Nicholas Etz, United Airlines

Airport gate assignment is an important factor in airline operations. This presentation introduces a model for determining whether a schedule can be gated at key stations. It assesses core gating considerations, including towing, as well as bespoke constraints like remote parking restrictions. When a full gating solution is infeasible, results are then used as a basis for retiming recommendations. By providing a quick spot check for gate assignment feasibility, the model helps integrate gating information throughout the network planning pipeline.

Laurie Garrow, Georgia Institute of Technology

In January of 2023, a new center called ATL@GT was established at Georgia Tech under the direction of Laurie Garrow. The mission of ATL@GT is to lead research and education activities related to airline revenue management. One of the key activities of the center is the development of a competitive revenue management simulator that can be used to test different business strategies. The competitive RM simulator, called PassengerSim, is being designed with a highly flexible architecture that will enable students, researchers, and industry partners to write customized code to test out their own ideas while interfacing with the core simulator. In this presentation we will give an overview of our progress developing PassengerSim. Given our interest in using the simulator to explore both revenue management and scheduling planning applications, we present our vision for incorporating and calibrating passenger schedule preferences into the simulator and look forward to receiving audience feedback!

Yacine Benziani, Nazar Emirov, and Ketan Date, Amadeus

Airline scheduling for large networks poses significant challenges due to numerous variables and constraints. In addition to optimizing profitability and operational feasibility, airlines prioritize crew productivity as a secondary objective. Effective crew scheduling is pivotal for operational efficiency and crew satisfaction. Our proposed approach incorporates crew-friendly considerations into scheduling while managing other objectives with minimal complexity increase, thereby delivering promising crew outcomes without compromising operational goals. Factors such as duty time regulations, rest intervals, and crew pairing preferences are integrated to enhance crew well-being and performance. Although the exact formulation, while available, is not scalable for large networks, we employ approximations to model crew-friendly factors. We illustrate this approach using scheduling data from a leading European carrier.

In airline network planning, aircraft routing is an important problem. This task is essential not only during the early stages of schedule planning but also as the day of departure approaches. Ensuring that each aircraft maintains a feasible flying schedule is crucial, particularly to facilitate regular maintenance opportunities. Our project concentrates on refining the initial schedules to better accommodate maintenance needs. To begin with, we have introduced a novel scoring system designed to gauge the compatibility of a schedule with established maintenance regulations. Following this, we have developed a simple swap-based heuristic that leverages this score to enhance the lines of flying for individual aircraft. The implementation of this routing algorithm has significantly improved maintenance scheduling, with certain aircraft fleets witnessing up to a 70% enhancement in maintenance performance.

Emmanuel Carrier (speaker), Delta Air Lines & Peter Belobaba

As we develop a replacement for the Passenger Origin-Destination Simulator (PODS), we study the impact of passenger preferences for schedule on the performance of advanced revenue management (RM) methods. At the core of PODS sits the Boeing Decision Window model that is used to represent passenger preferences for schedule including preferred departure and arrival times and the attractiveness of shorter itineraries. We show that disabling these preferences shifts revenues from airlines with a superior schedule (more nonstops, shorter connections) to carriers that offer less convenient itineraries. Without modeling these preferences, simulation results substantially under-estimate the benefits of advanced RM algorithms such as network O-D control. A proper representation of passenger preferences for schedule will then be instrumental to provide a realistic environment to evaluate the performance of RM algorithms in the new simulator.

Hengda Jin, Texas A&M University

This study examines airline yield management analysts’ acquisition of non-airline companies’ regulatory filings and the association of that acquisition with airlines’ pricing decisions. I find that yield management analysts’ EDGAR searches are positively associated with yields (i.e., the product of average airfare and passenger load factors). This finding suggests that information acquisition helps analysts forecast business travel demand and thereby maximize revenue through effective intervention in yield management systems. Decomposing information acquisition into different types of filings, I find that the positive association is driven primarily by the acquisition of accounting reports and varies with the characteristics and content of accounting reports. Specifically, the association is weaker when the accounting reports acquired are less readable, and it is stronger when the accounting reports acquired provide more quantitative forward-looking statements about business travel demand. Overall, the evidence enhances our understanding of how the customer base’s regulatory filings facilitate suppliers’ pricing decisions.

