Server allocation for zero buffer tandem queues

Yarmand, Mohammad H.; Down, Douglas G.

doi:10.1016/j.ejor.2013.05.029

Cited by 10 publications

(4 citation statements)

References 20 publications

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“…In case the values of μ i (v i − 1) are equal for more than one station, choose the one with the lowest μ i first. See Yarmand and Down (2013) for further details.…”

Section: Heterogeneous Wmentioning

confidence: 99%

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Maximizing throughput in zero-buffer tandem lines with dedicated and flexible servers

Yarmand¹,

Down²

2014

IIE Transactions

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For tandem queues with no buffer spaces and both dedicated and flexible servers, this article studies how flexible servers should be assigned to maximize the throughput. The optimal policy is completely characterized. Insights gained from applying the Policy Iteration algorithm to systems with three, four, and five stations are used to devise heuristics for systems of arbitrary size. These heuristics are verified by numerical analysis. Throughput improvement obtained when, for a given server assignment, dedicated servers are changed to flexible servers.

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“…In case the values of μ i (v i − 1) are equal for more than one station, choose the one with the lowest μ i first. See Yarmand and Down (2013) for further details.…”

Section: Heterogeneous Wmentioning

confidence: 99%

“…On the other hand, Policy II makes use of the server multiplicity effect (Yarmand and Down, 2013). This effect suggests that comparing two stations with equal total mean service times, the station with a higher number of servers leads to higher throughput.…”

Section: Heterogeneous Wmentioning

confidence: 99%

Maximizing throughput in zero-buffer tandem lines with dedicated and flexible servers

Yarmand¹,

Down²

2014

IIE Transactions

Self Cite

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“…In addition to general finite buffers, we will also investigate the system with zero intermediate buffer, that is, B 2 = 0. Two‐station queueing systems with zero intermediate buffer (Kırkızlar, Andradóttir, & Ayhan, ; Yarmand & Down, ; Yarmand & G, ) or unlimited intermediate buffer (Pandelis, ) have attracted the attention of researchers on the server allocation problem. Unlike our work, these studies do not address the optimal pricing problem.…”

Section: Introductionmentioning

confidence: 99%

Revenue maximization in two‐station tandem queueing systems

Wang

Andradóttir

Ayhan

et al. 2020

Naval Research Logistics

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We study optimal pricing for tandem queueing systems with finite buffers. The service provider dynamically quotes prices to incoming price sensitive customers to maximize the long‐run average revenue. We present a Markov decision process model for the optimization problem. For systems with two stations, general‐sized buffers, and two or more prices, we describe the structure of the optimal dynamic pricing policy and develop tailored policy iteration algorithms to find an optimal pricing policy. For systems with two stations but no intermediate buffer, we characterize conditions under which quoting either a high or a low price to all customers is optimal and provide an easy‐to‐implement algorithm to solve the problem. Numerical experiments are conducted to compare the developed algorithms with the regular policy iteration algorithm. The work also discusses possible extensions of the obtained results to both three‐station systems and two‐station systems with price and congestion sensitive customers using numerical analysis.

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“…Since then, many authors have incorporated this feature in their studies. We cite the recent papers of Dimitriou [16], Wu and Lian [8], Choudhury and Ke [4], 2 Advances in Decision Sciences Ke et al [17], Yang et al [18], Yarmand and Down [19], Kumar et al [3], and Zhang and Wang [20].…”

Section: Introductionmentioning

confidence: 99%

Binomial Schedule for an M/G/1 Type Queueing System with an Unreliable Server under N-Policy

Tadj

Yoon

2014

Advances in Decision Sciences

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We consider in this paper an M/G/1 type queueing system with the following extensions. First, the server is unreliable and is subject to random breakdowns. Second, the server also implements the well-known N-policy. Third, instead of a Bernoulli vacation schedule, the more general notion of binomial schedule with K vacations is applied. A cost function with two decision variables is developed. A numerical example shows the effect of the system parameters on the optimal management policy.

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Server allocation for zero buffer tandem queues

Cited by 10 publications

References 20 publications

Maximizing throughput in zero-buffer tandem lines with dedicated and flexible servers

Maximizing throughput in zero-buffer tandem lines with dedicated and flexible servers

Revenue maximization in two‐station tandem queueing systems

Binomial Schedule for an M/G/1 Type Queueing System with an Unreliable Server under N-Policy

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