Edge Weighted Online Windowed Matching

Ashlagi, Itai; Burq, Maximilien; Dutta, Chinmoy; Jaillet, Patrick; Saberi, Amin; Sholley, Chris

doi:10.1145/3328526.3329573

Cited by 58 publications

(53 citation statements)

References 25 publications

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“…Indeed, the latter can be viewed as a special case of the former when each batch consists of exactly one request. Ashlagi et al (2018) numerically compare the performance of batching to that of the first‐dispatch protocol using New York City yellow cabs dataset, and show that batching outperforms the first‐dispatch protocol. Due to its substantial benefits, batching has been implemented widely in major ride‐hailing platforms.…”

Section: Matching In Ride‐hailingmentioning

confidence: 99%

“…Similarly, Hu and Zhou (2016) introduced a batched version of dynamic matching and derived the structural properties of the optimal policy under a stylized road network topology. Most recently, Ashlagi et al (2018) proposed online algorithms that have constant competitive ratios, that is, they achieve at least a constant fraction of the reward from an optimal offline policy with full information of future demand and supply.…”

Section: Matching In Ride‐hailingmentioning

confidence: 99%

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Dynamic pricing and matching in ride‐hailing platforms

Yan

Zhu

Korolko

et al. 2019

Naval Research Logistics

198

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Ride‐hailing platforms such as Uber, Lyft, and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research topics in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing (DP) algorithms—the two key levers in ride‐hailing—have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride‐hailing platforms. We provide a review of matching and DP techniques in ride‐hailing, and show that they are critical for providing an experience with low waiting time for both riders and drivers. Then we link the two levers together by studying a pool‐matching mechanism called dynamic waiting (DW) that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing DP and DW, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner's perspective.

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Section: Matching In Ride‐hailingmentioning

confidence: 99%

Section: Matching In Ride‐hailingmentioning

confidence: 99%

Dynamic pricing and matching in ride‐hailing platforms

Yan

Zhu

Korolko

et al. 2019

Naval Research Logistics

198

View full text Add to dashboard Cite

show abstract

“…Here are a few theoretical work which studied the design of approximation or online algorithms to achieve a bi-criterion approximation and/or online competitive ratios, see, e.g., [Ravi et al, 1993;Grandoni et al, 2009;Korula et al, 2013;Aggarwal et al, 2014;Esfandiari et al, 2016]. The work of [Bansal et al, 2012;Brubach et al, 2018;Fata et al, 2019] have the closest setting to us: each edge has an independent existence probability and each vertex from the offline and/or online side has a patience constraint on it. However, all investigated one single objective: maximization of the total profit over all matched edges.…”

Section: Related Workmentioning

confidence: 99%

“…Rideshares such as Uber and Lyft have received significant attention among research communities of computer science, operations research, and business, to name a few. One main research topic is the matching policy design of pairing drivers and riders, see, e.g., [Danassis et al, 2019;Curry et al, 2019;Ashlagi et al, 2019;Lowalekar et al, 2018;Bei and Zhang, 2018;Dickerson et al, 2018;Zhao et al, 2019]. Most of the current work focuses on either the promotion of system efficiency or that of users' satisfaction or both.…”

Section: Introductionmentioning

confidence: 99%

Trade the System Efficiency for the Income Equality of Drivers in Rideshare

2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

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Several scientific studies have reported the existence of the income gap among rideshare drivers based on demographic factors such as gender, age, race, etc. In this paper, we study the income inequality among rideshare drivers due to discriminative cancellations from riders, and the tradeoff between the income inequality (called fairness objective) with the system efficiency (called profit objective). We proposed an online bipartite-matching model where riders are assumed to arrive sequentially following a distribution known in advance. The highlight of our model is the concept of acceptance rate between any pair of driver-rider types, where types are defined based on demographic factors. Specially, we assume each rider can accept or cancel the driver assigned to her, each occurs with a certain probability which reflects the acceptance degree from the rider type towards the driver type. We construct a bi-objective linear program as a valid benchmark and propose two LP-based parameterized online algorithms. Rigorous online competitive ratio analysis is offered to demonstrate the flexibility and efficiency of our online algorithms in balancing the two conflicting goals, promotions of fairness and profit. Experimental results on a real-world dataset are provided as well, which confirm our theoretical predictions.

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“…Cloud computing is a fast expanding market with high competition where small efficiency gains translate to multi-billion dollar profits (Microsoft, 2018). Like many other markets (e.g., ridesharing platforms, kidney exchanges, online labor markets, and display advertising), the efficiency of this market relies on the performance of a matching algorithm (Ashlagi et al, 2019;Ma & Simchi-Levi, 2019;Behnezhad & Reyhani, 2018;Assadi et al, 2017). In the cloud computing case, the matching algorithm matches incoming requests for virtual machines to the hardware that will be used to satisfy them.…”

Section: Introductionmentioning

confidence: 99%

On the Cluster Admission Problem for Cloud Computing

Dierks

Kash²,

Seuken³

2021

jair

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Cloud computing providers face the problem of matching heterogeneous customer workloads to resources that will serve them. This is particularly challenging if customers, who are already running a job on a cluster, scale their resource usage up and down over time. The provider therefore has to continuously decide whether she can add additional workloads to a given cluster or if doing so would impact existing workloads’ ability to scale. Currently, this is often done using simple threshold policies to reserve large parts of each cluster, which leads to low efficiency (i.e., low average utilization of the cluster). We propose more sophisticated policies for controlling admission to a cluster and demonstrate that they significantly increase cluster utilization. We first introduce the cluster admission problem and formalize it as a constrained Partially Observable Markov Decision Process (POMDP). As it is infeasible to solve the POMDP optimally, we then systematically design admission policies that estimate moments of each workload’s distribution of future resource usage. Via extensive simulations grounded in a trace from Microsoft Azure, we show that our admission policies lead to a substantial improvement over the simple threshold policy. We then show that substantial further gains are possible if high-quality information is available about arriving workloads. Based on this, we propose an information elicitation approach to incentivize users to provide this information and simulate its effects.

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Edge Weighted Online Windowed Matching

Cited by 58 publications

References 25 publications

Dynamic pricing and matching in ride‐hailing platforms

Dynamic pricing and matching in ride‐hailing platforms

Trade the System Efficiency for the Income Equality of Drivers in Rideshare

On the Cluster Admission Problem for Cloud Computing

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