Disclaimer: the agenda serves for orientation purpose and is still subject of change
All times are in Santiago local time.
Start time | Details |
---|---|
09:00 |
Welcome |
09:30 |
Keynote |
10:30 |
Coffee break |
11:00 |
Technical presentation: Leveraging Cloud Deploymentsby Remy Gauthier More and more airline organizations are deploying their airline operations and crew management software in a dematerialized (cloud) environment, or are considering to do so. Cloud deployments offer a more flexible infrastructure than traditional on-premise, or offsite physical servers that is not limited by the number of physical CPUs and memory. We are developing a product infrastructure that leverages the flexible nature of the cloud which allows for on-demand vertical and horizontal scaling. We will introduce this software landscape and show how we are pushing the concept of service-based architectures to offer a highly flexible deployment model coupled with compute-on-demand capabilities. |
11:45 |
Presentation TBD |
12:15 |
Sponsor presentation: NavBlue |
12:30 | Lunch |
13:30 |
Presentation TDB |
14:00 |
Presentation TBD
|
14:30 |
Technical presentation: FTL Effectiveness. How much lower risk? At what cost?by Tomas Klemets Is it at all possible to measure how effective regulatory rules are? Such as the rules governing work and rest time for pilots coming from EASA, FAA and the CAAC? Jeppesen, in collaboration with SWISS, another prominent international airline operator, a large space agency, and the British Airline Pilots Association (BALPA) has set out to do so. In this presentation we will share our findings regarding the improvements made to the EASA rules when stepping over from Subpart Q to the current ones. We have constructed a vast data pool of realistic work patterns built from real flights schedules, using the typical production tools many operators use, and planned the production only governed by regulation. The work patterns that emerged have then been assessed in terms of both fatigue risk and crew efficiency to determine FTL Effectiveness - and the results are not entirely what the industry may have hoped for or expected. This is, to date, the largest and most detailed attempt at quantifying effectiveness of regulatory rules using the best representation of science available to us. The structured approach, and the vast amount of data now available, opens for several novel ways of improving regulatory rules going forward. Tomas Klemets is a seasoned expert in airline crew management processes, with over 20 years of experience in the field. He currently serves as the Head of Scheduling Safety at Jeppesen, where he has consulted with over 100 airline operators worldwide. Tomas is responsible for the development of the Jeppesen fatigue risk management portfolio, which includes the pioneering R&D behind the Boeing Alertness Model. In his current role, Tomas leads a cross-functional DevOps team that is dedicated to bridging the gap between science, regulation, flight safety, and the integration of capabilities into the construction process for crew rosters. His focus is on reducing risk and improving crew wellbeing. Tomas is a frequent speaker at international conferences and has an impressive number of publications and news postings to his name. He is widely recognised for his expertise in the field and is a sought-after speaking partner for airlines seeking to improve their crew management processes. |
15:15 |
Coffee break |
15:45 |
Sponsor presentation: API |
16:15 | Technical presentation TBD |
16:45 | Presentation TBD |
17:15 | Modeling Crew Itineraries and Delays in the National Air Transportation Systemby Vikrant S. Vaze We propose, optimize, and validate a methodological framework for estimating the extent of the crew-propagated delays and disruptions (CPDDs). We identify the factors that influence the extent of the CPDDs and incorporate them into a robust crew-scheduling model. We develop a fast heuristic approach for solving the inverse of this robust crew-scheduling problem to generate crew schedules that are similar to real-world crew-scheduling samples. We develop a sequence of exact and heuristic techniques to quickly solve the forward problem within a small optimality gap for network sizes that are among the largest in robust crew-scheduling literature. Computational results using four large real-world airline networks demonstrate that the crew schedules produced by our approach generate propagation patterns similar to those observed in the real world. Extensive out-of-sample validation tests indicate that the parameters calibrated for one network perform reasonably well for other networks. We provide new insights into the perceived trade-off between planned costs and delay costs as reflected by actual airline crew schedules. Finally, we present a general approach to estimate the CPDDs for any given network using our methodological framework under a variety of data availability scenarios. |
Start time | Details |
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09:00 | Sponsor presentation: IBS Software |
09:30 | Technical presentationby Tristan Thiebaut and Stephen Bail |
10:00 | Sponsor presentation: Lufthansa Systems |
10:15 | Coffee break |
10:45 | Technical presentation: study results in crew management training standards |
11:15 | PanelModerator: Daniel Stecher of IBS Software |
12:30 | Lunch |
13:30 | Networking event |
Start time | Details |
---|---|
09:00 |
Sponsor presentation: Boeing |
09:30 |
