Abstract
The increasing demand for food delivery services necessitates optimizing courier efficiency to maintain service quality and cost-effectiveness. This study investigates the impact of order aggregation and courier assignment strategies on food delivery performance. We explore various order aggregation methods, including KMeans and DBSCAN clustering, and compare them with different courier assignment approaches, such as first-in-first-out and nearest courier selection. Through experiments on both synthetic and real-world data, we demonstrate that order aggregation, particularly when combined with the nearest courier approach, significantly reduces delivery times and courier travel distances, especially under high order volumes. Our findings provide valuable insights for food delivery platforms seeking to optimize their operations and enhance customer satisfaction.
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References
Agnetis, A., Cosmi, M., Nicosia, G., Pacifici, A.: Two is better than one? order aggregation in a meal delivery scheduling problem. Comput. Ind. Eng. 183, 109514 (2023). https://doi.org/10.1016/j.cie.2023.109514
Bozanta, A., et al.: Courier routing and assignment for food delivery service using reinforcement learning. Comput. Ind. Eng. 164, 107871 (2022). https://doi.org/10.1016/j.cie.2021.107871
Cosmi, M., Oriolo, G., Piccialli, V., Ventura, P.: Single courier single restaurant meal delivery (without routing). Oper. Res. Lett. 47(6), 537–541 (2019). https://doi.org/10.1016/j.orl.2019.09.007
Côté, J.F., Alves de Queiroz, T., Gallesi, F., Iori, M.: A branch-and-regret algorithm for the same-day delivery problem. Transport. Res. Part E: Logist. Transport. Rev. 177, 103226 (2023). https://doi.org/10.1016/j.tre.2023.103226
Fikar, C., Braekers, K.: Bi-objective optimization of e-grocery deliveries considering food quality losses. Comput. Ind. Eng. 163, 107848 (2022). https://doi.org/10.1016/j.cie.2021.107848
Jahanshahi, H., et al.: A deep reinforcement learning approach for the meal delivery problem. Knowl.-Based Syst. 243, 108489 (2022). https://doi.org/10.1016/j.knosys.2022.108489
Joshi, M., Singh, A., Ranu, S., Bagchi, A., Karia, P., Kala, P.: Batching and matching for food delivery in dynamic road networks. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 2099–2104 (2021). https://doi.org/10.1109/ICDE51399.2021.00207
Klein, V., Steinhardt, C.: Dynamic demand management and online tour planning for same-day delivery. Eur. J. Oper. Res. 307(2), 860–886 (2023). https://doi.org/10.1016/j.ejor.2022.09.011
Mao, W., Ming, L., Rong, Y., Tang, C.S., Zheng, H.: Faster deliveries and smarter order assignments for an on-demand meal delivery platform. Available at SSRN 3469015 (2019)
Reyes, D., Erera, A., Savelsbergh, M., Sahasrabudhe, S., O’Neil, R.: The meal delivery routing problem. Optim. Online 6571 (2018)
Simoni, M.D., Winkenbach, M.: Crowdsourced on-demand food delivery: an order batching and assignment algorithm. Transport. Res. Part C: Emerg. Technol. 149, 104055 (2023). https://doi.org/10.1016/j.trc.2023.104055
Steever, Z., Karwan, M., Murray, C.: Dynamic courier routing for a food delivery service. Comput. Oper. Res. 107, 173–188 (2019). https://doi.org/10.1016/j.cor.2019.03.008
Techno: Amazon business research analyst (2022). https://doi.org/10.34740/KAGGLE/DSV/4141593. https://www.kaggle.com/dsv/4141593
Ulmer, M.W., Thomas, B.W., Campbell, A.M., Woyak, N.: The restaurant meal delivery problem: dynamic pickup and delivery with deadlines and random ready times. Transp. Sci. 55(1), 75–100 (2021). https://doi.org/10.1287/trsc.2020.1000
Voccia, S.A., Campbell, A.M., Thomas, B.W.: The same-day delivery problem for online purchases. Transp. Sci. 53(1), 167–184 (2019). https://doi.org/10.1287/trsc.2016.0732
Yildiz, B., Savelsbergh, M.: Provably high-quality solutions for the meal delivery routing problem. Transp. Sci. 53(5), 1372–1388 (2019). https://doi.org/10.1287/trsc.2018.0887
Yildiz, B., Savelsbergh, M.: Service and capacity planning in crowd-sourced delivery. Transport. Res. Part C: Emerg. Technol. 100, 177–199 (2019). https://doi.org/10.1016/j.trc.2019.01.021
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Appendix
Appendix
Table 6 represents average results of models on 100 evaluations. Parameters are as follows: \(num_{couriers} = 10, max_{time} = 15, num_{rest} = 5, num_{orders} = 80\). This implies a fairly large number of orders in a short period of time.
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Kondratov, P., Tarasova, E. (2024). Optimizing Food Delivery Efficiency: The Impact of Order Aggregation and Courier Assignment Strategies. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2024. Communications in Computer and Information Science, vol 2281. Springer, Cham. https://doi.org/10.1007/978-3-031-77432-4_4
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DOI: https://doi.org/10.1007/978-3-031-77432-4_4
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