Allocation Problems in Ride-Sharing Platforms: Online Matching With Offline Reusable Resources

Dickerson, John P.; Sankararaman, Karthik Abinav; Srinivasan, Aravind; Xu, Pan

doi:10.1609/aaai.v32i1.11477

Cited by 45 publications

(50 citation statements)

References 36 publications

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“…This is implied by a well-known example for the prophet inequality problem Sucheston 1977, 1978). We note that this hardness result is applied even in the setting of (Dickerson et al 2021). Thus our result complements their result, which considers only non-adaptive algorithms.…”

Section: Our Contributionsupporting

confidence: 68%

“…The problem models practical situations that assign agents to tasks arriving online. For example, in a rideshare platform (Dickerson et al 2021;Dong et al 2021;Lowalekar, Varakantham, and Jaillet 2020;Nanda et al 2020) such as Uber and Lyft, we match drivers to riders where requests from riders arrive one by one. Other applications include crowdsourcing (Assadi, Hsu, and Jabbari 2015;Ho and Vaughan 2012;Xu et al 2017) and job hiring (Anagnostopoulos et al 2018;Dickerson et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…The goal is to design an online algorithm that maximizes the total profit. We here assume that D t is known in advance, which is called a Known Adversarial Distribution (KAD) model (Dickerson et al 2021); see also (Alaei, Hajiaghayi, and Liaghat 2012). If D t is the same for all t, it is called a Known I.I.D.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by practical applications, it has been studied to generalize the online task assignment problem with additional specific conditions. An example is reusability of agents (Dickerson et al 2021). In the standard online task assignment problem, when an agent is assigned a task, she will leave immediately from the market.…”

Section: Introductionmentioning

confidence: 99%

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Online Task Assignment Problems with Reusable Resources

Sumita

Ito²,

Takemura³

et al. 2022

AAAI

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We study online task assignment problem with reusable resources, motivated by practical applications such as ridesharing, crowdsourcing and job hiring. In the problem, we are given a set of offline vertices (agents), and, at each time, an online vertex (task) arrives randomly according to a known time-dependent distribution. Upon arrival, we assign the task to agents immediately and irrevocably. The goal of the problem is to maximize the expected total profit produced by completed tasks. The key features of our problem are (1) an agent is reusable, i.e., an agent comes back to the market after completing the assigned task, (2) an agent may reject the assigned task to stay the market, and (3) a task may accommodate multiple agents. The setting generalizes that of existing work in which an online task is assigned to one agent under (1). In this paper, we propose an online algorithm that is 1/2-competitive for the above setting, which is tight. Moreover, when each agent can reject assigned tasks at most Δ times, the algorithm is shown to have the competitive ratio Δ/(3Δ-1), which is at least 1/3. We also evaluate our proposed algorithm with numerical experiments.

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Section: Our Contributionsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Online Task Assignment Problems with Reusable Resources

Sumita

Ito²,

Takemura³

et al. 2022

AAAI

View full text Add to dashboard Cite

show abstract

“…From a conceptual standpoint, our approach has similarities with LP-based and simulation-based algorithms developed for related online matching problems (Manshadi et al 2012, Jaillet and Lu 2013, Dickerson et al 2018). These algorithms often solve an offline relaxation of the matching problem and use summary statistics of the offline solution to make randomized online decisions (e.g., an arriving vertex is randomly matched according to a distribution proportional to the flow solution).…”

Section: Discussion: Capturing the Pooling Effectsmentioning

confidence: 99%

Dynamic Stochastic Matching Under Limited Time

Aouad

Sarıtaç

2022

Operations Research

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In “Dynamic Stochastic Matching Under Limited Time,” Aouad and Sarıtaç analyze the design of matching policies in dynamic markets such as carpooling platforms and kidney exchange schemes. A crucial distinction with previous literature is that the agents’ arrivals and departures are fully dynamic. The demand and supply side are constantly replenished; each market participant remains available for potential matches during a limited period of time. Specifically, the authors formulate a general dynamic matching model over edge-weighted graphs, where the agents' arrivals and abandonments are stochastic and heterogeneous. The platform controls how long each agent waits and whom s/he is matched with. These decisions are subject to a fundamental tradeoff between increasing market thickness and mitigating the risk of abandonments from certain participants. The authors’ main contribution is to devise simple matching algorithms with strong performance guarantees for a broad class of networks. In contrast, they show that widely used batching algorithms have an arbitrary bad performance on certain graph-theoretic structures. Their analysis involves novel techniques including linear programming benchmarks, value function approximations, and proxies for continuous-time Markov chains, which may be of broader interest. Extensive simulations on real-world taxi demand data demonstrate that the newly developed algorithms can significantly improve cost efficiency against batching algorithms.

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Mix and Match: Markov Chains and Mixing Times for Matching in Rideshare

Curry

Dickerson

Sankararaman

et al. 2019

Web and Internet Economics

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Rideshare platforms such as Uber and Lyft dynamically dispatch drivers to match riders' requests. We model the dispatching process in rideshare as a Markov chain that takes into account the geographic mobility of both drivers and riders over time. Prior work explores dispatch policies in the limit of such Markov chains; we characterize when this limit assumption is valid, under a variety of natural dispatch policies. We give explicit bounds on convergence in general, and exact (including constants) convergence rates for special cases. Then, on simulated and real transit data, we show that our bounds characterize convergence rates-even when the necessary theoretical assumptions are relaxed. Additionally these policies compare well against a standard reinforcement learning algorithm which optimizes for profit without any convergence properties. IntroductionRideshare firms such as Uber, Lyft, and Didi Chuxing dynamically match riders to drivers via an online, digital platform. Riders request a driver through an online portal or mobile app; a driver is matched by the platform to a rider based on geographic proximity, driver preferences, pricing, and other factors. The rideshare driver then picks up the rider at her request location, transfers her to her destination, and reenters the platform to be matched again-albeit at a new geographic location. Part of the larger sharing economy, rideshare firms are increasingly competitive against traditional taxi services due to their ease of use, lower pricing, and immediacy of service [14].Matching riders to rideshare drivers is nontrivial. While the core process is a form of the well-studied online matching problem [23], current models developed in the EconCS, AI, and Operations Research communities [28,11,5] do not completely capture the mobile aspects of both the drivers and riders. Drivers and riders are agents who move about a constrained space (e.g., city streets), becoming (in)active periodically due to the matching process. When a platform receives a request, it must make a near-real-time dispatch decision amongst nearby drivers who are available at the current time. The platform's goal is to maximize an objective (e.g., revenue or throughput) by servicing requests in an online fashion, subject to various realworld constraints and challenges like setting prices, predicting supply and demand, fairness considerations, competing with other firms, and so on [8,17,4].In this paper, we study the dynamics of the nascent rideshare market under different dispatch strategies. Recent work uses Markov chains to model complex ride-sharing dynamics in a closed-world system-that is, a system with a fixed total supply of cars [28,4,5]. These assume the Markov chains reach their stationary distributions quickly, and thus all prior work optimizes for dispatch strategies in the limit. As a complement, the present paper characterizes-theoretically and empirically-when that limit assumption is valid under a variety of natural strategies.

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Allocation Problems in Ride-Sharing Platforms: Online Matching With Offline Reusable Resources

Cited by 45 publications

References 36 publications

Online Task Assignment Problems with Reusable Resources

Online Task Assignment Problems with Reusable Resources

Dynamic Stochastic Matching Under Limited Time

Mix and Match: Markov Chains and Mixing Times for Matching in Rideshare

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