17 research outputs found
On static vs dynamic (switching of) operational policies in aircraft turnaround team allocation and management
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
The dynamic bowser routing problem
We investigate opportunities offered by telematics and analytics to enable
better informed, and more integrated, collaborative management decisions on
construction sites. We focus on efficient refuelling of assets across
construction sites. More specifically, we develop decision support models that,
by leveraging data supplied by different assets, schedule refuelling operations
by minimising the distance travelled by the bowser truck as well as fuel
shortages. Motivated by a practical case study elicited in the context of a
project we recently conducted at Crossrail, we introduce the Dynamic Bowser
Routing Problem. In this problem the decision maker aims to dynamically refuel,
by dispatching a bowser truck, a set of assets which consume fuel and whose
location changes over time; the goal is to ensure that assets do not run out of
fuel and that the bowser covers the minimum possible distance. We investigate
deterministic and stochastic variants of this problem and introduce effective
and scalable mathematical programming models to tackle these cases. We
demonstrate the effectiveness of our approaches in the context of an extensive
computational study designed around data collected on site as well as supplied
by our project partners.
Keywords: Routing; Dynamic Bowser Routing Problem; Stochastic Bowser Routing
Problem; Mixed-Integer Linear Programming; Construction
Enhanced Operational Management of Airport Ground Support Equipment for Better Aircraft Turnaround Performance
Within the context of airport operations, this work focuses on enhancing the planning and real-time allocation
of certain resources that are used to turn around an aircraft between two consecutive flights. This sort of
operations takes place in the area of an airport that is called its apron. At peak times in particular, and when
resource capacity is really tight, apron operations tend to be affected by either unavailability or late arrival
of certain assets at the stand. The key element of this paper is the proposal of a new resource booking
system, which operates in real-time, and deals with the related uncertainties. The booking mechanism aims
to allow the airlines to book in advance certain resources, in particular ground support equipment. Our
choice of a real case study will help us to assess the likely benefits, pros and cons of this system
On static vs dynamic (switching of) operational policies in aircraft turnaround team allocation and management
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