2025 Annual Symposium Program

We analyze several popular control methodologies for network revenue management (network RM) considering their relevance to industry practice and pricing applications. Comparing advantages and disadvantages of both established and novel methods such as network decompositions (resource- or product-based), iterative proration or heuristic dynamic programming methods, we point out ways to improve their performance in practice as well as user adoption. We focus on recent results available in the literature that approach network decomposition from the product point of view and suggest ways to improve them.

Variable Opaque Products encourage shoppers to actively interact with websites in order to create personalised price-product combinations. This produces a rich data set that is well-suited as training data for machine learning models to determine willingness-to-pay and to optimise the price offered to the shopper. An overview of the process used and the models underpinning the WTP calculation is presented.

Lufthansa Industry Solutions, in collaboration with the DLR, Eurowings and Lufthansa Airlines, explore the transformative potential of quantum computing algorithms in enhancing airline operations, focusing on key operational challenges: i) flight cancellation, ii) route planning, iii) crew scheduling, and iv) tail assignment. We will present early results from our research project, where we investigate whether quantum computing can enhance decision-making processes both in scale and speed, leading to more efficient and sustainable aviation practices.

What do the crew think and want from crew planning? A short presentation drawing on my experience presenting to, talking to and interviewing pilots and cabin crew about crew planning. The presentation will explore some of the common conceptions we have as crew planners, are they true or are they not? Some of the topics explored will be: Do crew like the idea of mathematicians and optimizers planning their lives. Are we, crew planners, unrealistic in our expectations of crew? Do trade unions help or hinder? Do crew planners get an unfair reputation amongst crew? Looking forward: is the landscape changing in terms of both crew lifestyle expectations and also can AI change what can be delivered to crew. Can established AI techniques help deliver an improved experience for crew.

Fleet assignment models (FAM) are widely used to maximize an airline’s profitability by optimizing the assignment of aircraft types to an existing flight schedule. In order to increase the feasible solution space and aircraft availability, the model is typically allowed to retime flights. In a novel approach to schedule optimization, we leverage this retime functionality within the fleet assignment model to also optimize for connectivity in our hubs. We use an efficient machine learning model to teach the fleet assignment model about how demand on connecting itineraries will be affected by retimes. While our method is generalizable to O&D-based fleet assignment models, we will demonstrate how it improves the quality and profitability of the schedule when used with a leg-based fleet assignment model.

Funnel-based Learning and Optimization for Offers and Revenue (FLOOR) is a machine learning framework for optimizing offer distribution in airline bookings. It uses granular customer interaction data to predict conversion probability and allocate incentives strategically—offering discounts only to users less likely to book. Features like time spent, navigation patterns, and customer attributes train gradient boosting models to forecast booking completion. FLOOR improves conversion rates while reducing discount costs versus traditional methods. Its adaptive feedback loop captures seasonal and market shifts, enabling continuous refinement. This approach shows how behavioral analytics can drive efficient, revenue-maximizing offer strategies.

We study airline ancillary seat price optimization as a contextual multi-armed bandit, where context (flight-type/itinerary/time-to-departure) informs price selection and the policy “revenue-manages” the full seat inventory over the booking window. We model demand with a Poisson GLM and treat unknown elasticities within a Bayesian belief-MDP. On small problem instances, we compute the optimal policy by value iteration, balancing exploration and revenue exactly. To scale to large problem instances, we approximate the value function with a dual-stream deep learning network that separates arm uncertainty from contextual effects and fuses them into a single value estimate. Across realistic simulations, the approach increases revenue and reduces regret versus LinUCB/LinTS/Tree-UCB benchmarks, while preserving fast decision time. We discuss sensitivity to priors/price grids and integration with inventory and booking-window constraints.

Airlines must balance ticket sales across channels with distinct demand dynamics and price sensitivities. This study examines the revenue implications of direct bookings versus allotment sales to tour operators and travel agencies, using a unique year-long dataset from two major European carriers: a touristic airline and a network airline. These airlines operate under different market structures and employ contrasting sales mixes, enabling a comparative analysis of channel strategies. Focusing on flights to key holiday destinations, we construct flight-level performance indicators and apply descriptive statistics, booking-curve analyses, and fixed-effects regressions to quantify the effect of allotment shares on overall network revenue. The findings provide evidence on how channel composition influences revenue outcomes, offering insights for optimizing seat allocation strategies in airline revenue management.

