In this paper, researchers focus on the real-time management of turnaround operations, and assess the relative merits and limitations of so-called dispatching rules.
The aircraft turnaround process consists of "ground handling operations" that are required to set an aircraft ready for its subsequent departure. This work focuses on enhancing the planning and real-time allocation of apron operations that are used to turn around an aircraft between two consecutive flights.
Aircraft ground handling involves 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. 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.
The research team proposes a tactical robust scheme by which ground handlers and the airport operator cooperate, although 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).
Capacity analysis for systems with time varying constraints is still an open problem in Operations Research due to the non-stationarity of the problem domain. This is particularly true for Defence manpower supply which is subject to frequent temporal policy and resource changes. As such, the problem cannot be completely covered with a single overriding simulation or optimisation solution, but, rather, better described using piecewise interplay between simulation and optimisation. This paper describes such an approach for a flexible, interactive capacity analysis simulator with an embedded integer linear programming (ILP) optimiser.
The airline industry faces many causes of disruption. To minimise financial and reputational impact, the airline must adapt its schedules. Due to the complexity of the environment, simulation is a natural modelling approach. However, the large solution space, time constraints and system constraints make the search for revised schedules difficult. This paper presents a method for the aircraft recovery problem that uses multi-fidelity modelling including a trust region simulation optimisation algorithm to mitigate the computational costs of using high-fidelity simulations with its benefits for providing good estimates of the true performance.
Airports are intermodal hubs and natural interfaces between ground transport and air transport. In the current DLR project “Optimode.net”, an innovative approach is being developed to extend the management of an airport not only to airport landside and terminal processes but to go even further and incorporate feeder traffic in the management of airport processes. Thus providing travelers with a real door-to-door service and letting airport stakeholders benefit from efficient airport management. Technical core of the project is a simulation environment consisting of nine different simulation models with various simulation methods and abstraction levels. In this paper the simulation environment of a multi airport region which is used in the Optimode.net project will be described in detail and also the interaction of the different simulation modules will be explained. We will also show how this complex simulation environment is used to foster individual door-to-door travel and proactive airport management.
Sortie Generation Rate (SGR) is an important metric for air dominance. Lockheed Martin must demonstrate that the Air System can fly the sorties during an allotted time and deliver the capability to the war fighter. Aircraft turnaround time- the time between when the aircraft touches down, refuels, rearms, and completes inspections in order to release the aircraft, to aircraft wheels up - plays an important role in achieving the SGR requirement.
Contemporary aerospace programmes often suffer from large cost overruns, delivery delays and inferior product quality. This is caused in part by poor predictive quality of the early design phase processes with regards to the operational environment of a product. This paper develops the idea of a generic operational simulation that can help designers to rigorously analyse and test their early product concepts. The simulation focusses on civil Unmanned Air Vehicle products and missions to keep the scope of work tractable.
Product complexity in the aerospace industry has grown fast while design procedures and techniques did not keep pace. Product life cycle implications are largely neglected during the early design phase. Also, aerospace designers fail to optimize products for the intended operational environment. This study shows how a design, simulated within its anticipated operational environment, can inform about critical design parameters, thereby creating a more targeted design improving the chance of commercial success. An agent-based operational simulation for civil Unmanned Aerial Vehicles conducting maritime Search-andRescue missions is used to design and optimize aircrafts. Agent interactions with their environment over the product life-cycle are shown to lead to unexpected model outputs. Unique insights into the optimal design are gained by analysis of the operational performance of the aircraft within its simulated environment
Agent-based modelling is gaining popularity for investigating the behaviour of complex systems involving interactions of many players or agents. In this paper an agent-based simulation modelling technique is applied to understand the long term implications of strategy decisions for an aerospace value chain. The industry has unique elements including new business models, high levels of collaboration, long product lifecycles and long periods before positive paybacks are realised. Forecasting market conditions over this long term lifespan is inherently problematic and adds further complexity when devising a strategy. The model described includes all the major players and entities in the value chain and their interactions. Illustrative results are presented to demonstrate how the simulation approach can be used to evaluate strategy and policy decisions and their operational implications over the long term