Virginie Lurkin

The Airline Container Loading Problem with Pickup and Delivery

This paper considers the loading optimization problem for a set of containers and pallets transported into a cargo aircraft that serves multiple airports. Because of pickup and delivery operations that occur at intermediate airports, this problem is simultaneously a Weight, and Balance Problem and a Sequencing Problem. Our objective is to minimize fuel and handling operation costs. This problem is shown to be NP-hard. We resort to a mixed integer linear program. Based on real-world data from a professional partner (TNT Airways), we perform numerical experiments using a standard B, and C library. This approach yields better solutions than traditional manual planning, which results in substantial cost savings.

Modeling in air transportation: cargo loading and itinerary choice

This is a summary of the author’s Ph.D. thesis supervised by Michael Schyns and defended on April 29, 2016 at the University of Liège, Belgium. The thesis is written in English and is available from the author upon request at vlurkin@gmail.com and from http://hdl.handle.net/2268/196615. We examine two problems as part of this dissertation. The first is a cargo loading problem. The aim is to load a set of containers and pallets into a cargo aircraft that serves multiple airports. Our work is the first to model cargo transport as a series of trips consisting of several legs at the end of which pickup anddelivery operationsmight occur. This problem is crucial for airlines because in an attempt to reduce their costs, most airlines prefer to load as many containers as possible, even if all the loaded containers do not have the same final destination. Our results demonstrate that it is possible to quickly find near optimal or excellent feasible loading plans, and that our approach leads to substantial savings with respect to typical manual approaches currently used in practice. The second problemwe examine involves the estimation of itinerary choice models that include price variables and correct for price endogeneity using a control function that uses several types of instrumental variables. The motivation for developing these models is to demonstrate the importance of accounting for price endogeneity and to estimate different price sensitivities as a function of advance purchase periods. This is important as the airline industry can use our results to incorporate different customer segments as revealed through high-yield and low-yield booking curves when evaluating the profitability of airline schedules. Results based on Continental U.S. markets for May 2013 departures showed that models that fail to account for price endogeneity overestimate customers’ value of time and result in biased price estimates and incorrect pricing recommendations. The advanced models estimated (nested logit and ordered generalized extreme value (OGEV) models) are shown to outperform the baseline multinomial logit model with regard to statistical tests and behavioral interpretations. Additionally, results show that price sensitivities vary as a function of advance purchase periods, with those purchasing high-yield products being less price sensitive than those purchasing low-yield products (across any advance purchase periods) and those purchasing closer to depar-

Accounting for Price Endogeneity in Airline Itinerary Choice Models: An Application to Continental U.S. Markets

Network planning models, which forecast the profitability of airline schedules, support many critical decisions, including equipment purchase decisions. Network planning models include an itinerary choice model that is used to allocate air total demand in a city pair to different itineraries. Multinomial logit (MNL) models are commonly used in practice and capture how individuals make trade-offs among different itinerary attributes; however, none that we are aware of account for price endogeneity. This study formulates an itinerary choice model that is consistent with those used by industry and corrects for price endogeneity using a control function that uses several types of instrumental variables. We estimate our model using a database of more than 3 million tickets provided by the Airlines Reporting Corporation. Results based on Continental U.S. markets for May 2013 departures show that models that fail to account for price endogeneity overestimate customers’ value of time and result in biased price estimates and incorrect pricing recommendations. The size and comprehensiveness of our database allows us to estimate highly refined departure time of day preference curves that account for distance, direction of travel, number of time zones traversed, departure day of week and itinerary type (outbound, inbound or one-way). These time of day preference curves can be used by airlines, researchers, and government organizations in the evaluation of different policies such as congestion pricing.