Approximations for the waiting-time distribution in an $$M/PH/c$$ M / P H / c priority queue

Hanbali, Ahmad Al; Alvarez, E. M.; Heijden, Matthijs C. van der

doi:10.1007/s00291-015-0388-9

Cited by 12 publications

(4 citation statements)

References 19 publications

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“…The availability of service engineers is one of the main bottlenecks in ensuring that the service level agreements are met. Al Hanbali et al (2015) consider human resources, where they focus on the assignment of a set of engineers to a group of customers with varying service level requirements. The authors analyze a non-preemptive M PH c / / priority queue with various customer classes.…”

Section: Service Engineersmentioning

confidence: 99%

Joint optimization of spare parts inventory and service engineers staffing with full backlogging

Rahimi-Ghahroodi

Hanbali

Vliegen

et al. 2019

International Journal of Production Economics

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Section: Service Engineersmentioning

confidence: 99%

Joint optimization of spare parts inventory and service engineers staffing with full backlogging

Rahimi-Ghahroodi

Hanbali

Vliegen

et al. 2019

International Journal of Production Economics

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“…Since then, several interesting approaches have been proposed aiming to lift the restriction of identical and exponentially distributed service times in the solution of priority M/M/c queues. More recently, Al Hanbali et al [ALH15] removed the constraint of exponential service times by proposing an approximate solution to evaluate the first two moments of the waiting time in a non-preemptive M/G/c priority queue with identical service time distributions over all the classes. In the same year, as mentioned above, Wang et al [WAN15] relaxed the constraint of having identical service rate over all the classes by providing the exact analysis of a preemptive M/M/c queue with two priority classes having different service rates.…”

Section: Introductionmentioning

confidence: 99%

Multi-server preemptive priority queue with general arrivals and service times

Brandwajn

Begin

2017

Performance Evaluation

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International audienceWe present a simple approximate solution for preemptive-resume queues with multiple servers, general (phase-type) service and general (phase-type) interarrival time distributions. In our solution, priority levels are solved one at a time in the order of decreasing priorities. Each priority level is solved approximately using a reduced state description. The complexity of our approximate solution in terms of the number of equations solved grows linearly with the number of servers and priority levels. We studied a large number of numerical examples with a range of values for mean service times and offered loads across priority levels, varying the number of servers from 8 to 48. Discrete-event simulation was used to assess the accuracy of our approximate solution. Overall, in the case of Poisson and quasi-Poisson arrivals, expected relative error for the mean number of customers in the system was below 2% while the corresponding median relative error was below 0.25%. The good accuracy of our approximation appears to extend to the case of phase-type times between arrivals, with expected relative errors for the mean number in system below 5% even for a Pareto-like distribution of interarrival times with a large coefficient of variation. Our numerical results indicate that the proposed approximation provides a relatively simple and generally accurate approach to preemptive-resume queues with larger numbers of servers and general distributions of service and interarrival times

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“…Moreover, we divide the [35]. Furthermore, the concept of multi-server multiclass M/G/s systems has been examined in several studies considering various priority queue disciplines, other than FIFO [36] - [38]. However, to the best of our knowledge, it is the first time that a multi-server multiclass M/G/s queuing system is handled considering a FIFO discipline.…”

Section: Introductionmentioning

confidence: 99%

Analysis and quality of service evaluation of a fast charging station for electric vehicles

Zenginis

Vardakas

Zorba

et al. 2016

Energy

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Electrification of transportation is considered as one of the most promising ways to mitigate climate change and reduce national security risks from oil and gasoline imports. Fast Charging Stations (FCS) that provide high Quality of Service (QoS) will facilitate the wide market penetration of Electric Vehicles (EVs). In this paper, we analyze the operation of a FCS by employing a novel queuing model. Our analysis considers that the various EV models are divided into classes based on their battery size; then we compute the EVs' mean waiting time in the queue, taking into account the number of Charging Spots (CS) of the FCS, as well as the stochastic arrival process and the stochastic recharging needs of the various EV classes. Furthermore, the high precision of our analysis is confirmed through simulations. Therefore, our model may be utilized by existing FCS operators that need to provide high QoS, or by future investors for an efficient installation design.

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Approximations for the waiting-time distribution in an $$M/PH/c$$ M / P H / c priority queue

Cited by 12 publications

References 19 publications

Joint optimization of spare parts inventory and service engineers staffing with full backlogging

Joint optimization of spare parts inventory and service engineers staffing with full backlogging

Multi-server preemptive priority queue with general arrivals and service times

Analysis and quality of service evaluation of a fast charging station for electric vehicles

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