On the Optimal Design of a Bipartite Matching Queueing System

Afèche, Philipp; Caldentey, René; Gupta, Varun

doi:10.1287/opre.2020.2027

Cited by 29 publications

(38 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There has also been a number of studies that consider the design and performance evaluation of parallel FCFS systems. The problem of how to optimally design a matching graph to balance waiting time delays and matching rewards generated by pairing customers and servers under FCFS-ALIS has been studied in Afeche et al [2019]. Adan et al [2019] design heuristics to approximate customer-to-sever matching rates by means of an FCFS bipartite infinite matching model first proposed in Caldentey and Kaplan [2007], and further analyzed in and Fazel-Zarandi and Kaplan [2018].…”

Section: Discussionmentioning

confidence: 99%

Matching Queues with Reneging: a Product Form Solution

Castro

Nazerzadeh

Yan

2020

Preprint

View full text Add to dashboard Cite

Motivated by growing applications in two-sided markets, we study a parallel matching queue with reneging. Demand and supply units arrive to the system and are matched in an FCFS manner according to a compatibility graph specified by an N-system. If they cannot be matched upon arrival, they queue and may abandon the system as time goes by. We derive explicit product forms of the steady state distributions of this system by identifying a partial balance condition.

show abstract

Section: Discussionmentioning

confidence: 99%

Matching Queues with Reneging: a Product Form Solution

Castro

Nazerzadeh

Yan

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…They propose a quadratic program (QP) to compute the steady-state matching flows between customers and servers and prove the conditions under which the approximation is exact. We build on the theoretical results in [2] to design resource eligibility structures that match heterogeneous individuals and resources in the homelessness setting. Contrary to the model in [2], we do not assume that the queues or the matching rewards are given a priori.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We build on the theoretical results in [2] to design resource eligibility structures that match heterogeneous individuals and resources in the homelessness setting. Contrary to the model in [2], we do not assume that the queues or the matching rewards are given a priori. Instead, we propose to use observational data from historical policy to learn an appropriate grouping of individuals into distinct queues, estimate the matching rewards, and evaluate the resulting policies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Wait Time Average wait time is dependent on the structure of the matching topology. For example, minimum average wait time is attainable in a fully FCFS policy where M qr = 1 ∀q ∈ Q, r ∈ R. In [2], the authors characterize the general structural properties that impact average wait time. In particular, they show that under the heavy traffic condition, a matching system can be partitioned into a collection of complete resource pooling (CRP) subsystems that operate "almost" independently of each other.…”

Section: Policy Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Learning Resource Allocation Policies from Observational Data with an Application to Homeless Services Delivery

Rahmattalabi¹,

Vayanos²,

Dullerud³

et al. 2022

Preprint

View full text Add to dashboard Cite

We study the problem of learning, from observational data, fair and interpretable policies that effectively match heterogeneous individuals to scarce resources of different types. We model this problem as a multi-class multi-server queuing system where both individuals and resources arrive stochastically over time. Each individual, upon arrival, is assigned to a queue where they wait to be matched to a resource. The resources are assigned in a first come first served (FCFS) fashion according to an eligibility structure that encodes the resource types that serve each queue. We propose a methodology based on techniques in modern causal inference to construct the individual queues as well as learn the matching outcomes and provide a mixed-integer optimization (MIO) formulation to optimize the eligibility structure. The MIO problem maximizes policy outcome subject to wait time and fairness constraints. It is very flexible, allowing for additional linear domain constraints. We conduct extensive analyses using synthetic and real-world data. In particular, we evaluate our framework using data from the U.S. Homeless Management Information System (HMIS). We obtain wait times as low as an FCFS policy while improving the rate of exit from homelessness for underserved or vulnerable groups (7% higher for the Black individuals and 15% higher for those below 17 years old) and overall.

show abstract

“…Afeche et al. (2021) studied a two‐sided queue to find matching topologies to investigate the trade‐off between customer waiting times and matching rewards. However, the structure of our transplant queueing systems is different from matching queues because the patients change class while waiting due to health deterioration and the objective is to maximize the total expected pre‐ (waiting time) and post‐transplant survival (matching reward).…”

Section: Related Workmentioning

confidence: 99%

Is it Time to Include Post‐Transplant Survival in Heart Transplantation Allocation Rules?

Hasankhani

Khademi

2021

Production and Operations Management

View full text Add to dashboard Cite

Identifying an efficient and fair allocation of limited donated hearts to patients on the waiting list is one of the top priorities in heart transplantation management. The recent heart allocation rule by the United Network for Organ Sharing (UNOS) has emphasized medical urgency to address the heart transplant crisis by further dividing the previous sickest patient group into three subgroups. However, there is a significant debate on optimality and fairness of this policy because although it can help reduce pre‐transplant mortality, it may reduce post‐transplant survival. We undertake a rigorous study to address this debate by measuring the impacts of a variety of perspectives on the waiting list and patients. We show that the optimal policy of our proposed fluid model is a dynamic priority rule, and provide insight on the impact of fairness constraints on such priorities. We quantify the price that the society pays for following a medical urgency approach, which favors the sickest patients, compared to a utilitarian approach, which seeks to maximize total quality‐adjusted life years (QALYs). Our results, produced by a validated simulation model, reveal that the said price is 7.7% of total QALYs and increases to 11% by considering a broader regional sharing aligned with four‐hour heart cold ischemic time. We study other relevant objectives/measures in transplantation and our results show that the utilitarian policy outperforms the medical urgency policy in other measures as well. Our analysis provides novel insights on optimal patient allocation and sheds light on the debate around this challenging problem.

show abstract

On the Optimal Design of a Bipartite Matching Queueing System

Abstract: Designing Fair and Efficient Matching Service Systems

Cited by 29 publications

References 54 publications

Matching Queues with Reneging: a Product Form Solution

Matching Queues with Reneging: a Product Form Solution

Learning Resource Allocation Policies from Observational Data with an Application to Homeless Services Delivery

Is it Time to Include Post‐Transplant Survival in Heart Transplantation Allocation Rules?

Contact Info

Product

Resources

About