from their geographical longitude and latitude, which is then reduced by clustering algorithm (K-Means). Subsequently, the reduced distance matrix is used in a developed mixed-integer programming model scheduling supply and delivery through cross-dock and vehicle assignment, while taking into account the customers desired time frames, permissible delays, and acceptable service levels for different customer categories. Results of exact solution of this model have confirmed that by changing the key parameters of the model, it is possible to achieve outputs that lead to further profitability and success of an online-retailer.
Keywords
Clustering
Cross-docking
Machine learning
Online-retailer
Scheduling
Vehicle routing
Subjects
Baniamerian, A., Bashiri, M., & Zabihi, F. (2018). Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction. Journal of Industrial Engineering International, 14, 15-30.
Bodnár, B., & Juhász, J. (2023). Examination of the Development Possibilities of the Cross-docking Strategy. Advanced Logistic Systems-Theory and Practice, 17(1), 33-38.
Boysen, N., De Koster, R., & Weidinger, F. (2019). Warehousing in the e-commerce era: A survey. European Journal of Operational Research, 277(2), 396-411.
Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management science, 6(1), 80-91.
Filali, A. E., & Filali, S. E. (2021). Exploring applications of Machine Learning for supply chain management. 2021 Third International Conference on Transportation and Smart Technologies (TST), 
Ghasemi, N., Safavi, A., Saremi, H. R., & Asgary, A. (2022). Assessing the impact of Internet of Things (IoT) on urban multi-modal mobility for optimal routing: A meta-review. International Journal of Transportation Engineering, 10(1), 919-945.
Ghomi, V., Nooraei, S. V. R., Shekarian, N., Shokoohyar, S., & Parast, M. (2023). Improving supply chain resilience through investment in flexibility and innovation. International Journal of Systems Science: Operations & Logistics, 10(1), 2221068.
Ghorbani, E., Alinaghian, M., Gharehpetian, G. B., Mohammadi, S., & Perboli, G. (2020). A survey on environmentally friendly vehicle routing problem and a proposal of its classification. Sustainability, 12(21), 9079.
Gunawan, A., Widjaja, A. T., Vansteenwegen, P., & Yu, V. F. (2022). Two-phase Matheuristic for the vehicle routing problem with reverse cross-docking. Annals of Mathematics and Artificial Intelligence, 90(7), 915-949.
Hasani-Goodarzi, A., & Tavakkoli-Moghaddam, R. (2012). Capacitated vehicle routing problem for multi-product cross-docking with split deliveries and pickups. Procedia-Social and Behavioral Sciences, 62, 1360-1365.
Holliday, J. B., Blount, D., Osaba, E., & Luu, K. (2025). Advanced Quantum Annealing Approach to Vehicle Routing Problems with Time Windows. arXiv preprint arXiv:2503.24285.
Huang, Y.-D., Wu, S., & Yuan, X.-F. (2023). An Optimal Inventory Replenishment Strategy with Cross-docking System and Time Window Problem. International Conference on Genetic and Evolutionary Computing, 
Iklassov, Z., Sobirov, I., Solozabal, R., & Takáč, M. (2024). Reinforcement Learning for Solving Stochastic Vehicle Routing Problem. Asian Conference on Machine Learning, 
Kamble, S. S., Gunasekaran, A., Subramanian, N., Ghadge, A., Belhadi, A., & Venkatesh, M. (2023). Blockchain technology’s impact on supply chain integration and sustainable supply chain performance: Evidence from the automotive industry. Annals of Operations Research, 327(1), 575-600.
Küçükoğlu, İ., & Öztürk, N. (2019). A hybrid meta-heuristic algorithm for vehicle routing and packing problem with cross-docking. Journal of Intelligent Manufacturing, 30, 2927-2943.
Lee, Y. H., Jung, J. W., & Lee, K. M. (2006). Vehicle routing scheduling for cross-docking in the supply chain. Computers & industrial engineering, 51(2), 247-256.
