Perspectives on Queues: Combining Queues is Not Always Beneficial

Rothkopf, Michael H.; Rech, Paul

doi:10.1287/opre.35.6.906

Cited by 94 publications

(57 citation statements)

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“…In this literature, queuing theorists have shown that shorter waiting times occur from pooling identical streams of customers into a single queue served by a pool of identically skilled servers of the same number as were in the original, dedicated systems (Kleinrock 1976, Mandelbaum andReiman 1998). Much of this research has been conducted with call centers, and has shown that the benefits of flexible servers and pooled queues can outweigh potential drawbacks (Anupindi et al 2005, Bassamboo et al 2010, Gans et al 2003, Jouini et al 2008 1998, Rothkopf andRech 1987). Another factor that reduces the effectiveness of queue pooling is having strategic servers (Cachon and Zhang 2007, Debo et al 2008, Hopp et al 2007, Jouini et al 2008).…”

Section: Prior Research On Queue Management and Throughput Timesmentioning

confidence: 99%

“…However, some analytical models have shown that the structure of customer demand and behavioral responses of servers and customers can reduce the expected benefits of queue pooling (van Dijk and van der Sluis 2008, Hopp et al 2007, Loch 1998, Mandelbaum and Reiman 1998, Rothkopf and Rech 1987.…”

Section: Prior Research On Queue Management and Throughput Timesmentioning

confidence: 99%

“…For example, combining streams of customers who have different processing requirements can introduce inefficiencies which erode the benefits of pooling (Green and Nguyen 2001, Mandelbaum and Reiman 1998, Rothkopf and Rech 1987. Another factor that reduces the effectiveness of queue pooling is having strategic servers (Cachon and Zhang 2007, Debo et al 2008, Hopp et al 2007, Jouini et al 2008).…”

Section: Prior Research On Queue Management and Throughput Timesmentioning

confidence: 99%

See 2 more Smart Citations

The Diseconomies of Queue Pooling: An Empirical Investigation of Emergency Department Length of Stay

2015

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We conduct an empirical investigation of the impact of two different queue management systems on throughput times. Using an Emergency Department's (ED) patient-level data (N = 231,081) from 2007 to 2010, we find that patients' lengths of stay (LOS) were longer when physicians were assigned patients under a pooled queuing system, compared to when each physician operated under a dedicated queuing system. The dedicated queuing system resulted in a 10 percent decrease in LOS-a 32-minute reduction in LOS for an average patient of medium severity in this ED. We propose that the dedicated queuing system yielded shorter throughput times because it provided physicians with greater ability and incentive to manage their patients' flow through the ED from arrival to discharge. Consistent with social loafing theory, our analysis shows that patients were treated and discharged at a faster rate in the dedicated queuing system than in the pooled queuing system. We conduct additional analyses to rule out alternate explanations, such as stinting on care and decreased quality of care. Our paper has implications for health care organizations and others seeking to reduce throughput time, resource utilization, and costs.Key words: pooling, queue management, strategic servers, social loafing, empirical operations, health care 3 IntroductionImproving efficiency and customer experience are key objectives for managers of service organizations.Skillful application of operations management principles may prove helpful for achieving these goals. In this paper, we investigate queue management, a key operational decision. More specifically, we explore the impact on throughput time of using a pooled queuing system or a dedicated queuing system.Prior work on queue management has demonstrated through analytical models that pooling separate streams of identical customers into a single queue served by a bank of identical servers leads to a reduction in waiting time and an increase in server utilization (Eppen 1979, Kleinrock 1976. This occurs because pooling reduces the negative impact of variability in arrival times and processing times. Pooling enables incoming work to be processed by any one server from a bank of servers, which decreases the odds that an incoming unit of work will have to wait for service. This situation compares to the one where the unit of work can only be processed by a single dedicated server.Queuing theorists have advanced this stream of research by identifying conditions under which queue pooling may not yield the expected performance improvements (Debo et al. 2008, van Dijk and van der Sluis 2009, Hopp et al. 2007, Jouini et al. 2008, Loch 1998, Mandelbaum and Reiman 1998. However, few empirical studies have examined the performance of pooled versus dedicated queuing systems. This is an important omission because, in practice, employees can often make adjustments to how they manage their work system to achieve a goal, such as increasing their productivity (Hopp et al. 2009). Operations management scholars advocat...

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Section: Prior Research On Queue Management and Throughput Timesmentioning

confidence: 99%

Section: Prior Research On Queue Management and Throughput Timesmentioning

confidence: 99%

Section: Prior Research On Queue Management and Throughput Timesmentioning

confidence: 99%

See 1 more Smart Citation

The Diseconomies of Queue Pooling: An Empirical Investigation of Emergency Department Length of Stay

2015

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“…(In most of our analysis we do both.) As an example, pooling only queues would change several M/M/1 queues, say K, with arrival rate l and service rate m into an M/M/K queue with arrival rate Kl and service rate m; results of this flavor are contained in (Rothkopf andRech 1987 andSmith andWhitt 1981). Pooling only servers would change an M/M/K queue with arrival rate l and service rate m into an M/M/1 queue with arrival rate l and service rate Km; pooling of this type is considered in (Stidham 1970).…”

Section: Motivationmentioning

confidence: 99%

On Pooling in Queueing Networks

Mandelbaum

Reiman²

1998

Management Science

139

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W e view each station in a Jackson network as a queue of tasks, of a particular type, which are to be processed by the associated specialized server. A complete pooling of queues, into a single queue, and servers, into a single server, gives rise to an M/PH/1 queue, where the server is flexible in the sense that it processes all tasks. We assess the value of complete pooling by comparing the steady-state mean sojourn times of these two systems. The main insight from our analysis is that care must be used in pooling. Sometimes pooling helps, sometimes it hurts, and its effect (good or bad) can be unbounded. Also discussed briefly are alternative pooling scenarios, for example complete pooling of only queues which results in an M/PH/S system, or partial pooling which can be devastating enough to turn a stable Jackson network into an unstable Bramson network. We conclude with some possible future research directions.

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“…Although realistic models are too complex to be analyzed mathematically, the problem and solutions are related: overflow rules between queues can correspond to a dynamic usage of capacity. The definition of queues and servers [16,17], corresponds to the problem of defining patient groups and timeslot types. However, queuing systems do not consider specific timeslots and appointments, and therefore do not capture the full scheduling problem.…”

Section: Related Workmentioning

confidence: 99%