Christian Kumpf, Judith Semar, Lufthansa Group and Bernhard von Mutius, Kearney

Traditionally, airlines optimize their networks and schedules commercially considering operational constraints that are defined in separate processes. Current airline operations with challenging OTP and customer satisfaction shows that this is no longer suitable in today’s world. As a response, Lufthansa Group has decided to truly innovate by evaluating the consequences of missing operational robustness on eye level with commercial profit in schedules as early as 1 year out. This unlocks both additional OTP and ASK. Higher OTP and increased robustness by planning necessary buffers already in the early stages of network planning and scheduling and. Higher ASK with the same fleet by applying buffers where they are needed, hence, freeing up aircraft capacity to fly more. Achieving this is complex as it requires evaluating millions of different schedule designs and their effects on profitability and robustness. Given this task, manual planning reaches its limits as it can take airlines alone weeks to build only a few if not only one schedule scenario per hand. Hence, next level solvers are required – such as the NetLine/HubDesigner of Lufthansa Systems that can design new schedules from scratch and integrates network planning and scheduling tasks. Lufthansa Group and Lufthansa Systems jointly enhance the HubDesigner to consider delay costs during the optimization to reach the overall best balance between commercial profitability and operational targets. Supported by Kearney, Lufthansa Group and Lufthansa Systems will share some insights and learnings from introducing such a tool.

Rohan Nanda, Airbus

The impact of airport congestion on aircraft upgauging is frequently contested, as there is considerable debate as to what extent this trend is driven by economic incentives versus airport slot constraints. As more airports are forecast to reach capacity limits in the future, there is a need to better understand this phenomenon. By analyzing flight data, airport capacity, and airline scheduling practices, we aim to dissociate between the two drivers in order to understand their relative contributions. In doing so, we gain insights into how this effect could be incorporated into forecast models, in order to better anticipate aircraft size requirements.

Luis Correa, Copa Airlines

In the complex world of airline scheduling, achieving optimal flight schedules is crucial yet challenging and often time-consuming. This presentation introduces an optimization model developed to tackle this challenge for a hub-and-spoke airline. The model attempts to optimize flight itineraries, considering key operational constraints such as block times, minimum ground times, maintenance requirements, slots, airport curfews, minimum connecting time at the hub, and flight separation criteria to avoid hub congestion. It focuses on maximizing network revenue by enhancing flight connectivity within banks, considering that not all arrivals can connect with all departures in each bank. Attendees will learn about the model's formulation, constraint integration, and the practical implications of its implementation in network planning and scheduling.

Pascale Batchoun, Air Canada

The first step toward establishing a resilient flight schedule is to accurately predict its On-Time-Performance (OTP) and to evaluate the impact that both scheduling and operational changes could have on OTP. Air Canada has developed and deployed to production a machine learning-driven methodology that predicts system-level OTP key performance indicators (KPIs) while providing low level estimates for the block and turn durations at the flight level. Augmented with a simulation engine, the “OTP Schedule Optimizer” evaluates various scenarios to simulate disruptive and cascading delays, and their impact on our aircraft performance, passenger connections and crew flow. The system uses an optimization engine that leverages various outputs from the machine learning and simulation models, to recommend schedule changes to improve its OTP while minimizing the impact on passengers’ misconnections. We will shed the light on how we designed the system and share insights on performance KPIs, including simulation of operational changes, and what are some of the pain points highlighted in the schedule, and what actions to take in the Planning, Scheduling and operational windows.

