Research.

Strategies to decrease costs and wait times in the airline industry can be applied in many other industries. This month we highlight research that introduces these strategies. The first article describes an approach to making initial assignments of aircraft to flight segments to provide more flexibility to swap aircraft between flights at a later date if supply-demand imbalances arise. The second article focuses on a different type of flexibility: cross-training of employees when the work needs to be performed in different departments or locations, such as at an airport. These topics are addressed in the May issue of IIE Transactions (Volume 40, No. 5).

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Robust airline fleet assignments

Airline flight schedules are determined at least 45 days before the day of departure. Soon after, airlines must solve the difficult problem of determining which specific aircraft to assign to each flight while considering aircraft maintenance schedules and other practical constraints. But demands for the various flights are extremely difficult to predict so far in advance of the flight; airlines often find themselves in a predicament where they have assigned the wrong size aircraft to meet the actual demand. Swapping planes between flights closer to the date of departure when demand can be better predicted could reduce the supply-demand mismatch. This, however, can be accomplished only if it is possible to make a corresponding swap between flight crews--something that is often impossible due to different training requirements for flying each type of aircraft.

It is possible to choose assignments of aircraft to flights in such a way that it is easier to swap aircraft about two weeks before the departure date. This is a point in time when the ultimate demand is easier to predict and when a reallocation of seats between flights, achieved by swapping planes, allows the airlines to earn revenue on seats that would otherwise go empty.

In "Statistical Computer Experiments Approach to Airline Fleet Assignments," Venkata Pilla of American Airlines, Barry Smith of Sabre Research Group, and professors Jay Rosenberger and Victoria Chen of the University of Texas at Arlington present a method to attack this problem. The method combines a stochastic programming methodology for optimizing the assignment of aircraft to flights with a statistical methodology that reduces the computation time required for solving the optimization problem. The stochastic programming model optimizes the initial assignment of aircraft to flights while considering the future options of swapping aircraft closer to the departure date when warranted by the supply-demand imbalances. The statistical methodology uses a design and analysis of computer experiments approach to estimate the first-stage expected profit function, and the paper focuses on development of this approach.

The methodology has been tested on an actual airline network with 50 airports and over 2,300 flight segments. In a comparison to the deterministic fleet assignment model, the predicted expected profit from their method was higher for 71 percent of the tested solutions, indicating the potential for swapping to yield higher profit.

CONTACT: Venkata Pilla; venkata.pilla@aa.com; (817) 931-1712; Operations Research and Decision Support, American Airlines, 4333 Amon Carter Blvd., Fort Worth, TX 76155

Effective use of cross-training

In the wake of the economic downturn following the 9/11 tragedy, airlines needed to find ways to use their staff more efficiently in order to reduce operating costs. Since then, nearly all airlines have consolidated the many then-existing job categories into a much smaller number and have cross-trained their staff accordingly. As a consequence, a typical customer service agent at an airline is able to help you check-in for your flight, process the waiting list of stand-bys and request-for-upgrades at the gate, and collect your boarding pass as you board the plane. Similar arrangements are being used for other pools of employees, such as call center agents and nurses in hospitals.

Cross-training clearly provides these organizations more flexibility, and costs can usually be reduced as a result. But the question of how best to use that flexibility is difficult because it requires the ability to construct very detailed plans for allocating employees, sometimes involving hundreds of people. These plans must account for each employee's skill set as well as complicated patterns of work requirements that vary by department or location.

In "An Exact Algorithm for a Workforce Allocation Problem with Application to an Analysis of Cross-Training Policies," professor Michael Brusco of Florida State University develops an efficient optimization method to solve a detailed work force allocation problem. The optimization model accounts for the possibility of short-staffing and the nonlinear effects of short-staffing on customer service. The author used his optimization algorithm to explore the effects of factors such as absenteeism and demand variability on the performance of different types of cross-training policies. Among other things, he found that the degree of both absenteeism and demand variability have a marked impact on the value of cross-training.

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CONTACT: Michael Brusco; mbrusco@cob.fsu.edu; (850) 644-6512; Department of Marketing, College of Business, Florida State University, Tallahassee, FL 32306-1110

Candace "Candi" Yano, Ph.D., is a professor at the University of California at Berkeley, where she holds a joint appointment in the department of industrial engineering and operations research and in the Haas School of Business. She is the editor-in-chief of IIE Transactions and has been a member of IIE since 1983.

ABOUT IIE TRANSACTIONS

IIE Transactions is IIE's flagship research journal and is published monthly. It aims to foster exchange among researchers and practitioners in the industrial engineering community by publishing papers that are grounded in science and mathematics and motivated by engineering applications.

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EXECUTIVE SUMMARIES: EDITED BY CANDACE YANO