TY - GEN
T1 - On static vs dynamic (switching of) operational policies in aircraft turnaround team allocation and management
AU - Saha, Sudipta
AU - Tomasella, Maurizio
AU - Cattaneo, Giovanni
AU - Matta, Andrea
AU - Padrón, Silvia
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2022/2/23
Y1 - 2022/2/23
N2 - Aircraft turnaround operations represent the fulcrum of airport operations. They include all services to be provided to an aircraft between two consecutive flights. These services are executed by human operators, often organised in teams, who employ some related equipment and vehicles (e.g. conveyor belts, trolleys and tugs for baggage loading/unloading and transportation). In this paper, we focus on the real-time management of turnaround operations, and assess the relative merits and limitations of so-called dispatching rules that originate from the manufacturing literature. More precisely, we focus on the real-time allocation, on the day of operation, of teams of ground handling operators to aircraft turnarounds. This is pursued from the viewpoint of third-party service providers. We employ simulation, in conjunction with deep reinforcement learning, and work on the case of a real airport and the entirety of its turnaround operations involving multiple service providers.
AB - Aircraft turnaround operations represent the fulcrum of airport operations. They include all services to be provided to an aircraft between two consecutive flights. These services are executed by human operators, often organised in teams, who employ some related equipment and vehicles (e.g. conveyor belts, trolleys and tugs for baggage loading/unloading and transportation). In this paper, we focus on the real-time management of turnaround operations, and assess the relative merits and limitations of so-called dispatching rules that originate from the manufacturing literature. More precisely, we focus on the real-time allocation, on the day of operation, of teams of ground handling operators to aircraft turnarounds. This is pursued from the viewpoint of third-party service providers. We employ simulation, in conjunction with deep reinforcement learning, and work on the case of a real airport and the entirety of its turnaround operations involving multiple service providers.
UR - http://meetings2.informs.org/wordpress/wsc2021/
UR - https://informs-sim.org/
U2 - 10.1109/WSC52266.2021.9715316
DO - 10.1109/WSC52266.2021.9715316
M3 - Conference contribution
SN - 9781665433129
T3 - Proceedings - Winter Simulation Conference
BT - Proceedings of the 2021 Winter Simulation Conference
A2 - Kim, S.
A2 - Feng, B.
A2 - Smith, K.
A2 - Masoud, S.
A2 - Zheng, Z.
A2 - Szabo, C.
A2 - Loper, M.
PB - Institute of Electrical and Electronics Engineers
ER -