Learning and data-driven optimization in queues with strategic customers

Burnetas, Apostolos

doi:10.1007/s11134-022-09816-0

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…One can show that

\psi (c_0\mu , c_0\Lambda ) = \psi (\mu ,\Lambda )

. The

G/G/c/k

queue is a critical building block of service speed design (see, e.g., Anand et al., 2011; Burnetas, 2022).…”

Section: The Data‐driven Decision Problemmentioning

confidence: 99%

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The framework of parametric and nonparametric operational data analytics

Feng

Shanthikumar

2023

Production and Operations Management

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This paper introduces the general philosophy of the Operational Data Analytics (ODA) framework for data‐based decision modeling. The fundamental development of this framework lies in establishing the direct mapping from data to decision by identifying the appropriate class of operational statistics. The efficient decision making relies on a careful balance between data integration and decision validation. Through a canonical decision‐making problem under uncertainty, we show that the existing approaches (including statistical estimation and then optimization, retrospective optimization, sample average approximation, regularization, robust optimization, and robust satisficing) can all be unified through the lens of the ODA formulation. To make the key concepts accessible, we demonstrate, using a simple running example, how some of the existing approaches may become equivalent under the ODA framework, and how the ODA solution can improve the decision efficiency, especially in the small sample regime.This article is protected by copyright. All rights reserved

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“…One can show that

\psi (c_0\mu , c_0\Lambda ) = \psi (\mu ,\Lambda )

. The

G/G/c/k

queue is a critical building block of service speed design (see, e.g., Anand et al., 2011; Burnetas, 2022).…”

Section: The Data‐driven Decision Problemmentioning

confidence: 99%

“…One can show that . The queue is a critical building block of service speed design (see, e.g., Anand et al., 2011; Burnetas, 2022).…”

Section: The Data‐driven Decision Problemmentioning

confidence: 99%

The framework of parametric and nonparametric operational data analytics

Feng

Shanthikumar

2023

Production and Operations Management

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The Operational Data Analytics (ODA) for Service Speed Design

Feng,

Jiang,

Liu

et al. 2024

Management Science

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We develop the operational data analytics (ODA) framework for the classical service design problem of [Formula: see text] systems. The customer arrival rate is unknown. Instead, some historical data of interarrival times are collected. The data-integration model, specifying the mapping from the arrival data to the service rate, is formulated based on the time-scaling property of the stochastic service process. Validating the data-integration model against the long-run average service reward leads to a uniformly optimal service rate for any given sample size. We further derive the ODA-predicted reward function based on the data-integration model, which gives a consistent estimate of the underlying reward function. Our numerical experiments show that the ODA framework can lead to an efficient design of service rate and service capacity, which is insensitive to model specification. The ODA solution exhibits superior performance compared with the conventional estimation-and-then-optimization solutions in the small sample regime. This paper was accepted by David Simchi-Levi, operations management. Funding: Z. Jiang’s research is supported by the National Natural Science Foundation of China [Grant 71931007]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.00655 .

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Learning and data-driven optimization in queues with strategic customers

Cited by 2 publications

References 20 publications

The framework of parametric and nonparametric operational data analytics

The framework of parametric and nonparametric operational data analytics

The Operational Data Analytics (ODA) for Service Speed Design

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