Adaptive Approximation Schemes for Matching Queues

Fuente: arXiv
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Auteurs principaux: AmaniHamedani, Alireza, Aouad, Ali, Saberi, Amin
Format: Preprint
Publié: 2025
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author AmaniHamedani, Alireza
Aouad, Ali
Saberi, Amin
author_facet AmaniHamedani, Alireza
Aouad, Ali
Saberi, Amin
contents We study a continuous-time, infinite-horizon dynamic bipartite matching problem. Suppliers arrive according to a Poisson process; while waiting, they may abandon the queue at a uniform rate. Customers on the other hand must be matched upon arrival. The objective is to minimize the expected long-term average cost subject to a throughput constraint on the total match rate. Previous literature on dynamic matching focuses on "static" policies, where the matching decisions do not depend explicitly on the state of the supplier queues, achieving constant-factor approximations. By contrast, we design "adaptive" policies, which leverage queue length information, and obtain near-optimal polynomial-time algorithms for several classes of instances. First, we develop a bi-criteria fully polynomial-time approximation scheme for dynamic matching on networks with a constant number of queues--that computes a $(1-ε)$-approximation of the optimal policy in time polynomial in both the input size and $1/ε$. A key new technique is a hybrid LP relaxation, which combines static and state-dependent LP approximations of the queue dynamics, after a decomposition of the network. Networks with a constant number of queues are motivated by deceased organ donation schemes, where the supply types can be divided according to blood and tissue types. The above algorithm, combined with a careful cell decomposition gives an efficient polynomial-time approximation scheme for dynamic matching on Euclidean networks of fixed dimension. The Euclidean case is of interest in ride-hailing and spatial service platforms, where the goal is to fulfill as many trips as possible while minimizing driving distances.
format Preprint
id arxiv_https___arxiv_org_abs_2501_08775
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Adaptive Approximation Schemes for Matching Queues
AmaniHamedani, Alireza
Aouad, Ali
Saberi, Amin
Data Structures and Algorithms
68W27 (Primary), 60K25 (Secondary)
G.3; F.2
We study a continuous-time, infinite-horizon dynamic bipartite matching problem. Suppliers arrive according to a Poisson process; while waiting, they may abandon the queue at a uniform rate. Customers on the other hand must be matched upon arrival. The objective is to minimize the expected long-term average cost subject to a throughput constraint on the total match rate. Previous literature on dynamic matching focuses on "static" policies, where the matching decisions do not depend explicitly on the state of the supplier queues, achieving constant-factor approximations. By contrast, we design "adaptive" policies, which leverage queue length information, and obtain near-optimal polynomial-time algorithms for several classes of instances. First, we develop a bi-criteria fully polynomial-time approximation scheme for dynamic matching on networks with a constant number of queues--that computes a $(1-ε)$-approximation of the optimal policy in time polynomial in both the input size and $1/ε$. A key new technique is a hybrid LP relaxation, which combines static and state-dependent LP approximations of the queue dynamics, after a decomposition of the network. Networks with a constant number of queues are motivated by deceased organ donation schemes, where the supply types can be divided according to blood and tissue types. The above algorithm, combined with a careful cell decomposition gives an efficient polynomial-time approximation scheme for dynamic matching on Euclidean networks of fixed dimension. The Euclidean case is of interest in ride-hailing and spatial service platforms, where the goal is to fulfill as many trips as possible while minimizing driving distances.
title Adaptive Approximation Schemes for Matching Queues
topic Data Structures and Algorithms
68W27 (Primary), 60K25 (Secondary)
G.3; F.2
url https://arxiv.org/abs/2501.08775