Research output: Contribution to conference - Without ISBN/ISSN › Abstract
ADP strategies for resource allocation at congested airports. / Shone, Robert; Glazebrook, Kevin David; Zografos, Konstantinos.
2018. Abstract from StochMod 2018.Research output: Contribution to conference - Without ISBN/ISSN › Abstract
}
TY - CONF
T1 - ADP strategies for resource allocation at congested airports
AU - Shone, Robert
AU - Glazebrook, Kevin David
AU - Zografos, Konstantinos
PY - 2018
Y1 - 2018
N2 - In modern transportation systems there exists a need to develop fast, responsive and easily adaptable methods for computing optimal (or near-optimal) solutions to problems in which resources must be allocated dynamically in order to satisfy time-varying demands from multiple sources. In this talk we consider the case of a single airport which, in response to a pre-determined schedule of arrivals and departures, must use its runway capacity efficiently in order to minimise an objective function based on weighted second moments of aircraft queue lengths.In keeping with a well-established convention in the literature, we model departures and arrivals as independent stochastic queues with time-varying arrival and service rates. Service times are assumed to follow Erlang distributions, whereas for the arrival distributions we consider two possible cases: non-homogeneous Poisson processes and pre-scheduled arrivals with random deviations. We discuss how to formulate the problem of optimising airport capacity usage as a Markov decision process (MDP), and introduce a “surrogate problem” which closely resembles our original problem during periods of heavy demand. We then show that, in our surrogate problem, the MDP value function can be represented as a quadratic function of the state variables, and use this principle to develop ADP strategies for optimising capacity utilisation.
AB - In modern transportation systems there exists a need to develop fast, responsive and easily adaptable methods for computing optimal (or near-optimal) solutions to problems in which resources must be allocated dynamically in order to satisfy time-varying demands from multiple sources. In this talk we consider the case of a single airport which, in response to a pre-determined schedule of arrivals and departures, must use its runway capacity efficiently in order to minimise an objective function based on weighted second moments of aircraft queue lengths.In keeping with a well-established convention in the literature, we model departures and arrivals as independent stochastic queues with time-varying arrival and service rates. Service times are assumed to follow Erlang distributions, whereas for the arrival distributions we consider two possible cases: non-homogeneous Poisson processes and pre-scheduled arrivals with random deviations. We discuss how to formulate the problem of optimising airport capacity usage as a Markov decision process (MDP), and introduce a “surrogate problem” which closely resembles our original problem during periods of heavy demand. We then show that, in our surrogate problem, the MDP value function can be represented as a quadratic function of the state variables, and use this principle to develop ADP strategies for optimising capacity utilisation.
KW - Queueing; Approximate dynamic programming; Airport operations
M3 - Abstract
T2 - StochMod 2018
Y2 - 13 June 2018 through 15 June 2018
ER -