APPROXIMATIONS FOR THE GI/G/m QUEUE

Whitt, Ward

doi:10.1111/j.1937-5956.1993.tb00094.x

Cited by 297 publications

(157 citation statements)

References 68 publications

(80 reference statements)

Supporting

Mentioning

155

Contrasting

Order By: Relevance

“…In this study, the approximations presented in [34] are used for the G/G/S queue analysis. This model depends on only five parameters: the arrival rate (λ), the squared coefficient of variation of the inter-arrival time (c 2 a ), the average service time (τ ), the squared coefficient of variation of the service time (c 2 s ), and the number of servers (S).…”

Section: General Queuing Modelmentioning

confidence: 99%

General queuing model for optimal seamless delivery of payload processing in multi-core processors

et al. 2017

View full text Add to dashboard Cite

Recent developments in unmanned aerial systems (UAS) provide new opportunities in remote sensing application. In contrast to satellite and conventional (manned) aerial tasks, UAS flights can be operated in a very short period of time. UAS can also be more specifically focused toward a given task such as crop reconnaissance or electric line tower inspection. For some applications, the delivery time of the remote sensing results is crucial. The current three-phase procedure of data acquisition, data downloading and data processing, performed sequentially in time, represents a drawback that reduces the benefits of using unmanned aerial systems. In this paper, we present a parallel processing strategy, based on queuing theory, in which the data processing phase is performed on board in parallel with data acquisition. The unmanned aerial system payload has been enlarged with low-cost, lightweight, multicore boards to facilitate remote sensing data processing during flight. The storage of the raw sensing data is also done for possible further analysis; however, the ultimate decision support information can be seamless delivered to the customer upon landing. Furthermore, text alarms and limited imagery can also be provided during flight.

show abstract

Section: General Queuing Modelmentioning

confidence: 99%

General queuing model for optimal seamless delivery of payload processing in multi-core processors

et al. 2017

View full text Add to dashboard Cite

show abstract

“…To quantify these relationships, we use Whitt's simple heavy traffic approximation (Whitt 1993) for the expected waiting time in queue for a nurse line with m nurses 10 :…”

Section: Lemma 1 (1st and 2nd Moments Of The Stopping Time) When Thementioning

confidence: 99%

Design and Analysis of Diagnostic Service Centers

et al. 2010

View full text Add to dashboard Cite

In the health care domain, diagnostic service centers provide advice to patients over the phone about what the most appropriate course of action is based on their symptoms. Managers of such centers must strike a balance between accuracy of advice, callers' waiting time and staffing costs by setting the appropriate capacity (staffing) and service depth. We model this problem as a multiple-server queueing system with the servers performing a sequential testing process, and the customers deciding whether to use the service or not, based on their expectation of accuracy and congestion. We find the dual concerns of accuracy and congestion lead to a counterintuitive impact of capacity: Increasing capacity might increase congestion. In addition, (i) Patient population size is an important driver in management decisions, not only in staffing, but also in accuracy of advice; (ii) Increasing asymmetry in error costs may not increase asymmetry in the corresponding error rates; and (iii) The error costs for the two major stake-holders -the service manager and the patient -may impact the optimal staffing level in different ways. Finally, we highlight the relevance of our model and results to challenges in practice elicited during interviews with current clinical researchers and practitioners.

show abstract

“…Whitt (1993) reported that it is usually an excellent approximation, especially when ρ is high. In our analyses, we will use expression (1) to compute the average delay.…”

Section: Pooling Strategiesmentioning

confidence: 99%

Pooling strategies for call center agent cross-training

2009

View full text Add to dashboard Cite

We consider call center environments where agents serve distinct customer types, and investigate the efficiency benefits achievable via cross-training. We do this by first considering specialized agents grouped into N departments according to the customer type they serve.Then we examine cross-training policies that pool a set of departments K (selecting k = |K| of N ) into a single larger department in which every agent serves all of the pooled call types. For the pooled department, we analyze both First-Come-First-Served (FCFS) and Non-Preemptive Priority (NPP) service disciplines. By comparing the resulting queueing models via standard queueing approximations and numerical analysis, we characterize the impact of system parameters, such as department sizes, arrival rates and service times, on the decision of what departments to pool.

show abstract

APPROXIMATIONS FOR THE GI/G/m QUEUE

Cited by 297 publications

References 68 publications

General queuing model for optimal seamless delivery of payload processing in multi-core processors

General queuing model for optimal seamless delivery of payload processing in multi-core processors

Design and Analysis of Diagnostic Service Centers

Pooling strategies for call center agent cross-training

Contact Info

Product

Resources

About