The opportunity to comprehensively restructure a network and validate operational settings for enhancing operational performance without compromising profitability is rare, especially in an environment characterized by capacity-constrained and slot-coordinated airports, aging aircraft, and suboptimal infrastructure. Moreover, it presents a unique chance to challenge conventional norms regarding block and turn settings within the Australian domestic market context. Therefore, it is crucial to employ a data driven approach that evaluates the combined impact of numerous internal and external factors, marking a step up from traditional discrete and siloed operational assessments. We used a simulation and optimization approach, developed in partnership with Amadeus, that facilitated data-driven discussions at a more advanced level, enabling an iterative process to evaluate and narrow down from numerous schedule structure combinations to three scenarios evaluated against a balanced scorecard. This capability empowered informed decision-making with clear KPI targets aimed at enhancing operational performance while preserving profitability

Mikhail Andriyanov, Andriyanov & Partners Mathematicians and Economists PartG

Choosing the right fleet is challenged by the curse of dimensionality, where each composition of aircraft types ideally requires its own optimized network. We solve this network and fleet planning task as a simultaneous optimization problem. Traffic forecast, demand stimulation, market share simulation, network traffic distribution and evolutionary optimization are integrated into an end-to-end data pipeline. Each run of the algorithm automatically outputs the optimal number of aircraft units, destinations, and frequencies for each fleet mix. Optimization can maximize any objective, including pure profit and profitable growth. Implementation as a Python pipeline allows accounting for any practical constraints and considerations, such as strategically operated routes, multi-stop flights and cabin configurations. We showcase the approach using open data from the U.S. Department of Transportation, where the network and fleet of a hypothetical airline based at San Francisco Airport are optimized under various objectives and conditions.

Shahram Shahinpour (speaker) and Sureshan Karichery, Sabre Corporation

Reliability of airline schedule ensures smooth execution during operations, improves passenger experience and has direct effect on cost reduction and boosting financial bottomline. Therefore, it is imperative to measure on-time performance (OTP) metrics of the schedule and optimize them during network planning and schedule design tasks. In this talk, we first present a model to evaluate future schedule OTP at the network and flight level. Next, using evaluated OTP metrics and airline-specific business rules an optimization model is formulated and solved to produce the best solution that optimizes OTP and profitability objectives. The proposed optimization model is comprehensive with diverse set of rules and configurable target OTP levels. Benchmarks prove capability of proposed model in achieving planned OTP and financial targets.

Younes El Jarari Charqui, Airbus

Traditionally, long-term aircraft demand forecasts predominantly hinge on macroeconomic indicators, often overlooking the potential that supply has in stimulating or constraining demand. The study challenges the conventional demand-driven paradigm by quantifying the extent to which, in the past, supply has played a key role in the realised passenger air traffic. A framework for introducing supply-side features, including aircraft production rates and airline capacity/network plans, is proposed to improve long-term forecasting models.

Carlos Jose Nohra Khouri and Reza Baharnemati, Amadeus

In the airline industry, network planners grapple with critical questions related to market selection, flight frequency, and aircraft allocation as part of capacity planning. As airlines expand and face an ever-growing number of potential markets, optimizing network profitability becomes increasingly challenging. Mathematical optimization models offer a solution by recommending optimal nonstop routes and frequencies while adhering to constraints like aircraft availability and market bounds.

Dan Jeffrey, Senior Technical Account Manager – Americas, Gurobi Optimization

Recent releases of Gurobi Optimizer have added many new capabilities that turn your unsolvable MIP models into viable applications. MIP is very different than 5 years ago. New heuristics make highly combinatorial problems solvable as pure MIP’s where they were not solving before. Multiple objectives help math programmers customize the model to better fit the operations they need to serve. Gurobi performance improvements change many assumptions about viability of MIP for your large problems. We will also cover some little-known Gurobi capabilities that will be new to most users. 

Alex Ottes, Vice President Sales Americas & Judith Semar, Chief Product Owner NetLine Optimization

How can network planning and scheduling accelerate time to market while ensuring profitable and stable operations?

Data-driven decisions, optimization, and AI technology are crucial in shaping the future of airline planning processes. NetLine leverages the latest technological advancements to empower airlines to make the best decisions ahead of time by:

• Designing schedules with enhanced quality and faster time to market
• Generating robust lines of flying that synchronize aircraft and crew
• Mitigating delay risks in planning and scheduling
• Improving the resource efficiency of aircraft, crew, and slots
• Aligning planning and operations departments towards the common goal of profitable and stable operations