Sttar: A Simheuristics-Enabled Scheme for Multi-Stakeholder Coordination Of Aircraft Turnaround Operations

Abstract

Aircraft turnaround operations involve all services to an aircraft (e.g. passenger boarding/disembarking, re-fuelling, deicing) between its arrival and immediately following departure. The aircraft, parked at its stand, witnesses a number of service providers move around it to perform their duties. These companies run substantially independent operations, working for different airlines/flights within a confined area where many resources, including physical space itself, have inescapably to be shared. Inter-dependencies among service providers abound, and knock-on effects at disrupted times are rife. Coordination from the side of the airport operator is difficult. We envisage a tactical robust scheme whereby ground handlers and the airport operator cooperate, albeit indirectly, in the development of plans for the next day that are less likely to be impacted by at least the more frequent operational disruptions. The scheme is based on a simheuristic approach which integrates ad-hoc heuristics with a hybrid simulation model (agent-based/discrete-event). MOTIVATIONDemand for commercial air transport continues to grow. Passenger volume grew globally 7.1% in 2016 and is expected to more than double by 2035 (IATA 2017), which will require a strong infrastructure and operational efficiency. Additionally, there is a push to move to aircraft 4D trajectory in the US NextGen and Single European Sky initiatives, in which aircraft movement would no longer be restricted to the current node to node concept, but could exist in any xyz space coordinate at any time. This would increase the capacity of aircraft in the airspace (Schultz 2018) tremendously, adding to the need to optimize ground operational efficiency for the turnaround of aircraft in between ever more frequent flights.Ground handling consists of all of the processes that occur between the time that the aircraft reaches the allocated 'block' at the airport (i.e. its parking stand/position) and the time at which the aircraft departs for its next flight. This has been deemed the second most common reason for delays to flights (Van Leeuwen and Witteveen 2009), which means that ground handling service providers (SPs) are in need of support to

Constraint-based robust planning and scheduling of airport apron operations through simheuristics

“…Planning multiple kinds of resources from different SPs requires sharing sensitive knowledge and information (e.g., asset location in real-time) between the concerned actors-a significant limitation. An alternative protocol is discussed by Tomasella et al (2019) to plan turnaround teams under uncertainty. A central, airport-wide, entity verifies the suitability of the schedule developed independently by each SP, and feeds back to them certain performance estimates to suggest where a different plan would suit each SP, or the airport, any better.…”

Stochastic Delay Cost Functions to Estimate Delay Propagation Under Uncertainty

“…Some studies further consider dynamic cost indexing, i.e., speeding up the flight cruise segment to reduce arrival delays [33] and some compare it to retiming the departure of other aircraft to ensure passenger transfers [34]- [38]. Other approaches have a local focus and consider recovery options mostly during the turnaround at a major airport, which includes the possibility to shorten or omit entire sub-processes, or assign extra resources to speed up standard operating procedures [5], [39]- [44].…”

“…The variety of approaches is also reflected by the incorporated objectives, which aim at minimising flight and/or passenger delays [42], [44]- [46], while others set out to optimise the associated cost of delay and recovery [5], [30], [32], [33], [35], [43], [47]. The latter objective includes the optimal assignment of recovery options to those flights with the highest priority (business utility), which is reflected by a progressive increase of costs at higher departure delays [7], [46].…”

Simulation as a Decision Support Tool for Airport Planning: Riga International Airport Case Study

“…Aircraft turnaround operations including passenger boarding, disembarking and re-fuelling were taken into account by a robust tactical scheme for efficient resource allocation in cases of operational disruptions. The scheme is based on a combination of ad-hoc heuristics with an agent-based simulation model (Tomasella et al 2019). A general approach for modelling airport operations was presented by Scala et al (2019).…”