1,720,998 research outputs found
Day-ahead aircraft routing with data-driven primary delay predictions
No full textFlight delays are major sources of disruptions in airline operations. To mitigate them, day-ahead aircraft routing aims to create flight sequences that can absorb delays and minimize their propagation. However, flight delays are unknown ahead of operations; moreover, predicting delays is complicated by the fact that historical data encompass both primary delays (arising from exogenous sources) and propagated delays (arising from cascading effects in an airline’s network). This paper thus develops predictive and prescriptive analytics models to forecast primary delays and to optimize day-ahead aircraft routing toward delay mitigation. We develop a quantile regression model to reconstruct primary delays from historical data, and an ensemble machine learning model to predict them based on flight-level features, environmental features, and traffic features—estimated via a queuing model of airport operations. Then, we formulate deterministic and stochastic optimization models to support day-ahead aircraft routing. Using real-world data from Vueling Airlines, we evaluate the models out of sample against real-world counterfactuals. Results show that our predictive model achieves a mean absolute error of 7–8 minutes and that our prescriptive models can reduce delay costs by 3–5%. This paper shows the benefits of predictive and prescriptive analytics to enhance the robustness of airline operations by (i) creating shorter aircraft rotations, and (ii) strategically allocating schedule slack to avoid the propagation of long delays in later phases of the day. This research led to the deployment of the models in collaboration with the Vueling data science unit
Optimization of subsidized air transport networks using electric aircraft
No full textElectric aircraft represent a major technological breakthrough with a promise of revolutionizing aviation systems towards more sustainable and accessible services. Prominent electric aircraft prototypes feature limited seating capacity and short ranges, which make them well-suited for efficiently operating thin routes-particularly, regional routes serving remote regions-in the near future. To capitalize on this opportunity, this paper proposes an original optimization framework in support of the strategic design of subsidized air transport networks using electric aircraft. We first develop a quadratic optimization model to disaggregate air transport demand data based on demand generation and allocation properties, resulting in refined demand estimates at the territorial scale (instead of at the airport level). We then develop an integrated bi-objective optimization model for network and fleet planning, utilizing a novel time-space- energy formulation. This model aims to balance the two primary objectives of planning subsidized air transport networks: maximizing passenger surplus and minimizing system-wide subsidization costs, while incorporating detailed modeling of demand accommodation and electric aircraft operations. To address large-scale problems, we develop a solution approach involving reformulation and a tailored binary relaxation scheme. By considering a real-world case study of Sweden, we demonstrate the benefits of the proposed approach and highlight its major insights-in terms of route network, fleet, number of chargers, flight schedules, fleet assignment and environmental emissions-with a comparison of conventional, electric, and mixed fleets. Our results demonstrate that a complete substitution of first-generation electric aircraft may diminish consumer surplus, while a combined use of electric and conventional aircraft yields superior solutions, resulting in higher passenger surplus and reduced emissions for the same subsidy spending
Modellazione delle interazioni domanda-offerta nella pianificazione delle reti di trasporto aereo
No full textDelay predictive analytics for airport capacity management
Get PDFLocal delay predictions are crucial for optimizing airport capacity management, enhancing overall resilience, efficiency, and effectiveness of airport operations. This paper delves into the development and comparison of state-of-the-art predictive analytics techniques—spanning rule-based simulations, queuing models, and data-driven approaches—and demonstrates how they can empower informed decision-making toward mitigating the impact of potential delays across the whole spectrum of capacity management initiatives—from long-term strategic capacity planning to near real-time air traffic flow management. Using real-world data for four major airports in Southeast Asia, we comprehensively assess the performance of different methods and highlight the improved predictive capabilities achievable through data-driven methods and the incorporation of sophisticated features. Results show that (i) embedding queuing model features into machine learning models effectively captures congestion dynamics and nonlinear patterns, resulting in an improvement in predictive accuracy; (ii) incorporating advanced day-of features – lightning strikes, wind conditions, and propagated delays from prior hours – further enhances prediction accuracy, yielding
gains ranging from 15% to 30%, contingent on the specific airport; (iii) in cases where limited information is available (years to months in advance of operations), conventional simulation and queuing models emerge as robust alternatives. Ultimately, we conceptualize and validate a delay prediction framework for airport capacity management, characterizing the different planning phases based on their specific delay prediction requirements and identifying appropriate methods accordingly. This framework offers practical guidance to airport authorities, enabling them to effectively leverage delay predictions into their airport capacity management practices
Passenger-Centric Slot Allocation at Schedule-Coordinated Airports
No full textSchedule coordination is the primary form of demand management used at busy airports. At its core, slot allocation involves a highly complex combinatorial problem. In response, optimization models have been developed to minimize the displacement of flight schedules from airline requests, subject to physical and administrative constraints. Existing approaches, however, may not result in the best itineraries for passengers. This paper proposes an original passenger-centric approach to airport slot allocation to maximize available itineraries and minimize connecting times. Because of the uncertainty regarding passenger demand, the proposed approach combines predictive analytics to forecast passenger flows in flight networks from historical data and prescriptive analytics to optimize airport slot assignments in view of flight-centric and passenger-centric considerations. The problem is formulated as a mixed-integer nonconvex optimization model. To solve it, we propose an approximation scheme that alternates between flight-scheduling and passenger-accommodation modules and embed it into a large-scale neighborhood search algorithm. Using real-world data from the Singapore Changi and Lisbon Airports, we show that the proposed model and algorithm return solutions in acceptable computational times. Results suggest that slot-allocation outcomes can be made much more consistent with passenger flows at a relatively small cost in terms of flight displacement. Ultimately, this paper provides a new paradigm that can create more attractive flight schedules by bringing together airport-level considerations, airline-level considerations, and, for the first time, passenger-level considerations
