Integrated Fleet and Demand Control for On-Demand Meal Delivery Platforms

References

• Abeliuk A, Berbeglia G, Cebrian M, Van Hentenryck P (2016) Assortment optimization under a multinomial logit model with position bias and social influence. 4OR Quart. J. Oper. Res. 14(1):57–75.Crossref, Google Scholar
• Agarwal A, Hosanagar K, Smith MD (2011) Location, location, location: An analysis of profitability of position in online advertising markets. J. Marketing Res. 48(6):1057–1073.Crossref, Google Scholar
• Ahuja K, Chandra V, Lord V, Peens C (2021) Ordering in: The rapid evolution of food delivery. Accessed November 4, 2021, https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/ordering-in-the-rapid-evolution-of-food-delivery/.Google Scholar
• Aouad A, Segev D (2021) Display optimization for vertically differentiated locations under multinomial logit preferences. Management Sci. 67(6):3519–3550.Link, Google Scholar
• Azagirre X, Balwally A, Candeli G, Chamandy N, Han B, King A, Lee H, et al. (2024) A better match for drivers and riders: Reinforcement learning at Lyft. INFORMS J. Appl. Analytics 54(1):71–83.Link, Google Scholar
• Banerjee S, Freund D, Lykouris T (2022) Pricing and optimization in shared vehicle systems: An approximation framework. Oper. Res. 70(3):1783–1805.Link, Google Scholar
• Bettman JR, Luce MF, Payne JW (1998) Constructive consumer choice processes. J. Consumer Res. 25(3):187–217.Crossref, Google Scholar
• Boeing G (2017) OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Comput. Environment Urban Systems 65:126–139.Crossref, Google Scholar
• Bozanta A, Cevik M, Kavaklioglu C, Kavuk EM, Tosun A, Sonuc SB, Duranel A, Basar A (2022) Courier routing and assignment for food delivery service using reinforcement learning. Comput. Indust. Engrg. 164:107871.Crossref, Google Scholar
• Campbell AM, Savelsbergh M (2004) Efficient insertion heuristics for vehicle routing and scheduling problems. Transportation Sci. 38(3):369–378.Link, Google Scholar
• Cano MR, Espelt R, Morell MF (2021) Flexibility and freedom for whom? Precarity, freedom and flexibility in on-demand food delivery. Work Organ. Labour Globalisation 15(1):46–68.Google Scholar
• Chandon P, Hutchinson JW, Bradlow ET, Young SH (2009) Does in-store marketing work? Effects of the number and position of shelf facings on brand attention and evaluation at the point of purchase. J. Marketing 73(6):1–17.Crossref, Google Scholar
• Chen XM, Zheng H, Ke J, Yang H (2020) Dynamic optimization strategies for on-demand ride services platform: Surge pricing, commission rate, and incentives. Transportation Res. Part B Methodological 138:23–45.Crossref, Google Scholar
• Dai H, Liu P (2020) Workforce planning for O2O delivery systems with crowdsourced drivers. Ann. Oper. Res. 291(1):219–245.Crossref, Google Scholar
• Davis J, Gallego G, Topaloglu H (2013) Assortment planning under the multinomial logit model with totally unimodular constraint structures. Technical report, Cornell University, Ithaca, NY.Google Scholar
• Drèze X, Hoch SJ, Purk ME (1994) Shelf management and space elasticity. J. Retailing 70(4):301–326.Crossref, Google Scholar
• Feldman P, Frazelle AE, Swinney R (2023) Managing relationships between restaurants and food delivery platforms: Conflict, contracts, and coordination. Management Sci. 69(2):812–823.Link, Google Scholar
• Frank RE, Massy WF (1970) Shelf position and space effects on sales. J. Marketing Res. 7(1):59–66.Crossref, Google Scholar
• Ghose A, Yang S (2009) An empirical analysis of search engine advertising: Sponsored search in electronic markets. Management Sci. 55(10):1605–1622.Link, Google Scholar
• Graham M (2021) DoorDash introduces search-page ads for restaurants. Wall Street J. (October 12), https://www.wsj.com/articles/doordash-introduces-search-page-ads-for-restaurants-11634032920.Google Scholar
• Haferkamp J, Ulmer MW, Ehmke JF (2024) Heatmap-based decision support for repositioning in ride-sharing systems. Transportation Sci. 58(1):110–130.Link, Google Scholar
• Han B, Lee H, Martin S (2022) Real-time rideshare driver supply values using online reinforcement learning. KDD’22 Proc. 28th ACM SIGKDD Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 2968–2976.Google Scholar
• Hildebrandt FD, Ulmer MW (2022) Supervised learning for arrival time estimations in restaurant meal delivery. Transportation Sci. 56(4):1058–1084.Link, Google Scholar
