Solving semi-open queuing networks with time-varying arrivals: An application in container terminal landside operations

Dhingra, Vibhuti; Kumawat, Govind Lal; Roy, Debjit; Koster, René de

doi:10.1016/j.ejor.2017.12.020

Cited by 42 publications

(25 citation statements)

References 32 publications

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“…Although some research has focused on the design of container terminal seaside operations, studies that analyze landside operations are limited. Those that do, do not focus on the congestion at the shared resources (see Carlo et al (2013), Gharehgozli et al (2015), Roy and De Koster (2018), Dhingra et al (2018), Roy et al (2019)). Dhingra et al (2018) developed a container terminal model including truck operations with time-varying truck arrival rates.…”

Section: Figurementioning

confidence: 99%

“…Those that do, do not focus on the congestion at the shared resources (see Carlo et al (2013), Gharehgozli et al (2015), Roy and De Koster (2018), Dhingra et al (2018), Roy et al (2019)). Dhingra et al (2018) developed a container terminal model including truck operations with time-varying truck arrival rates. Some other papers that also examine landside operations do not really focus on the internal operations of container terminals (see e.g., Giuliano and O'Brien (2007)).…”

Section: Figurementioning

confidence: 99%

“…These models primarily consider the seaside processes only. Dhingra et al (2018) model ET arrivals at the landside of the terminal using a two-phase Markov-modulated Poisson process and estimate the number of trucks that should be allowed in the terminal. However, the container train arrivals, and the interaction with the train containers are not included in the model.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Effect of Service Priority and Resource Synchronization Choices on Landside Terminal Queues: Exact Analysis and Approximations

Roy

Ommeren²,

Koster

et al. 2020

SSRN Journal

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With the growth of ocean transport and with increasing vessel sizes, managing congestion at the landside of container terminals has become a major challenge. A terminal landside handles containers that arrive or depart via train or truck. Large terminals have to handle thousands of trucks and dozens of trains per day. As trains run on fixed schedule, their containers are prioritized in stacking and internal transport handling. This has consequences for the service of other modes, which might be subject to delays. We analyze the dynamic interactions between the landside resources using a stochastic stylized semi-open queuing network model with bulk arrivals, shared resources, and multi-class containers. We use the theory of regenerative processes and Markov chain analysis to analyze the network. The proposed network solution algorithm works for large-scale systems and yields sufficiently accurate estimates for performance measurement. The model can capture priority service for containers at shared resources (such as stack cranes), while preserving strict handling priorities. The model is used to explore the choice of different internal transport vehicles (coupled versus decoupled operations at stack and train gantry cranes) to understand the effect on delays. Our results show that decoupled transport resources can mitigate both the delays of containers that arrive by trucks and by trains. When train arrival rates are low, prioritizing the handling of train containers at the stack cranes significantly reduces their delays. Further, this priority has little effect on the delays of handling external truck containers.

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Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Effect of Service Priority and Resource Synchronization Choices on Landside Terminal Queues: Exact Analysis and Approximations

Roy

Ommeren²,

Koster

et al. 2020

SSRN Journal

Self Cite

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“…However, the exact analysis even of the simplest network, with two exponential single-server nodes, Poisson arrival process, and an infinite queue, remains a difficult task [15]. The semi-open networks with external queue are mainly analyzed by approximate methods and the matrix-geometric method [19][20][21][22][23].…”

Section: Semi-open Network With Replacementmentioning

confidence: 99%

On Truncation of the Matrix-Geometric Stationary Distributions

2019

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In this paper, we study queueing systems with an infinite and finite number of waiting places that can be modeled by a Quasi-Birth-and-Death process. We derive the conditions under which the stationary distribution for a loss system is a truncation of the stationary distribution of the Quasi-Birth-and-Death process and obtain the stationary distributions of both processes. We apply the obtained results to the analysis of a semi-open network in which a customer from an external queue replaces a customer leaving the system at the node from which the latter departed.

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“…At the same time, the majority of the research is implemented under the assumption that the arrivals occur according to a stationary Poisson process that has a constant arrival rate. Quite a short list of papers dealing with queueing networks with Markovian arrival process (not relating with car sharing) can be found in recent papers by [12,14] and references therein. A possibility of transition of servers between the nodes is not considered in these papers.…”

Section: Introductionmentioning

confidence: 99%

Queueing Network with Moving Servers as a Model of Car Sharing Systems

2019

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We consider a queueing network with a finite number of nodes and servers moving between the nodes as a model of car sharing. The arrival process of customers to various nodes is defined by a marked Markovian arrival process. The customer that arrives at a certain node when there is no idle server (car) is lost. Otherwise, he/she is able to start the service. With known probability, which depends on the node and the number of available cars, this customer can balk the service and leave the system. The service time of a customer has an exponential distribution. Location of the server in the network after service completion is random with the known probability distribution. The behaviour of the network is described by a multi-dimensional continuous-time Markov chain. The generator of this chain is derived which allows us to compute the stationary distribution of the network states. The formulas for computing the key performance indicators of the system are given. Numerical results are presented. They characterize the dependence of some performance measures of the network and the nodes on the total number of cars (fleet size of the car sharing system) and correlation in the arrival process.

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Solving semi-open queuing networks with time-varying arrivals: An application in container terminal landside operations

Cited by 42 publications

References 32 publications

Effect of Service Priority and Resource Synchronization Choices on Landside Terminal Queues: Exact Analysis and Approximations

Effect of Service Priority and Resource Synchronization Choices on Landside Terminal Queues: Exact Analysis and Approximations

On Truncation of the Matrix-Geometric Stationary Distributions

Queueing Network with Moving Servers as a Model of Car Sharing Systems

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