ANA has launched a pioneering system to manage flight schedule disruptions. Developed through trials since 2019, the system is powered by advanced mathematical optimization and combines ANA’s deep expertise in maintaining world-class operational quality with cutting-edge technology from Hitachi. When a disruption occurs, the system analyzes vast datasets—including flight schedules, maintenance plans, and crew schedules—to generate multiple optimal solutions in a short amount of time, a process that previously took hours. Operations staff make the final decision from the system-generated options. This dramatically accelerates decision-making, minimizing the impact of delays and cancellations on customers. It also helps standardize processes and address human resource shortages by automating complex tasks and leveling skill quality. This presentation introduces the case of ANA and its initiatives to enhance operational resilience.

Decision-making in airline operations is increasingly tool-driven, while strategic planning sees limited adoption. Scalability is the issue, limiting models to optimize only part of the problem, e.g. hubs in isolation and only partially capture supply-demand interactions. We introduce a decision-support model that fully accounts for both elements and explores multi-hub potential in hub-and-spoke networks. A MINLP jointly optimizes (i) destinations, (ii) frequencies, and (iii) aircraft deployment. We treat frequencies endogenously and account for competition. A utility-based model links itinerary attributes to demand and market-share; a calibrated cost function covers operating and fixed costs for consistent profit evaluation. Using a real-world case study with hubs in Amsterdam and Paris, we demonstrate that joint multi-hub optimization outperforms a single-hub benchmark in profitability and resilience under hub-capacity limits, demand shifts and fleet changes.

Airline ground staff scheduling is challenging given operational uncertainties such as correlated delays, fluctuating task times, and staff shortages. Traditional stochastic scheduling methods, such as stochastic programming, struggle to realistically capture task delays and dependencies. This study uses simulation-optimization (SimOpt) to better reflect real-life complexities. A discrete-event simulation mimics the arrivals and departures of aircraft, the baggage handling tasks, and the impact of delays, task duration uncertainties, and staff shortages. We apply and compare optimization methods including simulated annealing, genetic algorithms, and learning-based approaches, to develop shift-break schedules that handle daily operational uncertainties effectively. Results show that SimOpt provides more reliable decision support than traditional methods, leading to greater resilience and substantial cost savings in airline ground operations.

Traffic forecast at any airport or airline is key to operations planning as well as financial planning. At Groupe ADP, our traffic prediction team predicts traffic at a rather coarse granularity, typically looking at the number of flights operated from/to a world sub region by a particular airline, over a period of time, that can be days, weeks, or months. The specific problem we have tackled is the estimation of the number of scheduled flights which, eventually turn not to be operated, whether they are removed from later schedule versions or whether they get cancelled. Conversely, flights which were not planned in early versions of the schedule appear in later versions. These aspects challenge our long term planning analysis. We will go through alternative problem approaches and prediction models, their results, remaining challenges, and lessons learned.

“Can you explain why you recommend this flight?” This presentation traces the evolution of our research on flight itinerary choice models from simple linear approaches (past) to advanced AI methods (present). As these models grow more complex, enhancing transparency and explainability becomes essential (future), empowering both airlines and travellers to trust and understand flight recommendations among other applications. Finally, we discuss a new methodology for integrating LLM capabilities with advanced choice models to generate explanations that are understandable to human users, answering the traveller’s question: 'Can you explain why you recommend this flight to me?'"

We designed an optimization model for airport gate assignment that minimizes conflicts and improves operational efficiency. In collaboration with Quantagonia, we deployed the model on their platform, where it connected to an agentic large language model (LLM), enabling users to interact through natural language. Operational changes such as a gate becoming unavailable can be expressed directly in text, which the LLM interprets as additional constraints before re-solving the model to produce a new optimal assignment. By combining mathematical optimization with AI-driven interaction, this approach makes decision support more adaptive, transparent, and accessible for dynamic airport operations.

Cold-start conditions - where little or no historical data exist - are pervasive across airline decision making, from demand forecasting to pricing and inventory control. This presentation outlines a framework for mitigating these challenges through a combination of techniques: iterative feature engineering with orthogonal, geo-powered attributes; clustering and similarity analysis to identify market twins; and the integration of both natural and pretrained embeddings to bridge information gaps. Together these methods act as a “data prosthetic,” enabling models to deliver robust predictions even in data-sparse environments. The approach enhances accuracy, interpretability, and generalization across diverse airline applications, providing a scalable blueprint for handling data sparsity.