• Hildebrandt FD, Thomas BW, Ulmer MW (2023) Opportunities for reinforcement learning in stochastic dynamic vehicle routing. Comput. Oper. Res. 150:106071.Crossref, Google Scholar
• Liu Y, Luo Q, Gopalakrishnan R, Samaranayake S (2021) A framework for the joint optimization of assignment and pricing in mobility-on-demand systems with shared rides. Preprint, submitted December 28, https://arxiv.org/abs/2112.14297.Google Scholar
• Mao W, Ming L, Rong Y, Tang CS, Zheng H (2022) On-demand meal delivery platforms: Operational level data and research opportunities. Manufacturing Service Oper. Management 24(5):2535–2542.Link, Google Scholar
• Özkan E, Ward AR (2020) Dynamic matching for real-time ride sharing. Stochastic Systems 10(1):29–70.Link, Google Scholar
• Payne JW (1976) Task complexity and contingent processing in decision making: An information search and protocol analysis. Organ. Behav. Human Performance 16(2):366–387.Crossref, Google Scholar
• Powell W (2021) Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Qin Z, Tang X, Jiao Y, Zhang F, Xu Z, Zhu H, Ye J (2020) Ride-hailing order dispatching at DiDi via reinforcement learning. INFORMS J. Appl. Analytics 50(5):272–286.Link, Google Scholar
• Rana P, Haddon H (2021) DoorDash and Uber Eats are hot. They’re still not making money. Wall Street J. (May 28), https://www.wsj.com/articles/doordash-and-uber-eats-are-hot-theyre-still-not-making-money-11622194203.Google Scholar
• Reyes D, Erera A, Savelsbergh M, Sahasrabudhe S, O’Neil R (2018) The meal delivery routing problem. Technical report, Georgia Institute of Technology, Atlanta, GA.Google Scholar
• Shi Q, Dearden JA, Lai EK (2025) Shuffling as a sales tactic: An experimental study of selling product rankings. Management Sci. 71(5):3944–3965.Link, Google Scholar
• Silk AJ, Urban GL (1978) Pre-test-market evaluation of new packaged goods: A model and measurement methodology. J. Marketing Res. 15(2):171–191.Crossref, Google Scholar
• Soeffker N, Ulmer MW, Mattfeld DC (2022) Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review. Eur. J. Oper. Res. 298(3):801–820.Crossref, Google Scholar
• Ulmer MW, Savelsbergh M (2020) Workforce scheduling in the era of crowdsourced delivery. Transportation Sci. 54(4):1113–1133.Link, Google Scholar
• Ulmer MW, Thomas BW, Campbell AM, Woyak N (2021) The restaurant meal delivery problem: Dynamic pickup and delivery with deadlines and random ready times. Transportation Sci. 55(1):75–100.Link, Google Scholar
• Ursu RM (2018) The power of rankings: Quantifying the effect of rankings on online consumer search and purchase decisions. Marketing Sci. 37(4):530–552.Link, Google Scholar
• Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv. Neural Inform. Processing Systems 30:5998–6008.Google Scholar
• Wang M, Zhang H, Rusmevichientong P, Shen M (2025) Optimizing offline product design and online assortment policy: Measuring the relative impact of each decision. Management Sci. 71(5):4266–4286.Google Scholar
• Wang X, Wang L, Dong C, Ren H, Xing K (2023) An online deep reinforcement learning-based order recommendation framework for rider-centered food delivery system. IEEE Trans. Intelligent Transportation Systems 24(5):5640–5654.Crossref, Google Scholar
• Wang Z, Schaul T, Hessel M, Hasselt H, Lanctot M, Freitas N (2016) Dueling network architectures for deep reinforcement learning. Balcan MF, Weinberger KQ, eds. Proc. 33rd Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 48 (PMLR, New York), 1995–2003.Google Scholar
• Watkins CJ, Dayan P (1992) Technical note: Q-learning. Machine Learn. 8(3–4):279–292.Crossref, Google Scholar
• Wolsey LA, Nemhauser GL (2014) Integer and Combinatorial Optimization (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Yildiz B, Savelsbergh M (2019) Provably high-quality solutions for the meal delivery routing problem. Transportation Sci. 53(5):1372–1388.Link, Google Scholar
• Zhou Z, Roncoli C (2023) A fairness-aware joint pricing and matching framework for dynamic ridesharing. 2023 8th Internat. Conf. Models Tech. Intelligent Transportation Systems MT-ITS (IEEE, Piscataway, NJ), 1–6.Google Scholar
• Zou T, Zhou B (2025) Self-preferencing and search neutrality in online retail platforms. Management Sci. 71(5):4087–4107.Link, Google Scholar