Alexandre Jacquillat

An Integrated Scheduling and Operations Approach to Airport Congestion Mitigation

Most flight delays are created by imbalances between demand and capacity at the busiest airports. Absent large increases in capacity, airport congestion can only be mitigated through scheduling interventions or improved capacity utilization. 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, capacity, and delay-reduction 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 a stochastic queuing model of airport congestion, a dynamic programming model of capacity utilization, and an integer programming model of scheduling interventions. The algorithm is shown to converge in reasonable computational times. Extensive computational results for JFK Airport suggest that 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.

Dynamic Control of Runway Configurations and of Arrival and Departure Service Rates at JFK Airport Under Stochastic Queue Conditions

High levels of flight delays require implementation of airport congestion mitigation tools. In this paper, we optimize the use of airport capacity at the tactical level in the face of operational uncertainty. We formulate an original Dynamic Programming model that jointly and dynamically selects 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. 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 execution of the model and results in close-to-optimal policies. Together, these solution algorithms enable online implementation of the model using real-time information on flight schedules and meteorological conditions. 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 heuristics designed to imitate actual operating procedures suggest that the model can yield significant cost savings, estimated at 20% to 30%.

Interairline Equity in Airport Scheduling Interventions

In the absence of opportunities for capacity expansion or operational enhancements, air traffic congestion mitigation may require scheduling interventions to control overcapacity scheduling at busy airports. Previous research has shown that large delay reductions could be achieved through comparatively small changes in the schedule of flights. While existing approaches have focused on minimizing the overall impact across the airlines, this paper designs, optimizes, and assesses a novel approach for airport scheduling interventions that incorporates interairline equity objectives. It relies on a multilevel modeling architecture based on on-time performance (i.e., mitigating airport congestion), efficiency (i.e., meeting airline scheduling preferences), and equity (i.e., balancing scheduling adjustments fairly among the airlines) objectives, subject to scheduling and network connectivity constraints. Theoretical results show that, under some scheduling conditions, equity and efficiency can be jointly maximi...

Deployment and utilization of plug-in electric vehicles in round-trip carsharing systems

ABSTRACT Electric Vehicles (EVs) and Plug-in Hybrid Electric Vehicles (PHEVs) can reduce gasoline consumption, but increase vehicle acquisition costs and introduce operational constraints. We develop a comprehensive approach to EV/PHEV deployment and utilization in round-trip carsharing systems. First, we formulate and solve the tactical problem of utilizing a mix of gasoline vehicles and EVs/PHEVs to serve trip demand, using Mixed Integer Programming optimization to estimate the minimal gasoline consumption in a computationally efficient manner, and simulation to assess the effect of reservation order on realized gasoline consumption. Second, we use these results to inform the strategic deployment of EVs/PHEVs in the carsharing fleet, using meta-optimization. We implement our approach using data from a large carsharing provider. From the perspective of a carsharing operator, our results suggest that replacing some portion of existing gasoline fleets by EVs/PHEVs would result in gasoline savings likely to outweigh upfront investments and the constraints on vehicle utilization that it creates. Moreover, we find that easily implementable heuristics can capture some of these benefits, and that the integration of vehicle utilization patterns into the design of EV/PHEV deployment strategies can result in added benefits.

Congestion Mitigation at John F. Kennedy International Airport in New York City: Potential of Schedule Coordination

With the significant growth in air traffic experienced during the past few decades, airport capacity has become an increasingly costly constraint. Flight delays reached record-high levels in 2007, with a nationwide impact estimated at more than

Endogenous control of service rates in stochastic and dynamic queuing models of airport congestion

30 billion for that calendar year. At airports where capacity expansion and improvements in operational efficiency are not feasible, congestion could be mitigated in the short and medium terms through implementation of schedule coordination mechanisms. Such measures essentially reduce peak hour scheduling levels; however, these measures have been criticized for the constraints that they might create on airline scheduling. This paper presents a schedule coordination model that reduces flight delays while minimizing interference with airline scheduling and then applies the model to one of the most congested U.S. airports, John F. Kennedy International Airport in New York City. The analysis suggests that it may be possible to reduce peak arrival and departure delays by more than 30% and 50%, respectively, without eliminating flights, aircraft connections, or passenger connections and without modifying the scheduled departure and arrival times of any flight by more than 30 min. These results underscore the potential of schedule coordination as a means of achieving substantial congestion cost savings at the busiest U.S. airports. Opportunities and challenges associated with the implementation of such a mechanism are discussed.