Airlines increasingly use A/B testing to evaluate new pricing models and tactical changes. These experiments generate booking data that reflect complex demand patterns. A common mistake is to treat data points as independent, when seasonality, booking curves, and weekday effects actually create correlations. Ignoring this inflates significance and leads to misleading business decisions. A/A tests show the issue: false positives rise, so effects appear even when none exist. Clustering helps but widens confidence intervals and lengthens runtimes. More efficient approaches explicitly model autocorrelation. Methods such as generalized least squares (GLS) and switchback designs reduce noise, enabling more trustworthy conclusions with fewer samples and shorter experiment durations. This talk highlights why error structure matters and how accounting for autocorrelation improves both accuracy and speed in airline A/B tests.

Airlines struggle balancing planned CASK vs. operational robustness in short-term scheduling. We present a genetic algorithm (GA) based optimizer that improves schedule quality by including trade-offs between turn times, ops spares, and crew productivity, allowing for tuning turn buffers, ops spares, and crew productivity to balance CASK impact with day-of-ops recoverability. Using efficient-frontier views and simple stress tests, we quantify OTP–resilience trade-offs without claiming a single best answer. We’ll show preliminary experiments in simultaneous network planning optimisation across ports/days and outline next steps to incorporate crew feasibility and flows. The goal is a clear method for measuring revenue and cost trade-offs and informing schedule choices.

For an airline operating in the cargo spot market, real-time pricing of new request-for-quote (RFQ) orders is essential. Requests must be processed rapidly, and additional complexity arises from their multidimensional characteristics, such as type, weight, and volume. Furthermore, delivery dates are often flexible for both new and accepted orders, as shipments may be delayed at a potential cost. In this paper, we introduce a Dynamic Programming algorithm that quickly estimates the opportunity cost of cargo and reschedules accepted orders to minimize total cost. The algorithm offers greater computational efficiency than partial network re-optimization, while its accuracy and explicit rescheduling decisions compare favourably to bid-price controls derived from relaxed problem formulations. We also provide a numerical study on simulated datasets and discuss directions for future development.

SWISS and the Lufthansa Group are developing advanced software to support our operations control centers during disruption events. Disruptions may stem from various causes, but their impact ultimately falls on our passengers, making it essential that recovery decisions prioritize restoring their journeys effectively. In this presentation, we introduce a leg-based passenger recovery approach, where valid and tailored alternative itineraries are efficiently generated from leg data and assigned to affected passengers. We outline our methodology for assembling feasible legs and assessing seat availability, leveraging passenger-specific information to build customized alternatives. We then describe our cost function to price these alternatives and the optimization process used to identify the global optimal solution. Finally, we illustrate how this product is being applied in practice and provide an outlook on upcoming developments.

Accurate forecasting of passenger and baggage loads is critical for airline operations, driving weight and balance, baggage handling, staffing, and turnaround reliability. Traditional methods based on booking data or fixed ratios often miss real-time dynamics such as no-shows, late check-ins, and seasonal fluctuations. A machine learning framework was developed to generate multi-horizon forecasts of passenger counts and baggage weights at 310, 190, 100, and 70 minutes before departure. Tests on thousands of hub flights showed 15–20% lower error than booking-based baselines, with the largest gains during peak departure waves. The system is integrated into the Integrated Operations Control Center (IOCC) and used in real time for resource allocation and pre-departure fuel planning. Results confirm that advanced forecasting enhances planning reliability, reduces costs, and supports more efficient airline operations.

Railway time-tabling and scheduling is one of the toughest Operations Research problems to solve but of tremendous utility for firms. In a setting where competition is both dense and intense such as the high-speed corridors of Spain, the importance of finidng a schedule that satisfies all constraints and also is revenue-maximizing is challenging. We describe the implementation of a revenue-optimal scheduling formulation and solution that scales well and takes into account maintenace considerations and demand profiles. We describe computational experiments as well as practical implementation details.

Generative AI integrated with airline Operations Research can transform loyalty management by addressing long-standing inefficiencies. Traditional redemption grids face four challenges: static, one-size-fits-all pricing; fraud and bulk redemptions detected too late; inefficient seat allocation misaligned with demand; and P&L blind spots on the impact of points from different sources. Our proposed GenAI-powered grid solves these gaps. Pricing flexes dynamically by demand, route load, and tier; fraud is flagged in real time; inventory is allocated to maximize revenue; and P&L visibility improves by tracking redemptions by source. The benefits are summarized with PRIN: P&L Protection, Redemption Usage Optimization, Intelligent Personalization, and Network Fraud Prevention. This approach makes loyalty programs smarter, fairer, and more profitable—aligning redemptions with customer value while safeguarding airline revenues.

