A Joint Planning, Management and Operations Framework for Airport Infrastructure

Many airports around the world are actively considering development or expansion projects. Such projects can spur tremendous benefits but are investment-intensive and span several decades from conception to completion. We formulate the associated dynamic, complex decision-making problems using a broad systems frame. We propose a conceptual framework that links airport infrastructure investments and airport management and operations in a time-expanded, state-contingent problem. To develop this framework we consider the social and policy objectives for well functioning air transportation infrastructure, the decision levers available to stakeholders, the influence of the institutional field and regulatory context on these decisions, and the key performance measures that operationalize system ilities. Our framework integrates literature from investments under uncertainty, airport demand management, and airport operating procedures. Four case examples of airports in Delhi, Charlotte, London and New York illustrate decision-making in the context of our framework. We argue for a more integrated approach to decision-making while evaluating investments in greenfield airports or capacity expansions

Endogenous Control of Service Rates in Stochastic and Dynamic Queuing Models of Airport Congestion

Airport congestion mitigation requires reliable delay estimates. This paper presents an integrated model of airport congestion that combines a tactical model of capacity utilization into a strategic queuing model. The model quantifies the relationships between flight schedules, airport capacity and flight delays, while accounting for the way arrival and departure service rates can be controlled over the day to maximize operating efficiency. We show that the model estimates well the average and variability of the delays observed at New York’s airports. Results suggest that delays can be extremely sensitive to even small changes in flight schedules or airport capacity

Congestion Mitigation through Schedule Coordination at JFK: An Integrated Approach

Most flight delays are created by large temporary or long-term imbalances between demand and capacity at the busiest airports. Absent large increases in capacity, airport congestion can only be mitigated through improvements in the utilization of available capacity and the implementation of demand management measures. This paper presents an integrated approach that jointly optimizes the airport’s flight schedule at the strategic level and the utilization of airport capacity at the tactical level, subject to scheduling and capacity constraints. The capacity utilization part involves controlling the runway configuration and the balance of arrival and departure service rates to minimize congestion costs. The schedule optimization reschedules a selected set of flights to reduce the demand-capacity mismatches while minimizing interference with airline competitive scheduling. We develop an original iterative solution algorithm that integrates airport stochastic queue dynamics and a Dynamic Programming model of airport operating procedures into an Integer Programming model of flight rescheduling. The algorithm is shown to converge in reasonable computational times and is thus implementable in practice. Extensive computational results for JFK Airport suggest that very substantial delay reductions can be achieved through limited changes in airline schedules. It is also shown that the proposed integrated approach to airport congestion mitigation performs significantly better than the typical sequential approach where scheduling and operational decisions are made separately

A queuing model of airport congestion and policy implications at JFK and EWR

Thesis (S.M. in Technology and Policy)-- Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 119-121).Since the phasing-out of the High Density Rule, access to major commercial airports in the United States has been unconstrained or, in the case of the airports of New York, weakly constrained. This largely unregulated demand combined with capacity constraints led to record delay levels in 2007, whose costs were estimated as in excess of $30 billion a year. Mitigating airport congestion may be achieved through demand management measures. Quantifying the benefits of such measures requires careful modeling of flight delays as a function of flight schedules. This thesis applies a stochastic and dynamic queuing model to analyze operations at JFK and Newark (EWR), two of the most congested airports in the United States. Two models are used to approximate the dynamics of the queuing system: a numerical model called DELAYS and a new Monte Carlo simulation model, which combines time-varying stochastic models of demand and capacity. These two models are then calibrated and validated using historical records of operations. In particular, they provide estimates of the average throughput rate at JFK and EWR under different weather conditions. The models are then shown to predict accurately both the magnitude of the delays and their evolution over the course of a day of operations. In addition, the Monte Carlo simulation model evaluates reasonably well the variability of the delays between successive days of operations. These two models are then applied to a study of recent trends in scheduling and ontime performance at JFK and EWR. The analysis indicates that the significant delay reductions observed between 2007 and 2010 can be largely attributed to the relatively small reduction of airport demand over this period. In particular, it demonstrates the strongly nonlinear relationship between demand and delays when airports operate close to capacity. It also shows that, for a given daily number of flights, the more evenly they are distributed in a day, the lower the resulting delays are likely to be.by Alexandre Jacquillat.S.M.in Technology and Polic

Additional Results and Extensions for the paper "Probabilistic bounds on the k−k-Traveling Salesman Problem and the Traveling Repairman Problem''

This technical report provides additional results for the main paper ``Probabilistic bounds on the $k-$Traveling Salesman Problem ($k-$TSP) and the Traveling Repairman Problem (TRP)''. For the $k-$TSP, we extend the probabilistic bounds derived in the main paper to the case of distributions with general densities. For the TRP, we propose a utility-based notion of fairness and derive constant-factor probabilistic bounds for this objective, thus extending the TRP bounds from the main paper to non-linear utilities

Probabilistic bounds on the k−k-Traveling Salesman Problem and the Traveling Repairman Problem

