Meituan’s Real-Time Intelligent Dispatching Algorithms Build the World’s Largest Minute-Level Delivery Network
References
- (2009) Curriculum learning. Proc. 26th Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 41–48.Google Scholar
- Bosch (2022) eBike range assistant. Accessed September 29, 2023, https://www.bosch-ebike.com/en/service/range-assistant/.Google Scholar
- (2022) An imitation learning-enhanced iterated matching algorithm for on-demand food delivery. IEEE Trans. Intelligent Transportation Systems 23(10):18603–18619.Google Scholar
- (2015) XGBoost: Extreme gradient boosting. R package version 0.4-2 1(4):1–4.Google Scholar
- (2013) Parallel gaussian process optimization with upper confidence bound and pure exploration. Blockeel H, Kersting K, Nijssen S, Železný F, eds. Proc. Machine Learn. Knowledge Discovery Databases: Eur. Conf., Lecture Notes in Comput. Sci. (Springer, Berlin), 225–240.Google Scholar
- (2020) Layered sampling for robust optimization problems. Internat. Conf. Machine Learn. (PMLR, New York), 2556–2566.Google Scholar
- European Cyclists’ Federation (2011) Cycle more often 2 cool down the planet! Accessed January 6, 2023, https://ecf.com/system/files/Cycle_More_Often_2_Cool_Down_the_Planet.pdf.Google Scholar
- (2016) Guided cost learning: Deep inverse optimal control via policy optimization. Proc. 33rd Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 49–58.Google Scholar
- (2018) A tutorial on Bayesian optimization. Accessed July 10, 2023, https://arxiv.org/pdf/1807.02811.pdf.Google Scholar
- (2018) A review of multi-objective optimization: Methods and its applications. Cogent Engrg. 5(1):1–16.Google Scholar
- (2020) Graph representation learning. Synthesis Lectures Artificial Intelligence Machine Learn. 14(3):1–159.Google Scholar
- (2017) Inductive representation learning on large graphs. Adv. Neural Inform. Process. Systems, vol. 30 (ACM, New York), 1025–1035.Google Scholar
- (2001) Variable neighborhood search: Principles and applications. Eur. J. Oper. Res. 130(3):449–467.Google Scholar
- (2012) Imitation learning by coaching. Adv. Neural Inform. Process. Systems, vol. 25.Google Scholar
- (2015) Learning driving styles for autonomous vehicles from demonstration. Proc. IEEE Internat. Conf. Robotics Automation (Institute of Electrical and Electronics Engineers, Washington, DC), 2641–2646.Google Scholar
- (1955) The Hungarian method for the assignment problem. Naval Res. Logist. Quart. 2(1–2):83–97.Google Scholar
- (2019) Multi-objective stochastic optimization: A case of real-time matching in ride-sourcing markets. Preprint, submitted March 20, http://dx.doi.org/10.2139/ssrn.3356823.Google Scholar
- (1957) Algorithms for the assignment and transportation problems. J. Soc. Industry Appl. Math. 5(1):32–38.Google Scholar
- (2019) Large neighborhood search. Gendreau M, ed. Handbook of Metaheuristics (Springer, New York), 99–127.Google Scholar
- Statista (2022) Carbon intensity of the power sector in China from 2000 to 2022. Accessed July 10, 2023, https://www.statista.com/statistics/1300419/power-generation-emission-intensity-china/.Google Scholar
- (2015) Optimizing the CVaR via sampling. Proc. 29th AAAI Conf. Artificial Intelligence (Association for the Advancement of Artificial Intelligence, Palo Alto, CA), 2993–2999.Google Scholar
- (2018) Billion-scale commodity embedding for ecommerce recommendation in Alibaba. Proc. 24th ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (ACM, New York), 839–848.Google Scholar
- (2019) A two-stage fast heuristic for food delivery route planning problem. Proc. INFORMS Annual Meeting.Google Scholar
- (2008) Maximum entropy inverse reinforcement learning. AAAI, vol. 8 (AAAI Press, Palo Alto, CA), 1433–1438.Google Scholar