KLM Royal Dutch Airlines tackled critical Embraer 195-E2 engine challenges, where reduced maintenance intervals threatened fleet availability, increasing parked aircraft and in-service activations. A rapid, data-driven "E2 Availability Optimiser" Proof of Concept (PoC) was developed in one week. This model maps aircraft and engine states into an engine state network, minimizing unavailability and activation costs. Initially, the model had long runtimes (>4 hours) and low optimality (>20%). Performance was drastically improved using "virtual engines" and network simplification, reducing runtime to under 20 minutes with an optimality gap below 20%. The user-accessible tool optimizes engine assignments, resulting in 2-6 fewer parked aircraft and over 25% fewer engine changes. While overall aircraft availability slightly decreased, network stability improved. Future plans include tool maintenance, continuous decision support, and new innovation initiatives.

This study proposes a two-stage stochastic optimization model to minimize total operational costs and improve OTP in the presence of uncertainties. First-stage decisions optimize pre-hub departure adjustments: aircraft swaps, departure delays, and cruise speeds controlled via cost index (CI) adjustments. Second-stage decisions, applied in the subsequent hub cycle, re-optimize CI and swaps to mitigate realized disruptions.

Achieving a shift from air to rail is key to decarbonising transport, requiring models that capture multimodal behaviour and network performance under different schedules, policies and disruptions. This talk presents the multimodal performance framework and the strategic and tactical evaluators from SESAR’s MultiModX project. The strategic evaluator generates itineraries from flight schedules, rail timetables and policies, computes network-wide indicators, and evaluates passenger impacts of replanned networks during disruptions. The tactical evaluator assesses the realised network with passenger-centric metrics. Applications to Spain include long-term policies (integrated ticketing, CO2 taxation, flight bans), short-term disruptions (industrial action, cancellations) and mechanisms to support multimodality (airport fast track). Results show rail can absorb displaced demand, strengthening resilience and demonstrating the complementary role of air and rail.

A continuous challenge users of airline Planning Software face is proving ROI for optimizers in use. During initial implementation, the optimizer's results need to be benchmarked against manually created scenarios. But what happens after that? Well, ideally, the benchmark should continue at regular intervals while the optimizer is in production. However, after the optimizer is adopted, manually constructed BM scenarios often do not exist. We would like to present a framework to continuously evaluate ROI for WePlan’s planning optimizer consistently and robustly. An artificial manpower planning agent approximating the behavior of a human planner working manually allows us to benchmark the performance of the optimizer over time. We will present the results of our work and discuss how this agent can now potentially be used to make smarter decisions in MPP.

Operating lease is a frequently used and increasingly popular method of acquiring aircraft for airlines. However, this method typically imposes specific restrictions on the lessee's aircraft utilization decisions, such as a target hour-to-cycle ratio for the aircraft. Failing to meet this target over a certain period of time may result in supplementary rental payments. This study considers hour-to-cycle ratio constraints in the tail assignment problem. The aim is to develop exact solution approaches for the problem. We propose a branch-and- price algorithm. To enhance its performance, we introduce a beam search-based algorithm that generates feasible solutions for the problem and also devise a dancing links-based heuristic approach that finds upper bounds. The proposed algorithms successfully solved instances with up to 60 aircraft and 446 flights to optimality, while CPLEX, which was solving a connection network-based mathematical formulation, was unable to find a feasible solution within an hour.
This presentation is a finalist for the Anna Valicek Award Competition.

Effective air traffic management within the Terminal Manoeuvring Area (TMA) is imperative for mitigating delays, minimizing fuel consumption, and reducing emissions in the aviation sector. While existing research has predominantly focused on optimizing runway sequencing, the Terminal Airspace Scheduling Problem (TASP) has been relatively understudied. This work addresses this gap by proposing an innovative matheuristic algorithm (TMAOpt) that concurrently optimizes both runway aircraft sequencing and decisions within the TMA, including runway selection, speed control, utilization of holding patterns, vectoring, and point merges. The proposed approach combines a Linear Programming (LP) model with metaheuristic algorithms, providing a unique solution approach that balances rapid generation of feasible solutions (within 1 s of computation) and convergence (within 5 min of computation). Validation of our approach involved extensive evaluations using real- world data from the congested terminal airspace of Changi Airport in Singapore. Comparative analyses with existing methods, including commercial microsimulation models like AirTOP, showcase the superior performance of our algorithm, yielding sequences that reduce delays by up to 27%. A sensitivity analysis, exploring varying degrees of permitted TMA interventions, underscores the benefits of their balanced utilization.
This presentation is a finalist for the Anna Valicek Award Competition.