Lima, C., Batista, M., Relvas, S., & Barbosa-Povoa, A. (2023). Optimizing the design and planning of a sugar-bioethanol supply chain under uncertain market conditions. Industrial & Engineering Chemistry Research, 62(15), 6224-6240.
Necula, R., Breaban, M., & Raschip, M. (2017). Tackling dynamic vehicle routing problem with time windows by means of ant colony system. 2017 IEEE Congress on Evolutionary Computation (CEC), 
Nozari, H. (2025). NeuroTwinceutics™ as a Neuromorphic Digital Twin Model for Predictive and Personalized Pharmacotherapy. Transformative Science, 1(1), 1-8. 
Osaba, E., Villar-Rodriguez, E., & Asla, A. (2024). Solving a real-world package delivery routing problem using quantum annealers. Scientific Reports, 14(1), 24791.
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual: A multiple-item scale for measuring consumer perc. Journal of retailing, 64(1), 12.
Pei, P. P.-E., Simchi-Levi, D., & Tunca, T. I. (2011). Sourcing flexibility, spot trading, and procurement contract structure. Operations Research, 59(3), 578-601.
Poullet, J. (2020). Leveraging machine learning to solve The vehicle Routing Problem with Time Windows Massachusetts Institute of Technology].
Qian, C., Zhang, Y., Jiang, C., Pan, S., & Rong, Y. (2020). A real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing. Robotics and Computer-Integrated Manufacturing, 61, 101841.
Rushton, A., Rivett, D., Carlesso, L., Flynn, T., Hing, W., & Kerry, R. (2014). International framework for examination of the cervical region for potential of Cervical Arterial Dysfunction prior to Orthopaedic Manual Therapy intervention. Manual therapy, 19(3), 222-228.
Santos, M. J., Amorim, P., Marques, A., Carvalho, A., & Póvoa, A. (2020). The vehicle routing problem with backhauls towards a sustainability perspective: A review. Top, 28(2), 358-401.
Sippel, L., & Forbes, M. (2024). Enhancements of Fragment Based Algorithms for Vehicle Routing Problems. arXiv preprint arXiv:2411.13151.
Solomon, M. M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research, 35(2), 254-265.
Sultana, N. N., Baniwal, V., Basumatary, A., Mittal, P., Ghosh, S., & Khadilkar, H. (2021). Fast approximate solutions using reinforcement learning for dynamic capacitated vehicle routing with time windows. arXiv preprint arXiv:2102.12088.
Taillard, É., Badeau, P., Gendreau, M., Guertin, F., & Potvin, J.-Y. (1997). A tabu search heuristic for the vehicle routing problem with soft time windows. Transportation science, 31(2), 170-186.
Tirkolaee, E. B., Sadeghi, S., Mooseloo, F. M., Vandchali, H. R., & Aeini, S. (2021). Application of machine learning in supply chain management: a comprehensive overview of the main areas. Mathematical problems in engineering, 2021(1), 1476043.
Wölck, M., & Meisel, S. (2022). Branch-and-price approaches for real-time vehicle routing with picking, loading, and soft time windows. INFORMS Journal on Computing, 34(4), 2192-2211.
Zhang, X., Li, W., Li, R., Fu, Z., Tang, T., Zhang, Z., Chen, W.-Y., Noorshams, N., Jasapara, N., & Ding, X. (2025). Personalized Interpolation: An Efficient Method to Tame Flexible Optimization Window Estimation. arXiv preprint arXiv:2501.14103.
Zhou, Y., Huang, J., Shi, J., Wang, R., & Huang, K. (2021). The electric vehicle routing problem with partial recharge and vehicle recycling. Complex & Intelligent Systems, 7, 1445-1458.

  • Receive Date 09 September 2025
  • Revise Date 16 November 2025
  • Accept Date 15 December 2025