Abstract
In the attended home delivery industry, customers order products online, which are then delivered to their home within a time slot of their choice. Given the nature of the products, e.g., groceries or appliances, the customer must be at home to receive the goods. Therefore, on-time delivery is of key importance. Due to the inherent variability of various planning factors, such as uncertain travel times and time to deliver an order, gaps appear between route planning and execution of the delivery routes. Additionally, during the planning process additional customers may order and choose new time slots. This fuels the need for data-driven approaches that can account for these uncertainties. In this paper, we present an overview of ORTEC applications that use machine learning combined with route planning, to create accurate, yet efficient and robust route plans. To validate the impact of the proposed approaches in practice, the machine learning models are trained and tested on real-world data. Simulation experiments highlight the impact and value created by such approaches for home delivery companies.
Wouter Merkx and Ruggiero Seccia ā no longer work for ORTEC, Work done while at ORTEC.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agatz, N., Fan, Y., Stam, D.: The impact of green labels on time slot choice and operational sustainability. Prod. Oper. Manag. 30(7), 2285ā2303 (2021)
Agatz, N., Campbell, A.M., Fleischmann, M., Savels, M.: Challenges and opportunities in attended home delivery. In: Golden, B., Raghavan, S., Wasil, E. (eds.) The Vehicle Routing Problem: Latest Advances and New Challenges. ORCS, vol. 43, pp. 379ā396. Springer, Boston (2008). https://doi.org/10.1007/978-0-387-77778-8_17
Akamai: Akamai Online Retail Performance Report: Milliseconds Are Critical (2017). https://www.akamai.com/uk/en/about/news/press/2017-press/akamai-releases-spring-2017-state-of-online-retail-performance-report.jsp
Bogaerts, T., Masegosa, A.D., Angarita-Zapata, J.S., Onieva, E., Hellinckx, P.: A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data. Transp. Res. Part C: Emerg. Technol. 112, 62ā77 (2020)
Campbell, A.M., Savelsbergh, M.W.P.: Decision support for consumer direct grocery initiatives. Transp. Sci. 39(3), 313ā327 (2005)
Caruana, R., Karampatziakis, N., Yessenalina, A.: An empirical evaluation of supervised learning in high dimensions. In: International Conference on Machine Learning (2008)
Consumer Panel Services GfK: Shopping behavior 2022: of shocks and accelerators. https://discover.gfk.com/story/shopping-behavior-2022/page/4/3. Accessed 24 Aug 2023
Delling, D., Goldberg, A.V., Pajor, T., Werneck, R.F.: Customizable route planning. In: Pardalos, P.M., Rebennack, S. (eds.) SEA 2011. LNCS, vol. 6630, pp. 376ā387. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20662-7_32
Derrow-Pinion, A., et al.: ETA prediction with graph neural networks in Google maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3767ā3776 (2021)
Desaulniers, G., Madsen, O.B., Ropke, S.: The vehicle routing problem with time windows. In: Toth, P., Vigo, D. (eds.) Vehicle Routing: Problems, Methods, and Applications, MOS-SIAM Series on Optimization, 2nd edn., vol. 18, pp. 119ā159. SIAM - Society for Industrial and Applied Mathematics, Philadelphia (2014)
Ehmke, J.F., Campbell, A.M.: Customer acceptance mechanisms for home deliveries in metropolitan areas. Eur. J. Oper. Res. 233(1), 193ā207 (2014)
Forbes: E-commerce sales grew 50 (2022). https://www.forbes.com/sites/jasongoldberg/2022/02/18/e-commerce-sales-grew-50-to-870-billion-during-the-pandemic/
van der Hagen, L., Agatz, N., Spliet, R., Visser, T.R., Kok, L.: Machine learningābased feasibility checks for dynamic time slot management. Transp. Sci. 58(1), 94ā109 (2024)
Ho, T.K.: Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278ā282. IEEE (1995)
Jamieson, K., Talwalkar, A.: Non-stochastic best arm identification and hyperparameter optimization. In: Gretton, A., Robert, C.C. (eds.) Proceedings of the 19th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 51, pp. 240ā248. PMLR, Cadiz (2016). https://proceedings.mlr.press/v51/jamieson16.html
Kok, A.L., Meyer, C.M., Kopfer, H., Schutten, J.M.J.: A dynamic programming heuristic for the vehicle routing problem with time windows and European community social legislation. Transp. Sci. 44(4), 442ā454 (2010)
Kƶhler, C., Haferkamp, J.: Evaluation of delivery cost approximation for attended home deliveries. Transp. Res. Procedia 37, 67ā74 (2019)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436ā444 (2015)
Middelweerd, M.: Investigating different models that can be used to define the characteristics that influence customer behaviour in the online grocery sector. Masterās thesis, Department Maritime and Transport Technology of Faculty Mechanical, Maritime and Materials Engineering of Delft University of Technology (2023)
Morley, S.K., Brito, T.V., Welling, D.T.: Measures of model performance based on the log accuracy ratio. Space Weather 16(1), 69ā88 (2018)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Stat Methodol. 58(1), 267ā288 (1996)
Visser, T.R., Spliet, R.: Efficient move evaluations for time-dependent vehicle routing problems. Transp. Sci. 54(4), 1091ā1112 (2020)
Wang, D., Zhang, J., Cao, W., Li, J., Zheng, Y.: When will you arrive? Estimating travel time based on deep neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
WaĆmuth, K., Kƶhler, C., Agatz, N., Fleischmann, M.: Demand management for attended home deliveryāa literature review. Eur. J. Oper. Res. 311(3), 801ā815 (2023)
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4ā24 (2020)
Yang, X., Straus, A.K.: An approximate dynamic programming approach to attended home delivery management. Eur. J. Oper. Res. 263(3), 935ā945 (2017)
Yang, X., et al.: Choice-based demand management and vehicle routing in e-fulfillment. Transp. Sci. 50(2), 473ā488 (2016)
Zhang, H., Wu, H., Sun, W., Zheng, B.: DeepTravel: a neural network based travel time estimation model with auxiliary supervision. arXiv preprint arXiv:1802.02147 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
We acknowledge that all authors of this article are affiliated with ORTEC, which might introduce a potential conflict of interest. We have made every effort to ensure the objectivity and integrity of the research conducted and here reported. We want to highlight that most of the research reported was conducted in collaboration with universities, further ensuring the high quality and impartiality of the results reported.
Rights and permissions
Copyright information
Ā© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Visser, T.R., Seccia, R., Merkx, W., Kool, W., Kok, L. (2025). Machine Learning Applications in Route Planning for Attended Home Delivery. In: Oliehoek, F.A., Kok, M., Verwer, S. (eds) Artificial Intelligence and Machine Learning. BNAIC/Benelearn 2023. Communications in Computer and Information Science, vol 2187. Springer, Cham. https://doi.org/10.1007/978-3-031-74650-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-74650-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-74649-9
Online ISBN: 978-3-031-74650-5
eBook Packages: Artificial Intelligence (R0)