Topological network design of general, finite, multi-server queueing networks

Smith, J. MacGregor; Cruz, Frederico R. B.; Woensel, van T Tom

doi:10.1016/j.ejor.2009.03.012

Cited by 32 publications

(12 citation statements)

References 47 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Generalized Expansion Method was used as evaluative method and Powell's algorithm was used as generative technique. Similar work was done by Smith et al [3] for multi-server queuing networks.…”

Section: Generative Methods Evaluative Methodssupporting

confidence: 54%

Optimal Buffer Allocation in Tandem Closed Queing Network with Multi Servers Using PSO

K.L¹,

Reddy²,

Rao³

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

show abstract

Section: Generative Methods Evaluative Methodssupporting

confidence: 54%

Optimal Buffer Allocation in Tandem Closed Queing Network with Multi Servers Using PSO

K.L¹,

Reddy²,

Rao³

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

show abstract

“…This section presents almost all the notations we need for this paper. Figure 3 is a useful reference for the notation.˜ j is the effective arrival rate to node j = j (1− p K ), is the external Poisson arrival rate to the network, j (S) is the mean service rate at node j, c is the vector number of servers, ∈ (0, 1) is the threshold for the blocking probability, B j is the buffer capacity at node j excluding those in service, K j is the buffer capacity at node j including those in service, N is the number of stations in the network, p K is the blocking probability of finite queue of size K , p j 0 is the unconditional probability that there is no customer in the service channel at node j (either being served or being held after service), =˜ /( c) is the proportion of time each server is busy, 2 is the squared coefficient of variation of the service time, T s , x is the server allocation vector of decision variables in the optimization routine, is the mean throughput rate, and is the threshold mean throughput rate.…”

Section: Notationmentioning

confidence: 99%

“…The approach we take is to examine the convexity of the blocking probability within the range of the parameters felt to be important in the research under study. We shall assume that <1 and that the range of the number of servers is c = [2,10]. The fact that <1 is related to the fact that beyond 1, it appears that the general performance measures may no longer be convex.…”

Section: Derivation Of Concave Approximation Functionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal server allocation in general, finite, multi‐server queueing networks

Smith

Cruz

Woensel

2010

Appl Stoch Models Bus & Ind

Self Cite

View full text Add to dashboard Cite

SUMMARYQueueing networks with finite buffers, multiple servers, arbitrary acyclic, series-parallel topologies, and general service time distributions are considered in this paper. An approach to optimally allocate servers to series, merge, and split topologies and their combinations is demonstrated. The methodology builds on two-moment approximations to the service time distribution embedded in the generalized expansion method for computing the performance measures in complex finite queueing networks and Powell's algorithm for optimally allocating servers to the network topology. Convexity of the objective function along with results from computational experiments is presented for showing the efficacy of the methodology.

show abstract

“…It is a method of general expansion that is used under certain assumptions and can be used for service times in general. This method was applied to solve series and merge and split topologies of production lines with finite buffers [30,31].…”

Section: Complexitymentioning

confidence: 99%

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

et al. 2018

View full text Add to dashboard Cite

The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for different number of buffer slots and workstations were used to obtain a fourth-order mathematical model and four hidden layers' artificial neural network. Both models have a good performance in predicting the throughput, although the artificial neural network model shows a better fit ( = 1.0000) against the response surface methodology ( = 0.9996). Moreover, the artificial neural network produces better predictions for data not utilized in the models construction. Finally, this study can be used as a guide to forecast the maximum or near maximum throughput of production lines taking into account the buffer size and the number of machines in the line.

show abstract

Topological network design of general, finite, multi-server queueing networks

Cited by 32 publications

References 47 publications

Optimal Buffer Allocation in Tandem Closed Queing Network with Multi Servers Using PSO

Optimal Buffer Allocation in Tandem Closed Queing Network with Multi Servers Using PSO

Optimal server allocation in general, finite, multi‐server queueing networks

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

Contact Info

Product

Resources

About