John-Paul Clarke

Planning for Robust Airline Operations: Optimizing Aircraft Routings and Flight Departure Times to Minimize Passenger Disruptions

Airlines typically construct their schedules assuming that every flight leg will depart and arrive as planned. Because this optimistic scenario rarely occurs, these plans are frequently disrupted and airlines often incur significant costs in addition to those originally planned. Flight delays and schedule disruptions also cause passenger delays and disruptions. A more robust plan can reduce the occurrence and impact of these delays, thereby reducing costs. In this paper, we present two new approaches to minimize passenger disruptions and achieve robust airline schedule plans. The first approach involves routing aircraft, and the second involves retiming flight departure times. Because each airplane usually flies a sequence of flight legs, delay of one flight leg might propagate along the aircraft route to downstream flight legs and cause further delays and disruptions. We propose a new approach to reduce delay propagation by intelligently routing aircraft. We formulate this problem as a mixed-integer programming problem with stochastically generated inputs. An algorithmic solution approach is presented. Computational results obtained using data from a major U.S. airline show that our approach can reduce delay propagation significantly, thus improving on-time performance and reducing the numbers of passengers disrupted. Our second area of research considers passengers who miss their flight legs due to insufficient connection time. We develop a new approach to minimize the number of passenger misconnections by retiming the departure times of flight legs within a small time window. We formulate the problem and an algorithmic solution approach is presented. Computational results obtained using data from a major U.S. airline show that this approach can substantially reduce the number of passenger misconnections without significantly increasing operational costs.

Optimization of R&D project portfolios under endogenous uncertainty

Project portfolio management deals with the dynamic selection of research and development (R&D) projects and determination of resource allocations to these projects over a planning period. Given the uncertainties and resource limitations over the planning period, the objective is to maximize the expected total discounted return or the expectation of some other function for all projects over a long time horizon. We develop a detailed formal description of this problem and the corresponding decision process, and then model it as a multistage stochastic integer program with endogenous uncertainty. Accounting for this endogeneity, we propose an efficient solution approach for the resulting model, which involves the development of a formulation technique that is amenable to scenario decomposition. The proposed solution algorithm also includes an application of the sample average approximation method, where the sample problems are solved through Lagrangian relaxation and a new lower bounding heuristic. The performance of the overall solution procedure is demonstrated using several implementations of the proposed approach.

A mixed integer program for flight-level assignment and speed control for conflict resolution

We consider the air traffic conflict resolution problem and develop an optimization model for generating speed trajectories that minimize the fuel expended to avoid conflicts. The problem is formulated by metering aircraft at potential conflict points. The developed model is a mixed integer linear program that can be solved in near real-time for large number of aircraft.

An Optimization Approach to Airline Integrated Recovery

Although the airline industry has benefited from advancements made in computational and operational research methods, most implementations arise from the frictionless environment of the planning stage. Because 22% of all flights have been delayed and 3% have been cancelled in the United States since 2001, schedule perturbations are inevitable. The complexity of the operational environment is exacerbated by the need for obtaining a solution in as close to real-time as possible. Given some time horizon, the recovery process seeks to repair the flight schedule, aircraft rotations, crew schedule, and passenger itineraries in a tractable manner. Each component individually can be difficult to solve, so early research on irregular operations has studied these problems in isolation, leading to a sequential process by which the recovery process is conducted. Recent work has integrated a subset of these four components, usually abstracting from crew recovery. We present an optimization-based approach to solve the fully integrated airline recovery problem. After our solution methodology is presented, it is tested using data from an actual U.S. carrier with a dense hub-and-spoke network using a single-day horizon. It is shown that in several instances an integrated solution is delivered in a reasonable runtime. Moreover, we show the integrated approach can substantially improve the solution quality over the incumbent sequential approach. To the best of our knowledge, we are the first to present computational results on the fully integrated problem.

Near Real-Time Fuel-Optimal En Route Conflict Resolution

In this paper, we consider the air-traffic conflict-resolution problem and develop an optimization model to identify the required heading and speed changes of aircraft to avoid conflict such that fuel costs are minimized. Nonconvex fuel functions in the optimization problem are modeled through tight linear approximations, which enable the formulation of the problem as a mixed-integer linear program. The significance of the developed model is that fuel-optimal conflict-resolution maneuvers can be identified in near real time, even for conflicts involving a large number of aircraft. Computational tests based on realistic air-traffic scenarios demonstrate that conflicts involving up to 15 aircraft can be solved in less than 10 s with an optimality gap of around 0.02%.

