Jay M. Rosenberger

Rerouting Aircraft for Airline Recovery

Disruptions in airline transportation systems can prevent airlines from executing their schedules as planned. Adverse weather conditions, congestion at airports, and mechanical failures often hinder a flight schedule. During such events, decision makers must reschedule flight legs, and reroute aircraft, pilots, and passengers. We present an optimization model that reschedules legs and reroutes aircraft by minimizing an objective function involving rerouting and cancellation costs. We develop a heuristic for selecting which aircraft are rerouted, and we provide proof of concept by evaluating our model using a simulation of airline operations. Finally, we revise the model to minimize crew and passenger disruptions.

A Stochastic Model of Airline Operations

We present a stochastic model of the daily operations of an airline. Its primary purpose is to evaluate plans, such as crew schedules, as well as recovery policies in a random environment. We describe the structure of the stochastic model, sources of disruptions, recovery policies, and performance measures. Then, we describe SimAir--our simulation implementation of the stochastic model, and we give computational results. Finally, we give future directions for the study of airline recovery policies and planning under uncertainty.

A Robust Fleet-Assignment Model with Hub Isolation and Short Cycles

Airline decision makers cancel flights in operations because of disruptions. When canceling a flight, they usually cancel a cycle, a sequence of flights that begins and ends at the same airport. Consequently, a fleet assignment and aircraft rotation with many short cycles is frequently less sensitive to a flight cancellation than one with only a few short cycles. In this paper, we determine a lower bound for the number of short cycles using the hub connectivity of a fleet assignment, and we present fleet-assignment models (FAMs) that embed many short cycles and reduce hub connectivity within a solution. We show that solutions to such models perform better in operations than those of traditional FAMs that minimize planned operating cost and passenger spill.

A hybrid approach of goal programming for weapon systems selection

Because weapon systems are perceived as crucial in determining the outcome of a war, selecting weapon systems is a critical task for nations. Just as with other forms of decision analysis involving multiple criteria, selecting a weapon system poses complex, unstructured problems with a huge number of points that must be considered. Some defense analysts have committed themselves to developing efficient methodologies to solve weapon systems selection problems for the Republic of Koreas (ROK) Armed Forces. In the present study, we propose a hybrid approach for weapon systems selection that combines analytic hierarchy process (AHP) and principal component analysis (PCA) to determine the weights to assign to the factors that go into these selection decisions. These weights are inputted into a goal programming (GP) model to determine the best alternative among the weapon systems. The proposed hybrid approach that combines AHP, PCA and GP process components offsets the shortcomings posed by obscurity and arbitrariness in AHP and therefore can provide decision makers with more reasonable and realistic decision criteria than AHP alone. A case study on weapon system selection for the air force demonstrates the usefulness and effectiveness of the proposed hybrid AHP-PCA-GP approach.

A statistical computer experiments approach to airline fleet assignment

The fleet assignment model allocates a fleet of aircraft to scheduled flight legs in an airline timetable. The fleet assignment model addressed in this paper uses a two-stage stochastic programming framework along with the Boeing concept of demand driven dispatch to assign crew compatible aircraft in the first stage, so as to enhance the demand capturing potential of swapping in the second stage. A design and analysis of computer experiments approach is used to reduce the computation involved in solving the problem. The main contribution of this paper is a method to obtain an approximation for the expected profit function using a regression splines fit, generated over a Latin hypercube design. The results on the accuracy of the fit for a real airline carrier are presented and future work is discussed.

SimAir: a stochastic model of airline operations

Airline transportation systems are inherently random. However, airline planning models do not explicitly consider stochasticity in operations. Because of this, there is often a notable discrepancy between a schedules planned and actual performance. SimAir is a modular airline simulation that simulates the daily operations of a domestic airline. Its primary purpose is to evaluate plans, such as crew schedules, as well as recovery policies in a random environment. We describe the structure of SimAir, and we give future directions for the study of airline planning under uncertainty.

Optimizing revenue in CDMA networks under demand uncertainty

Mathematical programming models for telecommunications network design are prevalent in the literature, but little research has been reported on stochastic models for cellular networks. We present a stochastic revenue optimization model for CDMA networks inspired by bid pricing models from the airline industry. We describe the optimality conditions for the model and develop a supergradient algorithm to solve it. We provide computational results that show the effects of the distribution and variance of demand. Finally, we discuss areas of future research, including a method to optimize the locations of the towers.

A multivariate adaptive regression splines cutting plane approach for solving a two-stage stochastic programming fleet assignment model

The fleet assignment model assigns a fleet of aircraft types to the scheduled flight legs in an airline timetable published six to twelve weeks prior to the departure of the aircraft. The objective is to maximize profit. While costs associated with assigning a particular fleet type to a leg are easy to estimate, the revenues are based upon demand, which is realized close to departure. The uncertainty in demand makes it challenging to assign the right type of aircraft to each flight leg based on forecasts taken six to twelve weeks prior to departure. Therefore, in this paper, a twostage stochastic programming framework has been developed to model the uncertainty in demand, along with the Boeing concept of demand driven dispatch to reallocate aircraft closer to the departure of the aircraft. Traditionally, two-stage stochastic programming problems are solved using the L-shaped method. Due to the slow convergence of the L-shaped method, a novel multivariate adaptive regression splines cutting plane method has been developed. The results obtained from our approach are compared to that of the L-shaped method, and the value of demand-driven dispatch is estimated.

Queueing Network Models of the National Airspace System

Understanding the relationships between trajectory uncertainties due to aviation operations, precision of navigation and control, and the traffic flow efficiency are central to the design of next generation Air Transportation Systems. Monte-Carlo simulations using air traffic simulation software packages can be used to quantify these effects. However, they are generally time consuming, and do not provide explicit relationships for comparing various technology options. On the other hand, queuing models of the air traffic system can rapidly demonstrate the influence of trajectory uncertainties on traffic flow efficiency, facilitating tradeoff studies in an effective and time-efficient manner. A methodology for incorporating the trajectory uncertainty models into queuing network models of the air traffic at national, regional and local scales is discussed. Usefulness of these models in assessing the impact of uncertainties on traffic flow efficiency is illustrated.

Optimizing Load Control in a Collaborative Residential Microgrid Environment

This paper develops an analytical model for a residential microgrid (RMG) under a collaborative environment. The model assumes that the RMG community is under a social agreement referred to as Collaborative Consumption. The model includes a framework for RMGs using a unique method of demand response based on the particular characteristics of residential loads. Residential loads are categorized into three types based on their necessity and reschedulable ability. Consumers assign priority to their appliances. Then, the microgrid informs consumers about their real-time consumption and economic benefits associated with their participation in collaborative consumption. Accordingly, consumers can evaluate suggested alternatives prior to using appliances and consequentially make better decisions. Finally, the effects of model parameters on profit maximization are studied. The results of this paper estimate the economic benefits within a collaborative environment.