Fundamentals of queueing theory (2nd ed.).
Fundamentals of queueing theory (2nd ed.).
An analytical approach to the M/G/2 queue
Operations Research
Computational results of multiserver bulk-arrival queues with constant service time Mx/D/c
Operations Research - Supplement to Operations Research: stochastic processes
Models and algorithms for transient queueing congestion at airports
Management Science
Average waiting time of customers in an M/D/k queue with nonpreemptive priorities
Computers and Operations Research
Hub-and-spoke network design with congestion
Computers and Operations Research
The stochastic p-hub center problem with service-level constraints
Computers and Operations Research
Multiple allocation hub-and-spoke network design under hub congestion
Computers and Operations Research
A conditional p-hub location problem with attraction functions
Computers and Operations Research
e-Work based collaborative optimization approach for strategic logistic network design problem
Computers and Industrial Engineering
A Lagrangean Heuristic for Hub-and-Spoke System Design with Capacity Selection and Congestion
INFORMS Journal on Computing
Bicriteria p-Hub Location Problems and Evolutionary Algorithms
INFORMS Journal on Computing
Single allocation hub location problem under congestion: Network owner and user perspectives
Expert Systems with Applications: An International Journal
Twenty-Five Years of Hub Location Research
Transportation Science
Proceedings of the 2012 Symposium on Theory of Modeling and Simulation - DEVS Integrative M&S Symposium
An improved hybrid particle swarm optimization algorithm for fuzzy p-hub center problem
Computers and Industrial Engineering
Computers and Industrial Engineering
Solving fuzzy p-hub center problem by genetic algorithm incorporating local search
Applied Soft Computing
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Models are presented for the optimal location of hubs in airline networks, which take into consideration the congestion effects. Hubs, which are typically the most congested airports, are modeled as M/D/c queuing systems. A formula is derived for the probability of a number of customers in the system, which is later used to propose a capacity constraint. This constraint limits the probability of more than b airplanes in queue, to be smaller than or equal to a given value. Due to the computational complexity of the formulation, the model is solved using a heuristic based on tabu search. Computational experience is presented together with an example using a data set available in the literature.