Airlines new strategies


Customer satisfaction is a measure of how an organization meets the customer's expectations on its products and services. Customer satisfaction is a key performance indicator for the airlines. Airlines are trying new strategies in a cost-effective manner to provide quality service to passengers. Airports and airlines are two different entities and they work together to conduct and maintain operations at airports. In general, facilities and services at airports are controlled by the airport operator or individual airline. The relationship between the airport operator and airline is very important to the success of aviation business. Therefore, in this literature review we will focus on studies related to both airlines and airports.

Several research studies have been conducted in the past addressing different problems of the airport and airline processes. The study includes a literature review in the following areas:

  • Airport facility or terminal requirements
  • Passenger flow through the airport terminal
  • Airline operations
  • Passenger check-in process
  • Airport security
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Simulation and operations research techniques were mostly used in the literature to solve airline and airport related problems. Airports and airline processes are common areas for applying simulation. The airline industry has long been a fertile area for applying optimization techniques.

Airport facility and terminal requirements

An airport terminal is a building with facilities for processing passenger and it allows passengers to board and deplane from their aircraft. Inside the terminal, passengers check-in for their flight at check-in counters, purchase tickets from ticketing agents, handover luggage to agents and go through security.

Kiran et al. (2001) and Hafizogullari et al. (2003) focused on the airport terminal changes and facility requirements. Kiran et al. (2001) developed a dynamic stochastic simulation model using ProModel software to validate the design of the new international terminal at Istanbul Ataturk airport in Turkey. This model evaluated the passenger flow and identified the system bottleneck similar to those reported by Gatersleben and Weij (1999). The main objective of Kiran et al (2001) study was to validate the terminal design and identify the system bottlenecks and as well as the system capabilities. As a result of this study, the author recommended solutions to eliminate those bottlenecks and demonstrated that the gate assignments are very important in maximizing the system performance.

Gate assignment to a flight is an important feature of an airport's operation. The main function of gate assignments is to assign aircrafts to suitable gates. Several research were done in the past addressing gate assignment problems. In most of the research, flights were assigned to gates based on the planned flight schedule. However in actual flight operations, there is a good chance for a flight to arrive early or late because of uncontrollable factors such as weather and controllable factors such as aircraft maintenance and crew scheduling etc. These flight delays often occur, causing stochastic disturbances to the already assigned gate assignments and reduces the airline operating performances. Yan and Tang (2007) developed a heuristic approach embedded in a framework to help the airport authorities to make airport gate assignments that are sensitive to stochastic flight delays. This heuristic approach framework integrates both the planning and real-time parameters to solve gate assignment problems under stochastic flight delays. Data from Taiwan international airport was used to study and evaluate the heuristic framework.

Prior to September 11, 2001 or 9/11 attacks, passenger screening at airports was controlled by airlines and the passenger had to pass through magnetometer or metal detector and pass his or her luggage through a x-ray machine before boarding his or her aircraft. In the immediate aftermath of the 9/11 attack Aviation and Transportation Security Act (ATSA) passed by Congress transferred civil aviation security to Transportation Security Administration (TSA) on November 2001. This act required all baggage handed over by passenger to airline to carry it under the aircraft have to be screened by explosives detection device. The ATSA also established a timeline for the TSA to perform 100% checked baggage screening. To determine the impact of the baggage screening requirements, Hafizogullari et al. (2003) conducted a case study at Lambert St.Louis International Airport (STL) to evaluate the equipment and facility requirements to meet 100% checked baggage screening for all airlines serving STL. Discrete event simulation was used to evaluate the baggage screening process and the author recommended use of a drop and go option for baggage screening at the lobby.

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Estimating the real estate space to process passenger at airport terminal is an important element of landside planning. Subprasom et al. (2002) developed an analytical tool based on deterministic queuing theory to estimate the passenger space by minimizing the total cost which is defined as the sum of the cost of constructing, operating, and maintaining the facility and the cost of passenger inconvenience. The passenger cost is defined as a function of average queue length and personal space.

Passenger flow through the airport terminal

Passenger flow is the number of passengers passing through the terminal or airport facility at any given point of time. The impact of the events of September 11, 2001 (9/11) is mostly felt at airports and as a precautionary measurement, passengers are experiencing number of stops with a variety of security checks through their entire journey from the entrance of the airport to the boarding gate of the aircraft. In this literature review, all the studies related to passenger flow before and after 9/11 are reviewed to understand the research objectives and the techniques used to solve the objectives.

