Staffing for many-server systems facing non-standard arrival processes

Heemskerk, Mariska; Mandjes, Michel; Mathijsen, Britt W. J.

doi:10.1016/j.ejor.2021.07.046

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…Heemskerk et al. (2017, 2022) also propose an infinite‐server queuing system with a mixed Poisson arrival process in a random environment, a special case of a stationary Cox process . Boxma et al.…”

Section: Contribution To the Literaturementioning

confidence: 99%

ICU capacity expansion under uncertainty in the early stages of a pandemic

Gambaro

Fusai

Sodhi

et al. 2023

Production and Operations Management

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We propose a general modular approach to support decision‐makers' response in the early stages of a pandemic with resource expansion, motivated by the shortage of Covid‐19‐related intensive care units (ICU) capacity in 2020 in Italy. Our approach uses (1) a stochastic extension of an epidemic model for scenarios of projected infections, (2) a capacity load model to translate infections into scenarios of demand for the resources of interest, and (3) an optimization model to allocate this demand to the projected levels of resources based on different values of investment. We demonstrate this approach with the onset of the first and second Covid‐19 waves in three Italian regions, using the data available at that time. For epidemic modeling, we used a parsimonious stochastic susceptible‐infected‐removed model with a robust estimation procedure based on bootstrap resampling, suitable for a noisy and data‐limited environment. For capacity loading, we used a Cox queuing model to translate the projected infections into demand for ICU, using stochastic intensity to capture the variability of the patient arrival process. Finally, we used stochastic dynamic optimization to select the best policy (when and how much to expand) to minimize the expected number of patients denied ICU for any level of investment in capacity expansion and obtain an efficient frontier. The frontier allows a trade‐off between investment in additional resources and the number of patients denied intensive care. Moreover, in the panic‐driven early days of a pandemic, decision‐makers can also obtain the time until which they can postpone action, potentially reducing investment costs without increasing the expected number of denied patients.

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Section: Contribution To the Literaturementioning

confidence: 99%

ICU capacity expansion under uncertainty in the early stages of a pandemic

Gambaro

Fusai

Sodhi

et al. 2023

Production and Operations Management

View full text Add to dashboard Cite

show abstract

“…Sun and Liu (2021) also consider CIR driven DSPP as arrival input and suggest that the square-root staffing calibrated by the overdispersion level could achieve probability of delay target in heavy-traffic. Heemskerk et al (2022) investigate the staffing problem for manyserver queue with Cox arrival process via an offered-load approach using analytic results for infinite-server queue with the same arrival process as developed in Heemskerk et al (2017). Daw et al (2019) considers the staffing problem for queues with batch arrivals and finds out that it is asymptotically optimal to set the service capacity by mean arrival rate plus additional safety staffing level proportional to batch sizes as batch sizes grow to infinity.…”

Section: Related Literaturementioning

confidence: 99%

Improvement of Outpatient Service Processes: A Case Study of the University of Hong Kong-Shenzhen Hospital

Chen

Alturas

2023

Preprint

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This work presents a case study on The University of Hong Kong-Shenzhen Hospital (HKU-SZH), which was the first to implement an outpatient appointment registration system. This study provides an anatomy of the hospital outpatient process through various methods and theories, such as literature review, field research, expert consultation, business process improvement theory and Information technology, with the objective of identifying objectives and strategies of the hospital case in improving its outpatient process. By means of outpatient process improvement, this study aim to increase the case hospital’s efficiency and raise its patients’ satisfaction so that the hospital may enhance its comprehensive competence. In addition, an effective and operable methodology will be generated, which is expected to serve as a reference for other hospitals to improve their operation and their management. Therefore, the research question is to know which factors most influence patient satisfaction. It was found that service attitude, service value, and waiting time have a significant influence on patient satisfaction.

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Internet of Drones: Improving Multipath TCP over WiFi with Federated Multi-Armed Bandits for Limitless Connectivity

Pokhrel

Mandjes

2022

Drones

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We consider multipath TCP (MPTCP) flows over the data networking dynamics of IEEE 802.11ay for drone surveillance of areas using high-definition video streaming. Mobility-induced handoffs are critical in IEEE 802.11ay (because of the smaller coverage of mmWaves), which adversely affects the performance of such data streaming flows. As a result of the enhanced 802.11ay network events and features (triggered by beamforming, channel bonding, MIMO, mobility-induced handoffs, channel sharing, retransmissions, etc.), the time taken for packets to travel end-to-end in 802.11ay are inherently time-varying. Several fundamental assumptions inherent in stochastic TCP models, including Poisson arrivals of packets, Gaussian process, and parameter certainty, are challenged by the improved data traffic dynamics over IEEE 802.11ay networks. The MPTCP model’s state estimation differs largely from the actual network values. We develop a new data-driven stochastic framework to address current deficiencies of MPTCP models and design a foundational architecture for intelligent multipath scheduling (at the transport layer) considering lower layer (hybrid) beamforming. At the heart of our cross-layer architecture is an intelligent learning agent for actuating and interfacing, which learns from experience optimal packet cloning, scheduling, aggregation, and beamforming using successful features of multi-armed bandits and federated learning. We demonstrate that the proposed framework can estimate and optimize jointly (explore–exploit) and is more practicable for designing the next generation of low-delay and robust MPTCP models.

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Staffing for many-server systems facing non-standard arrival processes

Cited by 3 publications

References 47 publications

ICU capacity expansion under uncertainty in the early stages of a pandemic

ICU capacity expansion under uncertainty in the early stages of a pandemic

Improvement of Outpatient Service Processes: A Case Study of the University of Hong Kong-Shenzhen Hospital

Internet of Drones: Improving Multipath TCP over WiFi with Federated Multi-Armed Bandits for Limitless Connectivity

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