Digital twin applications in urban logistics: an overview

Figures & data

Figure 1. Trend of research of DT applications in Urban Logistics up to May 2023.

Figure 2. General technical anatomy of an urban logistics DT based on Belfadel et al. (2021).

Table 1. Use cases of Urban Logistics DT in literature.

Paper	Application
Gutierrez-Franco et al. (2021)	Urban distribution by a retailer in a developing mega-city
Zdolsek Draksler et al. (2023)	cross-border postal-logistics
Shen et al. (2022)	Tobacco Supply Chain
Vallejo et al. (2021)	Urban food supply chain for carbon food-print reduction
Miao and Lan (2021)	Image processing for intelligent logistics distribution management
Liu et al. (2021)	Freight Parking Management
Ghandar et al. (2021)	Urban agriculture and distribution.

Figure 3. Our proposed high-level urban logistics DT ontology.

Figure 4. Our proposed ontology for the AI component in Figure 3.

Table 2. Sample of the DDO applications in VRP from literature. We also mention the VRP variants they deals with and the associated learning and optimization techniques.

Paper	Problem	Methods
James et al. (2019)	green logistic system online routing	reinforcement learning, combinatorial optimization
Niu et al. (2021)	multi-objective stochastic VRP	hypothesis generation, genetic algorithm
Niu et al. (2022)	multi-objective stochastic VRP	hypothesis generation, genetic algorithm
Joe and Lau (2020)	stochastic VRP	reinforcement learning, genetic algorithm
Morabit et al. (2021)	VRP with time windows	supervised learning, branch-and-price
Furian et al. (2021)	capacitated VRP	supervised learning, branch-and-price
Cooray and Rupasinghe (2017)	energy minimizing VRP	unsupervised learning, genetic algorithm
da Costa et al. (2021)	standard VRP	reinforcement learning, local search
Nazari et al. (2018)	capacitated VRP	reinforcement learning, combinatorial optimization
Hottung and Tierney (2019)	split-delivery/capacitated VRP	reinforcement learning, local search
J. Zhao et al. (2020)	standard VRP/with time windows	reinforcement learning, local search

Table 3. Entity specification for an urban logistics DT for an LSP carrying out urban distribution. The AI component is explained below.

Entity	Instances
Stakeholders	LSPs, customers, truck operators
Resources	Trucks
Measures	driving times, time windows
KPIs	total driving time, number of vehicles used, average number of stops per vehicle
Decisions	Routing order among customers; order cancellation
Data	customer locations, customer demands, traffic data etc.

Algorithm 1 Pseudo-code to solve CVRPTW1.

1: Input: $\mathcal{N}$, ${\mathcal{C}}^{\mathcal{N}}$, $\mathcal{T}$.

2: Let $\bar{R}=\mathrm{\varnothing }.$

3: Construct initial solution for CVRPTW1.

4:while termination criterion for optimization is not met do

5: Use reinforcement learning agent to evaluate actions.

6: Use actions to search for candidate solutions locally

7: Generate candidate solutions $R$ and add them to $\bar{R}$.

8: end while

9: for solution $R$ in $\bar{R}$ do

10: verify $R$ using simulation

11: if simulation objective of $R$ is better than $R^{\ast }$ then

12: $R^{\ast }=R.$

13: end if

14: end for

15: return $R^{\ast }$

Figure 5. Example pipeline for solving CVRPTW1.

Figure 6. Data flow in reinforcement learning in a DT. Transition data contains current and succeeding states $S_t$ and $S_{t+1}$, actions $A$, estimated and actual rewards $\hat{R}$ and $R$.

Belfadel, A., Hörl, S., Tapia, R. J., & Puchinger, J. (2021). Towards a digital twin framework for adaptive last mile city logistics. In 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia, (pp. 1–6). IEEE.

 Google Scholar

Gutierrez-Franco, E., Mejia-Argueta, C., & Rabelo, L. (2021). Data-driven methodology to support long-lasting logistics and decision making for urban last-mile operations. Sustainability, 13(11), 6230. https://doi.org/10.3390/su13116230

 Web of Science ®Google Scholar

Zdolsek Draksler, T., Cimperman, M., & Obrecht, M. (2023). Data-driven supply chain operations—the pilot case of postal logistics and the cross-border optimization potential. Sensors, 23(3), 1624. https://doi.org/10.3390/s23031624

 PubMed Web of Science ®Google Scholar

Shen, X., Zhang, Y., Tang, Y., Qin, Y., Liu, N., & Yi, Z. (2022). A study on the impact of digital tobacco logistics on tobacco supply chain performance: Taking the tobacco industry in Guangxi as an example. Industrial Management & Data Systems, 122(6), 1416–1452. https://doi.org/10.1108/IMDS-05-2021-0270

 Web of Science ®Google Scholar

Vallejo, M. E., Larios, V. M., Magallanes, V. G., Cobián, C., Castañeda, M. D. L. L. G., & Téllez, G. B. (2021). Creating resilience for climate change in smart cities based on the local food supply chain. In 2021 IEEE International Smart Cities Conference (ISC2) (pp. 1–7). IEEE.

