Advanced Disruption Management

Advanced Disruption management

Article by Ian Evans

November 10, 2010

Constraint Technologies

Airline disruption management has been the subject of extensive research and development over the past 20 years. Currently several commercial products are available that integrate management of fleet, crew, passengers, freight and scheduled maintenance into a single real time decision support tool that allows rapid visualisation of the current situation and the effects of planned recovery.

Advanced optimisation technology can assist the operations controller to select better recovery options. This technology is now becoming available for other transport modes such as rail – meaning that advanced disruption management will soon become ubiquitous.

Disruption management consists of 3 stages - situational awareness to understand the current situation and determine if schedule recovery is required, creation and evaluation of recovery scenarios, and finally the publishing of updates to the schedule. Each operator performs disruption management for their own fleets and crews within limits set by the network controller (e.g. Air Traffic Control for aviation).

For situational awareness, the operations controller will typically use one prime means of visualisation, which in the case of airlines consists of a real-time Gantt chart showing the current and planned positions of each aircraft with any problems visually highlighted. Problems that are automatically detected include insufficient vehicle turnaround time, connection problems for crew and passengers, movement slot violations and maintenance violations.

While a Gantt chart provides convenient situational visualisation for modes such as air where there are few constraints on vehicle rerouting, for modes such as rail a train diagram (also known as a time-space diagram) is most often used as it enables visualisation of network constraints.

While this difference makes it seem as if rail is different from other modes, in fact time-space diagrams are useful in all modes when visualising passenger and crew connections. Similarly, Gantt charts are useful in the rail context to visualise stabling and scheduled maintenance requirements. Modern situational awareness platforms are now including many different types of visualisation, allowing users to select the type that best matches their needs.

An important need in situational awareness is to know whether any recovery action is required. A minor delay will normally resolve itself naturally, but a situational awareness tool must be able to tell if a problem will propagate instead, meaning that action is needed to recover the schedule.

Since schedule recovery often requires making a number of coordinated changes, modern airline control systems allow the user to create one or more “what-if?” scenarios that incorporate several unpublished changes. The impact of the scenarios can then be evaluated and the best scenario can then be published.

Specialised technology has been developed to enable the use of real-time scenarios, as these need to be updated automatically as vehicles continue to arrive and depart without losing the desired changes. This is done by only storing the changes while leaving the remainder of the schedule live, and automatically undoing the changes if reality conflicts with what was planned. This technological framework is the key technology that is now being extended to other modes.

When evaluating the current situation or recovery scenarios, it is important to be able to compare forecasts of Key Performance Indicators (KPIs). KPIs such as on-time performance are published regularly for airlines, and are used as a competitive marketing tool. KPIs such as the percentage of trains delayed and cancelled are often used in a rail environment, and failure to meet targets can result in significant fines.

While most airline disruption management systems rely heavily on human decision making, there has been much research on automated recovery optimisation in all modes. Optimisers for airlines typically consider more non-fleet issues (e.g. crew, and detailed passenger itineraries and scheduled maintenance), while those for rail consider more network constraints (e.g. interlocking, temporary track possessions). Solutions from optimisers can then be further improved by the controller before publishing.

It is only a matter of time before the advantages of advanced disruption management spreads to all modes of transportation.