• Corpus ID: 239998538

Fairer LP-based Online Allocation

@article{Chen2021FairerLO,
  title={Fairer LP-based Online Allocation},
  author={Guanting Chen and Xiaocheng Li and Yinyu Ye},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.14621}
}
In this paper, we consider a Linear Program (LP)-based online resource allocation problem where a decision maker accepts or rejects incoming customer requests irrevocably in order to maximize expected revenue given limited resources. At each time, a new order/customer/bid is revealed with a request of some resource(s) and a reward. We consider a stochastic setting where all the orders are i.i.d. sampled from an unknown distribution. Such formulation gives rise to many classic applications such… 

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References

SHOWING 1-10 OF 68 REFERENCES
A Re-Solving Heuristic with Uniformly Bounded Loss for Network Revenue Management
TLDR
A family of re-solving heuristics that periodically re-optimize an approximation to the original problem known as the deterministic linear program (DLP), where random customer arrivals are replaced by their expectations are studied.
Online Allocation and Pricing: Constant Regret via Bellman Inequalities
TLDR
This work develops a framework for designing simple and efficient policies for a family of online allocation and pricing problems that includes online packing, budget-constrained probing, dynamic pricing, and online contextual bandits with knapsacks, based on Bellman inequalities.
Constant Regret in Online Allocation: On the Sufficiency of a Single Historical Trace
We consider online decision-making problems where resources are allocated dynamically to a stochastic stream of requests, and decisions are made to maximize reward while satisfying a set of
Online Resource Allocation with Limited Flexibility
TLDR
The effectiveness of the long chain design in mitigating supply-demand mismatch under a simple myopic online allocation policy is shown and an upper bound on the expected total number of lost sales is provided that is irrespective of how large the market size is.
Dynamic resource allocation: The geometry and robustness of constant regret
We study a family of dynamic resource allocation problems, wherein requests of different types arrive over time and are accepted or rejected. Each request type is characterized by its reward and the
Uniform Loss Algorithms for Online Stochastic Decision-Making With Applications to Bin Packing
  • Siddhartha Banerjee, Daniel Freund
  • Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
  • 2020
TLDR
This work introduces a simple, yet general, condition under which a natural model-predictive control algorithm obtains uniform additive loss (independent of the horizon) compared to an optimal solution with full knowledge of arrivals.
An Asymptotically Optimal Policy for a Quantity-Based Network Revenue Management Problem
TLDR
It is shown that re-solving the fluid model is required for extending the asymptotic optimality from the fluid scale to the diffusion scale, and a new policy is developed that achieves diffusion-scale optimality.
A Dynamic Near-Optimal Algorithm for Online Linear Programming
TLDR
This paper proposes a learning-based algorithm that works by dynamically updating a threshold price vector at geometric time intervals, where the dual prices learned from the revealed columns in the previous period are used to determine the sequential decisions in the current period.
Sequential Fair Allocation of Limited Resources under Stochastic Demands
TLDR
This work considers the problem of dividing limited resources between a set of agents arriving sequentially with unknown (stochastic) utilities and proposes a simple policy, HopeOnline, that aims to `match' the ex-post fair allocation vector using the current available resources and `predicted' histogram of future utilities.
Combinatorial Sleeping Bandits with Fairness Constraints
  • Fengjiao Li, Jia Liu, Bo Ji
  • Computer Science, Mathematics
    IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
  • 2019
TLDR
A new Combinatorial Sleeping multi-armed bandit model with Fairness constraints, called CSMAB-F, is proposed, aiming to address the aforementioned crucial modeling issues and rigorously proves that not only LFG is feasibility-optimal but it also has a time-average regret upper bounded.
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