Simulation has been very valuable in studying passenger flow in the airports. Gatersleben and Weij (1999) studied the presence of bottlenecks and their causes for the passenger to flow through the airport terminal and tested potential solutions to examine whether those solutions satisfied the passenger expectations.

As of 2001, 12 major airlines operated in United States use a hub-and-spoke network to route their aircraft. Hub-and-spoke operation is a system of connection in which all traffic moves along the spoke to a centrally located hub. A hub is an airport that flights are routed through and it increases the passenger transfer traffic. When there is no direct flight to a destination, passengers are required to make at least one connection through the hub airport before reaching their final destination. Therefore it is important for airlines to maintain minimum connect time for passengers to connect from one aircraft to another aircraft at the hub airport. Hafizogullari et al. (2002) conducted a case study for Delta Airlines at John F. Kennedy (JFK) international airport to model the transfer passenger flow at JFK airport. Simulation was used as a tool to build the model and predict passenger travel times and wait times at different processing points. This simulation model accounted for the airport design and operational policies at airports and determined the minimum connect times for the passenger to flow through JFK airport terminal.

A similar type of passenger flow study was done by Takakuwa and Oyama (2003) to analyze the passenger flow for the international departures at Kansai International airport in Japan. In this study, an entire airport is designed using an Arena simulation program to examine the passenger flow on both the third and fourth floors of the terminal building, where series of all required processes of international departures exists. Passengers arrive to the fourth floor and complete all the required processes for their departure and move to third floor to complete passport verification and gets on a monorail to reach boarding gate area. In this study, the time spent at each stage to depart an international flight is examined and the author recommended adding supporting staff at peak hours to reduce the passengers missing their flight.

Airline Operations

Researchers Martens and Adem (2001), Lee et al. (2003), and Khoury et al. (2005) have used simulation modeling to study the effect of airline operations. Air traffic management is an important factor for airlines. Increase in air travel demand and airport congestion requires airport to expand its capacity to meet the demand. If the demand is not met or other factors such as aircraft maintenance, air traffic congestion, bad weather, late arrival of an incoming aircraft, slow boarding, etc. causes delays in the system. Some of these delays happen because of an inadequacy in the network design. The following three researches in this section are primarily focused on airline flight delays.

Martens and Adem (2001) developed a flexible simulation module “to study the impact on punctuality of changes in the flight timetable, boosts in performance, the availability of spare aircraft, and many other operational improvement scenarios”. This flexible module allowed constructing any network of choice to evaluate and compare different airlines network time tables.

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In airline operations, different kinds of unexpected disruptions occur and affect the planned flight schedule. These disturbances result into loss of passenger revenue, satisfaction and crew utilization. Unexpected disruptions are not considered by the airlines in the planning stage and in the real event of a disruption, airlines attempt to implement recovering procedure to bring back the airline operation to normal condition. SIMAIR was designed to evaluate the airline schedule during disruptions and to test the methods used for recovering from disruptions (Lee et al, 2003). There have been multiple studies over the past years to provide recovery solutions, but none of the studies had a common tool to evaluate the effectiveness of recovery procedures. SIMAIR uses object oriented approach and it is designed in C++ programming language to evaluate airline operations.

Khoury et al. (2005) examined Detroit Metropolitan Airport to predict parameters of interest to air transportation system using a simulation model and 3D animation. This study differed from all of the above by focusing on airside elements such as runways, runway exits, taxiway segments, and gates. The main objective of Khoury et al. (2005) research is to predict runway capacity and delays at airports using discrete event simulation and validate the model using animation.

In addition to the above simulation models, some studies applying operations research techniques (Hutchinson and Hill, 2003; Azmat and Widmer, 2004; Kiatcharoenpol and Laosirihongthong, 2006; Yan and Tang, 2007) have been developed to solve airline related problems. Hutchison and Hill (2003) used simulation optimization to develop ways to minimize air delays caused due to weather, congestion or landing queues and looked into methods to maximize the on-time performance of departure flights. Azmat and Widmer (2004) described a heuristic approach with a three-step algorithm to quantify the minimal workforce requirement for weekly demand and also quantified daily work by minimizing the overtime hours.

Kiatcharoenpol and Laosirihongthong (2006) evaluated the quality in airline operations using mathematical model and statistical analysis. Kiatcharoenpol and Laosirihongthong (2006) suggested that “to improve service quality, the primary step is to evaluate the existing level of service quality and to set the service strategy to satisfying passengers”.

