TY - JOUR
T1 - Obtaining the optimal fleet mix
T2 - A case study about towing tractors at airports
AU - Du, Jia Yan
AU - Brunner, Jens O.
AU - Kolisch, Rainer
N1 - Publisher Copyright:
© 2015 Elsevier Ltd
PY - 2016/10/1
Y1 - 2016/10/1
N2 - Planes do not have a reverse gear. Hence, they need to be towed by tractors when leaving the gate. Towing tractors differ with respect to investment as well as variable costs and plane type compatibility. We propose a model which addresses the problem of a cost minimal fleet composition to support towing service providers in their strategic investment decisions. The model takes into account a maximum lifetime, a minimum duration of use, an overhaul option and a sell option. In a case study with a major European airport (our cooperating airport) we generate a multi-period fleet investment schedule. Furthermore, we introduce a 4-step approach for demand aggregation based on flight schedule information. We analyze the impact of demand variation, flight schedule disruptions and cost structure on the optimal buy, overhaul and sell policy. The scenario analyses demonstrate the robustness of the investment schedule with respect to these factors. Ignoring the existing fleet, a green field scenario reveals saving potentials of more than 5% when applying this model.
AB - Planes do not have a reverse gear. Hence, they need to be towed by tractors when leaving the gate. Towing tractors differ with respect to investment as well as variable costs and plane type compatibility. We propose a model which addresses the problem of a cost minimal fleet composition to support towing service providers in their strategic investment decisions. The model takes into account a maximum lifetime, a minimum duration of use, an overhaul option and a sell option. In a case study with a major European airport (our cooperating airport) we generate a multi-period fleet investment schedule. Furthermore, we introduce a 4-step approach for demand aggregation based on flight schedule information. We analyze the impact of demand variation, flight schedule disruptions and cost structure on the optimal buy, overhaul and sell policy. The scenario analyses demonstrate the robustness of the investment schedule with respect to these factors. Ignoring the existing fleet, a green field scenario reveals saving potentials of more than 5% when applying this model.
KW - Airport operations management
KW - Fleet composition problem
KW - Turnaround processes
UR - http://www.scopus.com/inward/record.url?scp=84952060881&partnerID=8YFLogxK
U2 - 10.1016/j.omega.2015.11.005
DO - 10.1016/j.omega.2015.11.005
M3 - Article
AN - SCOPUS:84952060881
SN - 0305-0483
VL - 64
SP - 102
EP - 114
JO - Omega (United Kingdom)
JF - Omega (United Kingdom)
ER -