 Google Scholar

Miao, J., & Lan, S. (2021). Application of visual sensing image processing technology under digital twins to the intelligent logistics system. Advances in Civil Engineering, 2021, 1–13. https://doi.org/10.1155/2021/5743387

 Web of Science ®Google Scholar

Liu, Y., Folz, P., Pan, S., Ramparany, F., Bolle, S., Ballot, E., & Coupaye, T. (2021). Digital twin-driven approach for smart city logistics: The case of freight parking management. In Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems: IFIP WG 5.7 International Conference, APMS 2021, Nantes, France, September 5–9, 2021, Proceedings, Part IV (pp. 237–246). Springer.

 Google Scholar

Ghandar, A., Ahmed, A., Zulfiqar, S., Hua, Z., Hanai, M., & Theodoropoulos, G. (2021). A decision support system for urban agriculture using digital twin: A case study with aquaponics. IEEE Access, 9, 35691–35708. https://doi.org/10.1109/ACCESS.2021.3061722

 Web of Science ®Google Scholar

James, J., Yu, W., & Gu, J. (2019). Online vehicle routing with neural combinatorial optimization and deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems, 20(10), 3806–3817. https://doi.org/10.1109/TITS.2019.2909109

 Web of Science ®Google Scholar

Niu, Y., Kong, D., Wen, R., Cao, Z., & Xiao, J. (2021). An improved learnable evolution model for solving multi-objective vehicle routing problem with stochastic demand. Knowledge-Based Systems, 230, 107378. https://doi.org/10.1016/j.knosys.2021.107378

 Web of Science ®Google Scholar

Niu, Y., Shao, J., Xiao, J., Song, W., & Cao, Z. (2022). Multi-objective evolutionary algorithm based on rbf network for solving the stochastic vehicle routing problem. Information Sciences, 609, 387–410. https://doi.org/10.1016/j.ins.2022.07.087

 Web of Science ®Google Scholar

Joe, W., & Lau, H. C. (2020). Deep reinforcement learning approach to solve dynamic vehicle routing problem with stochastic customers. In Proceedings of the international Conference on Automated Planning and Scheduling, Nancy, France, 30, (pp. 394–402).

 Google Scholar

Morabit, M., Desaulniers, G., & Lodi, A. (2021). Machine-learning–based column selection for column generation. Transportation Science, 55(4), 815–831. https://doi.org/10.1287/trsc.2021.1045

 Web of Science ®Google Scholar

Furian, N., O’Sullivan, M., Walker, C., & Çela, E. (2021). A machine learning-based branch and price algorithm for a sampled vehicle routing problem. OR Spectrum, 43(3), 693–732. https://doi.org/10.1007/s00291-020-00615-8

 Web of Science ®Google Scholar

Cooray, P., & Rupasinghe, T. D. (2017). Machine learning-based parameter tuned genetic algorithm for energy minimizing vehicle routing problem. Journal of Industrial Engineering, 2017, 2017. https://doi.org/10.1155/2017/3019523

 Google Scholar

Da Costa, P., Rhuggenaath, J., Zhang, Y., Akcay, U., & Alp Anwd Kaymak. (2021). Learning 2-opt heuristics for routing problems via deep reinforcement learning. SN Computer Science, 2(5), 388. https://doi.org/10.1007/s42979-021-00779-2

 Google Scholar

Nazari, M., Oroojlooy, A., Snyder, L., & Takác, M. (2018). Reinforcement learning for solving the vehicle routing problem. Advances in Neural Information Processing Systems 31.

 Google Scholar

Hottung, A., & Tierney, K. (2019). Neural large neighborhood search for the capacitated vehicle routing problem. arXiv preprint arXiv:1911.09539.

 Google Scholar

Zhao, J., Mao, M., Zhao, X., & Zou, J. (2020). A hybrid of deep reinforcement learning and local search for the vehicle routing problems. IEEE Transactions on Intelligent Transportation Systems, 22(11), 7208–7218. https://doi.org/10.1109/TITS.2020.3003163

 Web of Science ®Google Scholar