Blind Dynamic Resource Allocation in Closed Networks via Mirror Backpressure

Kanoria, Yash; Qian, Pengyu

doi:10.1145/3391403.3399498

Cited by 9 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To investigate the impact of this type of inefficiency, a stream of literature has focused on understanding the financial consequence of the spatial imbalance of supply and demand by explicitly modeling the steady state equilibrium dynamics of rider and driver flows on a network. Some papers in this literature focus on the class of static pricing controls where pricing decisions are determined a priori (Banerjee et al 2021, Ozkan 2018, Besbes et al 2021b, Bimpikis et al 2019, whereas others focus on developing dynamic pricing controls where prices can be adjusted based on real-time imbalances of demand and supply across the network (Balseiro et al 2021, Kanoria and Qian 2019, Varma et al 2022.…”

Section: Related Literaturementioning

confidence: 99%

“…The only papers we are aware of that focus on dynamic pricing controls are Balseiro et al (2021), Qian (2019), andVarma et al (2022). Balseiro et al (2021) study a Lagragianrelaxation based dynamic pricing control and show that their proposed control is asymptotically optimal in the hub-and-spoke setting where the number of demand regions (more precisely, the number of "spokes") is large.…”

Section: Related Literaturementioning

confidence: 99%

See 1 more Smart Citation

Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and the Value of Dynamic Pricing

Chen

Lei

Jasin

2023

Operations Research

View full text Add to dashboard Cite

In “Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and The Value of Dynamic Pricing,” Chen, Lei, and Jasin consider a dynamic pricing problem faced by a ride-hailing service provider who manages a fixed number of servers and serves price-sensitive customers within a network. Servers serve arriving customers by relocating from the requested origins to destinations within a certain travel time. The authors first propose a static pricing policy based on the optimal solution to a deterministic relaxation of the original stochastic problem. They show that the proposed static policy matches the best possible asymptotic performance of any static policy. The authors further propose a dynamic pricing policy that adaptively changes the prices in a way that reduces the impact of past demand randomness on the balance of future distributions of servers and customers across the network. They show that the dynamic pricing policy achieves significantly better asymptotical performance. The proposed policies and their performance guarantees are further extended to a case where the firm jointly decides the relocation of vacant servers

show abstract

Section: Related Literaturementioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and the Value of Dynamic Pricing

Chen

Lei

Jasin

2023

Operations Research

View full text Add to dashboard Cite

show abstract

“…Ashlagi et al [2018], Dickerson et al [2018] and Aouad and Saritaç [2020] focus on matching between riders and drivers and the pooling of shared rides, taking into consideration the online arrival of supply and demand in space. Further, Kanoria and Qian [2020], Qin et al [2020] and Özkan and Ward [2020] design policies that dispatch drivers from areas with relatively abundant supply, while Cai et al [2019] and Pang et al [2017] look at the role of information availability and transparency in platform design. Dynamic pricing [Banerjee et al, 2015], state-dependent dispatching [Banerjee et al, 2018, driver admission control [Afeche et al, 2018] and capacity planning [Besbes et al, 2018] are also studied using queueing-theoretical models.…”

Section: Related Workmentioning

confidence: 99%

Price Cycles in Ridesharing Platforms

Yu¹,

Ma²,

Wierman³

2022

Preprint

View full text Add to dashboard Cite

In ridesharing platforms such as Uber and Lyft, it is observed that drivers sometimes collaboratively go o ine when the price is low, and then return after the price has risen due to the perceived lack of supply. This collective strategy leads to cyclic uctuations in prices and available drivers, resulting in poor reliability and social welfare. We study a continuous time, non-atomic model and prove that such online/o ine strategies may form a Nash equilibrium among drivers, but lead to a lower total driver payo if the market is su ciently dense. Further, we show how to set price oors that e ectively mitigate the emergence and impact of price cycles.

show abstract

“…In the presence of spatial imbalance and temporal variation of supply and demand, Bimpikis et al [2019] and Besbes et al [2020] study revenue-optimal pricing; Ma et al [2019] propose origin-destination based pricing that is appropriately smooth in space and time, achieving welfare optimality and incentive compatibility; Garg and Nazerzadeh [2020] show that additive instead of multiplicative "surge" pricing is more incentive aligned for drivers when prices need to be origin-based only. Considering the online arrival of supply and demand and their distribution in space, Kanoria and Qian [2020], Qin et al [2020] and Özkan and Ward [2020] study dynamic matching policies that dispatch drivers from areas with relatively abundant supply, and Ashlagi et al [2019], Dickerson et al [2018] and Aouad and Saritaç [2020] focus on the online matching between riders and drivers and the pooling of shared rides. In this work, we focus on a single origin where the optimal destination-based pricing is infeasible due to operation constraints such that some trips are necessarily more lucrative than the others.…”

Section: Related Workmentioning

confidence: 99%

Randomized FIFO Mechanisms

Castro¹,

Ma²,

Nazerzadeh³

et al. 2021

Preprint

View full text Add to dashboard Cite

We study the matching of jobs to workers in a queue, e.g. a ridesharing platform dispatching drivers to pick up riders at an airport. Under FIFO dispatching, the heterogeneity in trip earnings incentivizes drivers to cherry-pick, increasing riders' waiting time for a match and resulting in a loss of efficiency and reliability. We first present the direct FIFO mechanism, which offers lower-earning trips to drivers further down the queue. The option to skip the rest of the line incentivizes drivers to accept all dispatches, but the mechanism would be considered unfair since drivers closer to the head of the queue may have lower priority for trips to certain destinations. To avoid the use of unfair dispatch rules, we introduce a family of randomized FIFO mechanisms, which send declined trips gradually down the queue in a randomized manner. We prove that a randomized FIFO mechanism achieves the first best throughput and the second best revenue in equilibrium. Extensive counterfactual simulations using data from the City of Chicago demonstrate substantial improvements of revenue and throughput, highlighting the effectiveness of using waiting times to align incentives and reduce the variability in driver earnings.

show abstract

Blind Dynamic Resource Allocation in Closed Networks via Mirror Backpressure

Cited by 9 publications

References 0 publications

Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and the Value of Dynamic Pricing

Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and the Value of Dynamic Pricing

Price Cycles in Ridesharing Platforms

Randomized FIFO Mechanisms

Contact Info

Product

Resources

About