| People | Locations | Statistics |
|---|---|---|
| Ziakopoulos, Apostolos | Athens |
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| Vigliani, Alessandro | Turin |
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| Catani, Jacopo | Rome |
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| Statheros, Thomas | Stevenage |
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| Utriainen, Roni | Tampere |
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| Guglieri, Giorgio | Turin |
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| Martínez Sánchez, Joaquín |
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| Tobolar, Jakub |
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| Volodarets, M. |
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| Piwowar, Piotr |
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| Tennoy, Aud | Oslo |
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| Matos, Ana Rita |
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| Cicevic, Svetlana |
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| Sommer, Carsten | Kassel |
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| Liu, Meiqi |
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| Pirdavani, Ali | Hasselt |
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| Niklaß, Malte |
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| Lima, Pedro | Braga |
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| Turunen, Anu W. |
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| Antunes, Carlos Henggeler |
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| Krasnov, Oleg A. |
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| Lopes, Joao P. |
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| Turan, Osman |
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| Lučanin, Vojkan | Belgrade |
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| Tanaskovic, Jovan |
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Bruneel, Herwig
in Cooperation with on an Cooperation-Score of 37%
Topics
- vehicle
- customer
- queuing
- highway traffic
- intersection
- motivation
- turning lane
- road
- variable
- production
- employee
- communication system
- public transport
- theory
- comfort
- estimating
- density
- submarine
- traffic density
- travel
- engineering
- neighborhood
- traffic flow
- transient
- travel time
- local traffic
- flux
- traffic model
- fluid
- commuter
- game theory
- waiting time
- Statistic
- infrastructure
- chain
- contrast
- Markov chain
- airport
- security
- automobile
- estimate
- filter
- T intersection
- show 13 more
Publications
- 2022Performance analysis of a continuous-time two-class global first-come-first-served queue with two servers and presortingcitations
- 2016Discrete-time queues with variable service capacity: a basic model and its analysiscitations
- 2015Public vs. personal transportation: a rational choice based on queueing theory
- 2014Rush hour roulette and the public transport choice
- 2014A continuous-time queueing model with class clustering and global FCFS service disciplinecitations
- 2011Road splits: smooth or rough passage?
Places of action
document
Discrete-time queues with variable service capacity: a basic model and its analysis
Abstract
In this paper, we present a basic discrete-time queueing model whereby the service process is decomposed in two (variable) components: the demand of each customer, expressed in a number of work units needed to provide full service of the customer, and the capacity of the server, i.e., the number of work units that the service facility is able to perform per time unit. The model is closely related to multi-server queueing models with server interruptions, in the sense that the service facility is able to deliver more than one unit of work per time unit, and that the number of work units that can be executed per time unit is not constant over time. Although multi-server queueing models with server interruptions—to some extent—allow us to study the concept of variable capacity, these models have a major disadvantage. The models are notoriously hard to analyze and even when explicit expressions are obtained, these contain various unknown probabilities that have to be calculated numerically, which makes the expressions difficult to interpret. For the model in this paper, on the other hand, we are able to obtain explicit closed-form expressions for the main performance measures of interest. Possible applications of this type of queueing model are numerous: the variable service capacity could model variable available bandwidths in communication networks, a varying production capacity in factories, a variable number of workers in an HR-environment, varying capacity in road traffic, etc.
Topics
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