Location models for airline hubs behaving as M/D/c queues

Abstract

Models are presented for the optimal location of hubs in airline networks, that take into consideration the congestion effects. Hubs, which are the most congested airports, are modeled as M/D/c queuing systems, that is, Poisson arrivals, deterministic service time, and c servers. A formula is derived for the probability of a number of customers in the system, which is later used to propose a probabilistic constraint. This constraint limits the probability of b airplanes in queue, to be lesser than a value α. Due to the computational complexity of the formulation, The model is solved using a meta-heuristic based on tabu search. Computational experience is presented.

A conditional p-hub location problem with attraction functions

“…An example of capacity constraint that limits waiting time at hubs is presented by Marianov and Serra [16].…”

2-Stage stochastic programming approach for hub location problem under uncertainty: A case study of air network of Iran

“…Marianov and Serra (2003) focus on stochasticity at the hub nodes by representing hub airports as M/D/c queues; in their model, there is a constraint that limits the probability of more than a certain number of airplanes in a queue, to smaller than or equal to a given value, and then they solved this model by using a heuristic based on Tabu Search and tested on the well-known CAB data set. This data set was introduced by O' Kelly (1987) and is based on the airline passenger network of 25 US cities in 1970 evaluated by the Civil Aeronautics Board (CAB).…”

Meta-heuristic solution approaches for robust single allocation p-hub median problem with stochastic demands and travel times

“…Marianov and Serra [20] studied stochasticity at the hub nodes by considering the hub airports as M/D/C queuing systems and limiting the number of airplanes in a queue at the hub airports via chance constraints. The proposed model of this study was solved by a heuristic method based on tabu Search (TS) procedure.…”