Centralized vs. Decentralized Ambulance Diversion: A Network Perspective

Deo, Sarang; Gurvich, Itai

doi:10.2139/ssrn.1619419

Cited by 24 publications

(40 citation statements)

References 27 publications

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“…(Sun et al 2006, Vilkes et al 2004 and none of the studies to our knowledge considers the joint effect of hospital characteristics and network characteristics on the extent of diversion. Our results also seem to be qualitatively consistent with the analytical results in Deo and Gurvich (2011) that decentralized ambulance diversion results in overuse of diversion without reaping the operational benefits of pooling of capacity.…”

Section: Network Effectsupporting

confidence: 88%

The Impact of Size and Occupancy of Hospital on the Extent of Ambulance Diversion: Theory and Evidence

2012

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Section: Network Effectsupporting

confidence: 88%

The Impact of Size and Occupancy of Hospital on the Extent of Ambulance Diversion: Theory and Evidence

2012

Self Cite

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“…We analytically extend these results to the number of customers in system and in queue at each server noting that this is not a trivial extension from the total number of customers in the whole system. Threshold Routing is discussed in Deo and Gurvich () and Do and Shunko () for a queueing system with fixed service rate. Do and Shunko () propose and analyze a set of routing policies that preserve the long‐term arrival rate to each server and demonstrate that any policy in this set, including the Threshold Routing policy, stochastically reduces and smooths the queue dedicated to each server, thereby providing a Pareto improvement.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Impact of Behavioral Factors on Performance of Multi‐Server Queueing Systems

Hùng

Shunko

Lucas

et al. 2018

Production and Operations Management

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R ecent studies have shown that the processing speed of employees in service-based queueing systems is impacted by various behavioral factors. However, there is limited analytical work to investigate how these behavioral factors affect the overall performance of different queueing system designs. In this study, we focus on the response of human servers to the design and congestion level of the queueing system in which they operate. Specifically, we incorporate two behavioral factors into multi-server analytical queueing models: (1) server speedup due to increase of workload, and (2) server slowdown due to social loafing when multiple workers share the workload. We evaluate how these factors affect the performance of both the multi-server single-queue (SQ) and multi-server parallel-queue (PQ) system and the relative superiority of each system with respect to the number of customers in queue and the expected wait time in queue. We show that the impact of workload-dependent speedup can be decomposed into a direct effect and indirect effect on system performance. The direct effect leads to a reduced queue size due to increased expected service rate, while the indirect effect decreases queue size due to the "smoothing" effect. We quantify the performance impacts associated with both behavioral factors, illustrate the conditions where each effect dominates, and derive threshold values for these behavioral effects beyond which PQ systems outperform SQ systems. We also consider strategic routing and its impact on the performance of PQ systems. Our analytical contributions and numerical analyses offer important managerial guidance regarding the choice of the queueing system design and provide a theoretical foundation for future research in behavioral queueing.

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“…This is based on a quantification of "preparedness," an effort that is continued with great detail by Lee (2011). Deo and Gurvich (2011) utilize a queueing network model to test five different decision making strategies, two of which employ the knowledge of a centralized "social planner" to aid in real-time ambulance routing decisions.…”

Section: Ambulance Deployment and Locationmentioning

confidence: 99%