Passenger check-in process

Several studies have been focused on evaluating the check-in process at airports (Chung and Sodeinde, 2000; Yen et al. 2001; Appelt et al. 2007) and manpower planning for the check-in counters (Hon Wai and Mak, 1999; Van Dijk, 2006). Check-in is the first process that the passenger is involved with the airline at airport before flight departure. The key tasks involved in the check-in process are ticket inspection, passport verification for international travel, boarding pass issuance, baggage checking, seat assignment, etc.

Chung and Sodeinde (2000) applied simultaneous engineering concept to minimize customer processing time. Simultaneous engineering or concurrent engineering is a workflow that carries out number of tasks in parallel. Instead of working sequentially through the stages, concurrent engineering carries out the task in parallel to reduce product development time. A total of five alternative simultaneous service approaches were investigated using simulation analysis to assess the check-in process (Chung and Sodeinde, 2000) and found that there was a 36% improvement in the wait time and the passenger processing time.

Yen et al. (2001) used a mathematical model to measure the level of service in both check-in and baggage-claim processes and compared the perceived time reported by passengers and the observed time measured by researchers. Two set of data samples are obtained from both passenger and researcher and compared to measure the service level of airport terminal check-in process. The comparison analysis reveals that the perceived time is greater than the researcher time and the passenger is overestimating the time spent in the check-in process.

Delays in processing passengers at airport vary with times of the day, day of week and the staffing allocation. The customer satisfaction at airport is directly proportional to the time spent by the passenger at the airport. Appelt et al. (2007) studied the check-in system at a medium-sized airport to identify the delays in the check-in system and created scenarios to improve the efficiency of the system. This study was done at the Buffalo airport in United States and the researchers spent significant amount of time in collecting check-in process data on different hours of the day. Appelt et al. (2007) used the actual observed data and developed a simulation model using Arena software to analyze the time spent in the system.

Operating right number of check-in counters at right time of the day reduces the waiting time of the passenger at airport lobby to check-in for his or her flight. Hon Wai and Mak (1999) developed a knowledge based simulation system to predict resource requirements at an international airport. It determined how many check-in counters should be scheduled to satisfy passengers with sufficient quality of service. This knowledge based system considers the stochastic factors such as the passenger arrival rates for different times of the day and the different requirements for servicing different destinations. The simulation system is implemented at the Hong Kong Kai Tak international airport and it is in use since 1995.

Van Dijk & Van der Sluis (2006) investigated the check-in planning problem. This research used simulation and integer programming approach to determine the minimum requirement of resources by satisfying the required service level for each individual flight. The author uses simulation approach to analyze stochastic parameters and integer programming to examine deterministic factors of the airport check-in process.

Airport Security

Airport security is a combination of techniques and resources used in protecting airports, passengers and aircrafts from crime. The impact of 9/11 event have made many changes at airports in the United States and there are series of inspections done on baggage and passenger before the passenger boards his or her aircraft. The objective of airport screening is to make sure that no threat enters the system.

Lazar Babu et al (2004) used mixed integer linear program to explore the benefit of classifying passengers into different groups. The author assumed that the degree of inspection and the number of checks may vary for different groups, but the threat probability is identical for all passengers. In this research the author evaluates the false alarm rate and the key objective is to reduce false alarm probability.

As a follow-up to the above research by Lazar Babu et al. (2004), another research work by Nie et al. (2008) re-examined the risks associated with different passenger grouping. Instead of assuming that the threat probability is identical for all passengers, Nie et al. assumed that passengers are classified into different risk levels through a computer assisted passenger prescreening system. The main objective of Nie et al.'s research is to minimize the false alarm probability by considering all the information on passenger risk attributes.

Literature Review Summary

From the review of the literature done in this chapter it can be summarized that most of the research in airline industry was focused on the problems related to airport and airline business processes. All the previous studies had designed either new simulation or mathematical models from foundation to analyze the objectives. None of the study except Martens and Adem (2001) have discussed about building reusable model to address similar questions posted by airlines and airport authority. Although in the literature, different kinds of airline problems and different parts of airlines are addressed before but none of them have studied about the passenger segmentation and the impact of joining the wrong queue or polluting queue to finish his or her transaction.

The proposed research focuses on enhancing customer satisfaction by studying the check-in system of the airline. This primarily concentrates on studying the performance and service impact of passenger segmentation on check-in experience and its consequent queue pollution. This study uses reusable simulation component framework with an excel user interface to evaluate the system performance and utilizes the framework as a decision support system allowing any airline to consider alternatives in improving the current check-in process. The library of airline modules designed in this study will benefit airlines to reuse it multiple times to study the check-in system in a timely manner. A major US airline and its check-in system will be used to test and validate the methodology.