Risk Aversion to Short Connections in Airline Itinerary Choice

Network airlines traditionally attempt to minimize passenger connecting times at hub airports, assuming that passengers prefer minimum scheduled elapsed times for their trips. However, minimizing connecting times creates schedule peaks at hub airports. These peaks are extremely cost-intensive in terms of additional personnel, resources, runway capacity, and schedule recovery. Consequently, passenger connecting times should be minimized only if the anticipated revenue gain of minimizing passenger connecting times is larger than the increase in operating cost (i.e., if this policy increases overall operating profit). The extent to which a change in elapsed time affects passenger itinerary choice—and thus an airlines market share—is analyzed. An existing airline itinerary choice survey is extended to test the assumption that passenger demand is affected by the length of connecting times. Previous studies have not explicitly focused on the connecting time at hubs in their models. The hypothesis is that passe...

State of the Practice: A Review of the Application of OR/MS in Freight Transportation

Freight transportation is an important part of the global supply chain. As distances shipped grow and supply chains become more complex and fragile, operations research OR can play an important role in improving the efficiency and robustness of supply networks. This article describes the state of the practice in OR and freight transportation, highlighting recent successful and widely used analytical techniques in oceanic transportation and port operations, and barge, freight rail, intermodal, truckload, less than truckload, and air freight transportation, as well as the use of OR techniques in third-party logistics.

Assigning Gates by Resolving Physical Conflicts

The ramp area is a complex portion of the airport, often congestion and interactions between aircraft limit ecient ows for arriving and departing aircraft. Aircraft taxiing trac is constrained by minimum aircraft separation, and geometry of airports. In this paper, we present a new approach to assign gate to minimize ramp congestion and propose a simulation model to predict the operation time within the congested ramp. The optimized gate assignment reduces ramp operation time up by 50%. In addition the estimation of ramp operation times is more accurate when applying the optimized gate assignment algorithm.

History, Development and Analysis of Noise Abatement Arrival Procedures for UK Airports

*† ‡ § ¶ There is increasing pressure to reduce the environmental impacts of air transport operations. To that end, a discussion of noise abatement procedures for approach operations in the busy UK airspace is presented in this paper. Firstly, a brief history of noise abatement procedures is presented together with a description of the most promising operational technique: Continuous Descent Approach procedures. The impacts of airspace constraints and the trade-offs between environmental benefits and operational flexibility in the noise abatement procedure design process are discussed in the context of the UK environment and compared to less congested airspace in other parts of the world. The development of noise abatement procedures can be supported through the use of analysis tools that enable impacts on key metrics to be assessed, including environmental variables (such as noise, fuel burn and emissions) and operational factors (such as “flyability” by different aircraft types and impacts on runway capacity). A suite of tools to examine these key impacts are described. A case study of the development of noise abatement approach procedures at a candidate airport is used to illustrate the concepts and tools discussed in a specific circumstance. Finally, the impact of advanced technologies and designs are discussed to highlight the most promising areas of research that may enable further improvement in the future.

Optimizing Pushback Decisions to Valuate Airport Surface Surveillance Information

As airport surface surveillance technologies develop, aircraft ground position information becomes more easily available and accurate. This paper provides a better understanding of the value of future surface surveillance systems where departures, and more specifically pushback times, will be optimized. It analytically quantifies the potential benefits yielded by providing surveillance information to the agent or system that is entrusted with tactically optimizing pushback clearances under nominal conditions. A stochastic model of surface operations is developed for single-ramp surface operations and calibrated to emulate departure surface operations at LaGuardia Airport. Two levels of information are examined within a tactically optimized collaborative decision-making framework. For each level, emissions, number of taxiing aircraft, and runway utilization rate are analyzed and compared with a simple threshold policy to evaluate surface surveillance information. Safety benefits, however, are not considered in this paper. It is estimated that optimally controlling pushback clearances from a single-ramp area using detailed surface surveillance information does not provide significant benefits when compared with controlling pushback clearances using a gate-holding policy based on the number of aircraft currently taxiing. However, when the runway is functioning at intermediate capacity (50%-72% runway utilization rates), e.g., under adverse weather conditions, surveillance information may improve optimization of departure operations. In such case, emissions and the number of taxiing aircraft are reduced by up to 6% when compared with the gate-holding policy and by up to 3% when compared with the performance of an intelligent operator with limited information.