The $k-$traveling salesman problem ($k$-TSP) seeks a tour of minimal length that visits a subset of $k\leq n$ points. The traveling repairman problem (TRP) seeks a complete tour with minimal latency. This paper provides constant-factor probabilistic approximations of both problems. We first show that the optimal length of the $k$-TSP path grows at a rate of $\Theta\left(k/n^{\frac{1}{2}\left(1+\frac{1}{k-1}\right)}\right)$. The proof provides a constant-factor approximation scheme, which solves a TSP in a high-concentration zone -- leveraging large deviations of local concentrations. Then, we show that the optimal TRP latency grows at a rate of $\Theta(n\sqrt n)$. This result extends the classical Beardwood-Halton-Hammersley theorem to the TRP. Again, the proof provides a constant-factor approximation scheme, which visits zones by decreasing order of probability density. We discuss practical implications of this result in the design of transportation and logistics systems. Finally, we propose dedicated notions of fairness -- randomized population-based fairness for the $k$-TSP and geographical fairness for the TRP -- and give algorithms to balance efficiency and fairness

Airport Congestion Mitigation through Dynamic Control of Runway Configurations and of Arrival and Departure Service Rates under Stochastic Operating Conditions

The high levels of flight delays require the implementation of airport congestion mitigation tools. In this paper, we optimize the utilization of airport capacity at the tactical level in the face of operational uncertainty. We formulate an original Dynamic Programming model that selects jointly and dynamically runway configurations and the balance of arrival and departure service rates at a busy airport to minimize congestion costs, under stochastic queue dynamics and stochastic operating conditions. The control is exercised as a function of flight schedules, of arrival and departure queue lengths and of weather and wind conditions. We implement the model in a realistic setting at JFK Airport. The exact Dynamic Programming algorithm terminates within reasonable time frames. In addition, we implement an approximate one-step look-ahead algorithm that considerably accelerates the execution of the model and results in close-to-optimal policies. In combination, these solution algorithms enable the on-line implementation of the model using real-time information on flight schedules and meteorological conditions. The application of the model shows that the optimal policy is path-dependent, i.e., it depends on prior decisions and on the stochastic evolution of arrival and departure queues during the day. This underscores the theoretical and practical need for integrating operating stochasticity into the decision-making framework. From comparisons with an alternative model based on deterministic queue dynamics, we estimate the benefit of considering queue stochasticity at 5% to 20%. Finally, comparisons with advanced heuristics aimed to imitate actual operating procedures suggest that the model can yield significant cost savings, estimated at 20% to 30%

Branch-and-Price for Prescriptive Contagion Analytics

Predictive contagion models are ubiquitous in epidemiology, social sciences, engineering, and management. This paper formulates a prescriptive contagion analytics model where a decision-maker allocates shared resources across multiple segments of a population, each governed by continuous-time dynamics. We define four real-world problems under this umbrella: vaccine distribution, vaccination centers deployment, content promotion, and congestion mitigation. These problems feature a large-scale mixed-integer non-convex optimization structure with constraints governed by ordinary differential equations, combining the challenges of discrete optimization, non-linear optimization, and continuous-time system dynamics. This paper develops a branch-and-price methodology for prescriptive contagion analytics based on: (i) a set partitioning reformulation; (ii) a column generation decomposition; (iii) a state-clustering algorithm for discrete-decision continuous-state dynamic programming; and (iv) a tri-partite branching scheme to circumvent non-linearities. Extensive experiments show that the algorithm scales to very large and otherwise-intractable instances, outperforming state-of-the-art benchmarks. Our methodology provides practical benefits in contagion systems; in particular, it can increase the effectiveness of a vaccination campaign by an estimated 12-70%, resulting in 7,000 to 12,000 extra saved lives over a three-month horizon mirroring the COVID-19 pandemic. We provide an open-source implementation of the methodology in an online repository to enable replication

Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer Ridesharing

Unlike commercial ridesharing, non-commercial peer-to-peer (P2P) ridesharing has been subject to limited research -- although it can promote viable solutions in non-urban communities. This paper focuses on the core problem in P2P ridesharing: the matching of riders and drivers. We elevate users' preferences as a first-order concern and introduce novel notions of fairness and stability in P2P ridesharing. We propose algorithms for efficient matching while considering user-centric factors, including users' preferred departure time, fairness, and stability. Results suggest that fair and stable solutions can be obtained in reasonable computational times and can improve baseline outcomes based on system-wide efficiency exclusively

Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs

Ground delay programs (GDPs) comprise the main interventions to optimize flight operations in congested air traffic networks. The core GDP objective is to minimize flight delays, but this may not result in optimal outcomes for passengers—especially with connecting itineraries. This paper proposes a novel passenger-centric optimization approach to GDPs by balancing flight and passenger delays in large-scale networks. For tractability, we decompose the problem using a rolling procedure, enabling the model’s implementation in manageable runtimes. Computational results based on real-world data suggest that our modeling and computational framework can reduce passenger delays significantly at small increases in flight delay costs through two main mechanisms: (i) delay allocation (delaying versus prioritizing flights) and (ii) delay introduction (holding flights to avoid passenger misconnections). In practice, however, passenger itineraries are unknown to air traffic managers; accordingly, we propose statistical learning models to predict passenger itineraries and optimize GDP operations accordingly. Results show that the proposed passenger-centric approach is highly robust to imperfect knowledge of passenger itineraries and can provide significant benefits even in the current decentralized environment based on collaborative decision making. </jats:p