Technical presentation: Listening to Crew Feedback: The Artificial Intelligence Approachby Alexander Motzek Daily thousands of feedback comments are written by cabin and gate crew members. Those comments contain extremely valuable information, but often remain unheard. If analyzed, they reveal pain-stacking problems in handling, operations, catering, technical flaws, or general unhappiness which otherwise goes unnoticed. Unfortunately, the sheer number of comments to be read prevents any quick and holistic analyses. Often, only ad-hoc, single-case occurrences are followed without any clear situational awareness. If feedback is not responded to, initial reporters become frustrated, as, e.g., the same reported seat malfunction may still not be fixed when boarding the same aircraft, a week later; no feedback loop appears to be present. Fortunately, huge advancements in artificial intelligence in recent years have made it possible to turn thousands of written feedbacks into easily digestible insights. We have built an automated classification model that quickly highlights insights about emerging problem clusters and presents them for visual drill-down into clusters. On top of that, we solve the feedback-loop problem by transforming written action statements from mechanic inspections into a direct visualization of a cabin state for all crew members. In this talk, we present our insights, discuss approaches, and show future applications of artificial intelligence in cabin crew management. |
10:00 |
Technical presentation: Practical Applications of Machine Learning on Roster Data: Predicting Absences and Measuring SatisfactionBy Sara Kamel, Karim Maarouf Crew roster data in its raw form is quite challenging to make use of. However, transforming this data into an easily accessible format and combining that with the application of machine learning techniques unlocks its huge potential. In this talk, we explore the application of machine learning algorithms on crew roster data. We will share some of the challenges and insights from our work on the OPSD project at Swiss. We will demonstrate how integrating crew roster data into a cloud analytical platform provides opportunities enabled by machine learning to improve operations. We focus on two use cases, namely predicting crew absences and measuring crew satisfaction. However, that is certainly not the end of the story. Once data is integrated and organized, this enables rapid ideation and prototyping of other use cases. The benefits are limited only by the creativity in applying some of these advanced techniques. We also discuss some considerations and lessons learned from this journey. |
10:30 |
Sponsor presentation: TA Connections |
10:45 |
Coffee break |
11:15 | Sponsor presentation: VeeOne |
11:30 |
Technical presentation: Innovation in crew rostering: standby’s, fairshare distribution and pre-assignmentsby Steven Rushworth We have developed a new crew rostering system that is now in production at several airlines. The pairing and rostering, typically separate steps in crew scheduling, are now calculated simultaneously in a single run. Our system generates personalized pairings for each crew member that take into account their preferences and preassigned activities. This leads to roster with a lower cost and a higher blockhour/duty hour ratio. Based on these foundations we were able to continue to innovate and push the boundaries even further. Some of the recent innovations are:
During this talk we will discuss the details of this approach and discuss the benefits it brings for the operations and crew. |
12:00 |
Technical presentationby Franziska Burmester |
12:30 |
Lunch |
13:30 |
Technical presentation: Integrated planning |
14:00 |
A Robust Pairing Model for Airline Crew SchedulingBy Vikrant S.Vaze Delays and disruptions in airline operations annually result in billions of dollars of additional costs to airlines, passengers, and the economy. Airlines strive to mitigate these costs by creating schedules that are less likely to get disrupted or schedules that are easier to repair when there are disruptions. In this paper, we present a robust optimization model for the crew pairing problem, which generates crew schedules that are less likely to get disrupted. Our model allows adding robustness without requiring detailed knowledge of the underlying delay distributions. Moreover, our model allows us to capture in detail the delay propagation through crew connections and the complex cost structure of the pay-and-credit crew salary scheme, thus enabling us to find a good trade-off between the deterministic component of the planned costs on the one hand and the expected delay and disruption costs on the other hand. Our robust crew pairing model is based on a deterministic crew pairing model formulated as a mixed-integer linear program. The robust version that we propose retains the linearity of the constraints and objective function and thus can be handled by commercial solvers, which facilitates its implementation in practice. We propose and implement a new solution algorithm for solving our model to optimality. Several optimal solutions with varying robustness levels are compared for the network of a moderate-size airline in the United States. We test the model’s solutions in a simulation environment using real-world delay data. Our simulation results show that the robust crew pairing solutions lead to lower delays and fewer instances of operational infeasibilities, thus requiring fewer recovery actions to address them. We find that, with the inclusion of robustness, it is possible to generate crew pairing solutions that significantly reduce the delay and disruption costs with only a small increase in planned costs. |
14:30 |
Presentation TBD |
15:00 |
Coffee break |
15:30 |
Presentation TBD |
16:00 |
Awards for "best presentation" and "best innovation"Conference recap and farewall |