Airline Network Planning: Mixed-integer non-convex optimization with demand–supply interactions
No full textAirlines routinely use analytics tools to support flight scheduling, fleet assignment, revenue management, crew scheduling, and many other operational decisions. However, decision support systems are less prevalent to support strategic planning. This paper fills that gap with an original mixed-integer non-convex optimization model, named Airline Network Planning with Supply and Demand interactions (ANPSD). The ANPSD optimizes network planning (including route selection, flight frequencies and fleet composition), while capturing interdependencies between airline supply and passenger demand. We first estimate a demand model as a function of flight frequencies and network configuration, using a two-stage least-squares procedure fitted to historical data, and then formalize the ANPSD by integrating the empirical demand function into an optimization model. The model is formulated as a non-convex mixed-integer program. To solve it, we develop an exact cutting plane algorithm, named 2αECP, which iteratively generates hyperplanes to develop an outer approximation of the non-linear demand functions. Computational results show that the 2αECP algorithm outperforms state-of-the-art benchmarks and generates tight solution quality guarantees. A case study based on the network of a major European carrier shows that the ANPSD provides much stronger solutions than baselines that ignore – fully or partially – demand–supply interactions
A space–time-energy flow-based integer programming model to design and operate a regional shared automated electric vehicle (SAEV) system and corresponding charging network
Get PDFShared automated vehicles are expected to be part of the supply of transportation systems in the future. Parallel to this evolution, there is the rapid penetration of battery electric vehicles (BEVs). The limitations in battery capacity and charging speed of BEVs can influence the planning and operation of shared automated electric vehicle (SAEV) systems. The design of such systems needs to include these limitations so that their viability is properly estimated. In this paper, we develop a space–time-energy flow-based integer programming (IP) model in support of the strategic design of a regional SAEV system. The proposed approach optimizes the fleet (size and composition) and charging facilities (number and location), while explicitly accounting for vehicle operations in aggregated terms (including movements with users, relocations, and charging times). The model is used to assess the impact of vehicle range and different types of chargers in the optimal design of an interurban SAEV transport system in the center of Portugal. Results show a reduction in profit as the vehicle range increases. In regards to energy, it is observed that the adoption of long-range vehicles reduces the energy spent in relocations, and increases the amount of energy charged at a lower price. Additionally, it is found that a system with long-range vehicles does not take advantage of having fast chargers. Concerning the chargers’ optimal location, systems using short-range vehicles have more chargers close to the main commuter trips attracting cities, while systems with long-range vehicles have the chargers nearby the homes of users
Foreward to the Special Issue: “Predictive and prescriptive analytics for service reorganization in times of transition”
No full textStarting from the general domain of the present special issue (entitled: “Predictive and prescriptive analytics for service reorganization in times of transition”) and the primary motivations for its launch, the editorial discusses the collection of papers accepted for publication. These articles showcase the use of a wide range of quantitative methodologies across multiple application fields, highlighting the relevance of analytics to decision-making theory and practice. Moreover, they underscore the potential for interdisciplinary approaches in tackling open and emerging challenges within evolving goods and service network ecosystems
An auction framework for assessing the tendering of subsidised routes in air transportation
Get PDFGovernments offer subsidies along routes that are deemed commercially non-viable but economically and socially essential. Subsidised routes are often criticised for inefficiencies and excessive subsidies, which partly result from restrictions defined by transportation authorities during the tendering process, such as a maximum airfare and minimum number of daily flights. We develop an integrated auction framework-referred to as Single-round Combinatorial Auction for Subsidised routes (SCAS)-to provide decision support to transportation authorities when designing tendering processes for subsidised routes. The framework includes two main models as ingredients. First, the Airline Bid Preparation Model (ABPM), which replicates the airline's behaviour when preparing bids for subsidised routes. Second, the Winner Determination Problem (WDP), which is used to select the bids based on a given evaluation criterion. We capture the responsive relationship between passenger demand and supply of air services by including passenger utility as an endogenous variable in the ABPM. Additionally, as input to the ABPM, we estimate the route operating cost for small aircraft that typically operate subsidised routes. The usefulness of the approach is demonstrated with an application to the network of subsidised routes in Sweden, for which we provide policy guidelines. Our analysis suggests that having a restriction on the airfare but not the number of flights is an effective way to design the tendering process, which strikes a good balance between passengers, government and airlines goals. Additionally, we demonstrate that the transportation authorities can compensate not having a requirement on the number of daily flights through ensuring a higher number of passengers, i.e., by including maximisation of the number of passengers in the bid evaluation criterion or using passenger discounts
Twinkle
No full textThe current city lighting system that leaves many areas uncovered induces unsafe perceptions and instigates crimes. The addition of ubiquitous surveillance is an intrusion on privacy and does not take real-time actions. The cold, lifeless light shines in the darkness, trapping people in the solitude of silence. These absences motivated us to create Twinkle - a luminous transformative creature inhabits on light posts. They are curious aerial animals attracted by human activities. During daytime, they rest on urban light posts, expanding their solar panels for charging. At night, they interact with individuals walking on the street in their own way based on their distinct personalities. Twinkles are indirect solutions for improving urban safety without surveillance. We envisage a future that appliance goes beyond machine and becomes a companion with us
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