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Freeway logistics plays a pivotal role in economic development. Although the rapid development in big data and artificial intelligence motivates long-haul freeway logistics towards informatization and intellectualization, the transportation of bulk commodities still faces serious challenges arisen from dispersed freight demands and the lack of co-ordination among different operators. The present study thereby proposed intelligent algorithms for truck dispatching for freeway logistics. Specifically, our contributions include the establishment of mathematical models for full-truckload (FTL) and less-than-truckload (LTL) transportation modes, respectively, and the introduction of reinforcement learning with deep Q-networks tailored for each transportation mode to improve the decision-making in order acceptance and truck repositioning. Simulation experiments based on the real-world freeway logistics data collected in Guiyang, China show that our algorithms improved operational profitability substantially with a 76% and 30% revenue increase for FTL and LTL modes, respectively, compared with single-stage optimization. These results demonstrate the potential of reinforcement learning in revolutionizing freeway logistics and should lay a foundation for future research in intelligent logistics systems.
R. Kaewpuang, D. Niyato, P. S. Tan, and P. Wang, Cooperative management in full-truckload and less-than-truckload vehicle system, IEEE Trans. Veh. Technol., vol. 66, no. 7, pp. 5707–5722, 2017.
H. W. Chang, Y. C. Tai, and J. Y. J. Hsu, Context-aware taxi demand hotspots prediction, Int. J. Bus. Intell. Data Min., vol. 5, no. 1, pp. 3–18, 2010.
Y. Li, J. Lu, L. Zhang, and Y. Zhao, Taxi booking mobile app order demand prediction based on short-term traffic forecasting, Transp. Res. Rec. J. Transp. Res. Board, vol. 2634, no. 1, pp. 57–68, 2017.
L. Moreira-Matias, J. Gama, M. Ferreira, J. Mendes-Moreira, and L. Damas, Predicting taxi–passenger demand using streaming data, IEEE Trans. Intell. Transp. Syst., vol. 14, no. 3, pp. 1393–1402, 2013.
J. Huo, C. Liu, J. Chen, Q. Meng, J. Wang, and Z. Liu, Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach, Transp. Res. Part E Logist. Transp. Rev., vol. 173, p. 103108, 2023.
F. Rodrigues, I. Markou, and F. C. Pereira, Combining time-series and textual data for taxi demand prediction in event areas: A deep learning approach, Inf. Fusion, vol. 49, pp. 120–129, 2019.
L. Liu, Z. Qiu, G. Li, Q. Wang, W. Ouyang, and L. Lin, Contextualized spatial–temporal network for taxi origin-destination demand prediction, IEEE Trans. Intell. Transp. Syst., vol. 20, no. 10, pp. 3875–3887, 2019.
D. H. Lee, H. Wang, R. L. Cheu, and S. H. Teo, Taxi dispatch system based on current demands and real-time traffic conditions, Transp. Res. Rec. J. Transp. Res. Board, vol. 1882, no. 1, pp. 193–200, 2004.
H. Billhardt, A. Fernández, S. Ossowski, J. Palanca, and J. Bajo, Taxi dispatching strategies with compensations, Expert Syst. Appl., vol. 122, pp. 173–182, 2019.
K. Braekers, K. Ramaekers, and I. Van Nieuwenhuyse, The vehicle routing problem: State of the art classification and review, Comput. Ind. Eng., vol. 99, pp. 300–313, 2016.
Y. Bengio, A. Lodi, and A. Prouvost, Machine learning for combinatorial optimization: A methodological tourd’horizon, Eur. J. Oper. Res., vol. 290, no. 2, pp. 405–421, 2021.
F. Miao, S. Han, S. Lin, J. A. Stankovic, D. Zhang, S. Munir, H. Huang, T. He, and G. J. Pappas, Taxi dispatch with real-time sensing data in metropolitan areas: A receding horizon control approach, IEEE Trans. Autom. Sci. Eng., vol. 13, no. 2, pp. 463–478, 2016.
M. Lowalekar, P. Varakantham, and P. Jaillet, Online spatio-temporal matching in stochastic and dynamic domains, Artif. Intell., vol. 261, pp. 71–112, 2018.
J. F. Cordeau and G. Laporte, The dial-a-ride problem (DARP): Variants, modeling issues and algorithms, Q. J. Belg. Fr. Ital. Oper. Res. Soc., vol. 1, no. 2, pp. 89–101, 2003.
Y. Li and Y. Liu, Optimizing flexible one-to-two matching in ride-hailing systems with boundedly rational users, Transp. Res. Part E Logist. Transp. Rev., vol. 150, p. 102329, 2021.
J. Wang, X. Wang, S. Yang, H. Yang, X. Zhang, and Z. Gao, Predicting the matching probability and the expected ride/shared distance for each dynamic ridepooling order: A mathematical modeling approach, Transp. Res. Part B Methodol., vol. 154, pp. 125–146, 2021.
A. Schulz and C. Pfeiffer, A Branch-and-Cut algorithm for the dial-a-ride problem with incompatible customer types, Transp. Res. Part E Logist. Transp. Rev., vol. 181, p. 103394, 2024.
X. Cheng, S. Fu, J. Sun, M. Zuo, and X. Meng, Trust in online ride-sharing transactions: Impacts of heterogeneous order features, J. Manag. Inf. Syst., vol. 40, no. 1, pp. 183–207, 2023.
S. Feng, P. Duan, J. Ke, and H. Yang, Coordinating ride-sourcing and public transport services with a reinforcement learning approach, Transp. Res. Part C Emerg. Technol., vol. 138, p. 103611, 2022.
Z. T. Qin, X. Tang, Y. Jiao, F. Zhang, Z. Xu, H. Zhu, and J. Ye, Ride-hailing order dispatching at DiDi via reinforcement learning, Inf. J. Appl. Anal., vol. 50, no. 5, pp. 272–286, 2020.
Z. Zhu, J. Ke, and H. Wang, A mean-field Markov decision process model for spatial-temporal subsidies in ride-sourcing markets, Transp. Res. Part B Methodol., vol. 150, pp. 540–565, 2021.
A. O. Al-Abbasi, A. Ghosh, and V. Aggarwal, DeepPool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning, IEEE Trans. Intell. Transp. Syst., vol. 20, no. 12, pp. 4714–4727, 2019.
T. Abeywickrama, V. Liang, and K. L. Tan, Optimizing bipartite matching in real-world applications by incremental cost computation, Proc. VLDB Endow., vol. 14, no. 7, pp. 1150–1158, 2021.
H. Hosni, J. Naoum-Sawaya, and H. Artail, The shared-taxi problem: Formulation and solution methods, Transp. Res. Part B Methodol., vol. 70, pp. 303–318, 2014.
B. Ghaddar, J. Naoum-Sawaya, A. Kishimoto, N. Taheri, and B. Eck, A Lagrangian decomposition approach for the pump scheduling problem in water networks, Eur. J. Oper. Res., vol. 241, no. 2, pp. 490–501, 2015.
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