Dynamic allocation of check-in facilities and dynamic assignment of passengers at air terminals

https://doi.org/10.1016/j.cie.2012.04.003Get rights and content

Abstract

This study explores the dynamic allocation of check-in facilities and dynamic assignment of passengers at air terminals to achieve the objectives of minimizing total waiting time and better utilization of facilities. Taking into consideration different check-in services required by departing passengers, adjustments to allocations are made according to the maximum allowable wait time and the lowest service counter utilization rate allowed for the initial allocation condition. The developed model was validated for its feasibility and applied at the Taoyuan International Airport, Taiwan. The application results showed that dynamic allocation of check-in facilities can both reduce waiting times and increase service counter utilization rates. Such benefits can be further enhanced by dynamic assignment of passengers.

Highlights

► We explored the dynamic allocation of check-in facilities at air terminals. ► The model achieves minimization of waiting time and better utilization of facilities. ► Benefits can be further enhanced by dynamic assignment of passengers. ► The results obtained can serve as useful references for airline and airport.

Introduction

Along with incessant growth in world population, there is a concomitant increase in the number of air passengers. From 2003 through 2008 there was a significant 40% growth in total volume of air passenger (International Civil Aviation Organization, ICAO, 2008). However, global economic changes, such as the 2008 financial tsunami, and the emergence of new types of pandemic diseases have also caused a marked decrease in air travel. Hence, airline operators not only have to handle these demand fluctuations with dynamic assignments of fleets and facilities, but simultaneously seek ways to provide good service, and even enhance passenger satisfaction. Diversified check-in facilities offer many advantages to air passengers, such as enabling them to choose seats that meet their needs, preventing congestion and delay at check-in counters, and reducing check-in times. With better and more efficient check-in services, passengers have more free time for leisure while waiting for departure, which can mean more business opportunities and greater profits for commercial enterprises located at air terminals (Hsu & Chao, 2005). As such, airline operators should optimize check-in facilities both for cost minimization and passenger satisfaction. In view of these issues, this study explores the dynamic allocation of check-in facilities and dynamic assignment of passengers at air terminals.

Between arrival at the airport and boarding the flight, air passengers have to go through passenger and baggage check-in, security check, as well as immigration and customs. Among these routine formalities, security checks, as well as immigration and customs, are carried out on all crew and passengers as standardized procedures in accordance with regulations for national security and flight safety. In contrast, passenger and baggage check-in procedures vary depending on the operations of different airlines and airports. However, restricted timeframes for check-in before departure, limited numbers of available check-in counters, and special facilities provided for privileged passengers, such as first-class passengers or members of certain loyalty programs, may result in delays for other passengers. This not only damages the reputation and image of airports and airlines, but also affects the rights and interests of passengers. Therefore, effective and efficient utilization of resources to meet the check-in needs of air passengers has become an important issue for both airport and airline operators.

Kiosks (automated self-service check-in machines) are designed as one form of airport infrastructure, and act as a (i) time saver for passengers, (ii) cost saver for airlines, and (iii) space saver for airports (International Air Transport Association, IATA, 2006). Self-service technologies have already been extensively implemented in the airline industry and the IATA estimated common-use self-service (CUSS) savings of US$2.50 per check-in. With a 40% market penetration at every airport, the total annual industry savings add to US$ 1 billion (Lott, 2005). A passenger self-service survey done by Société International de Télécommunications Aéronautiques and Air Transport World (SITA/ATW) in 2009, however, indicated that no more than half of the passengers departing from the airports surveyed used self-service check-in (Karp, 2009). Of passengers who did not use self-service check-in options, many cited the need for checking baggage, which requires the assistance of an agent, as the main deterrent.

The airline industry employs a variety of self-service technologies, including kiosks, and online and mobile check-in technologies. Self-service check-in saves time for passengers and reduces operating costs for airlines (Weiss, 2006). As a result, more airlines plan to increase the number of self-service check-in kiosks and offer web and mobile check-in services (Jenner, 2009). In view of continuous growth in air transportation and increasing demand for self-service check-in, new facilities and strategies have been proposed with the objective of simplifying check-in procedures and minimizing the time required. Beginning in 2006, the SITA has conducted an annual self-service survey for passengers of different nationalities (SITA, 2009). Results of past surveys reveal increasing acceptance of self-service check-in facilities; in particular, online check-in and self-service check-in kiosks. In sum, studies focusing on passengers’ needs for check-in services are still lacking. As a result, current facilities and services often fall short of meeting passengers’ expectations or enhancing their satisfaction.

Literature on the issue of check-in facilities mainly concerns approaches to achieving more efficient check-in procedures. Research approaches previously employed include the queuing theory, system simulation, integer and dynamic planning, as well as experimental designs. Topics that have been explored include queuing time and space, walking distance, queuing methods, and allocation of facilities. For example, Parlar and Sharafali (2008) employed the queuing optimization approach to dynamic allocation of check-in counters; and Yan, Tang, and Chen (2004) proposed a model and a solution algorithm for assignment of flights to check-in counters. Taking into consideration the time and number of check-in counters to be opened, queuing length, waiting time and baggage-belt loading, Chun (1996) developed a constraint-satisfaction problem (CSP) algorithm to solve the check-in counter scheduling problem.

Following that, Chun and Mak (1999) applied intelligent resource simulation to check-in counter allocation. Yan et al. (2004) established three binary integer planning models and a solution algorithm for airport common-use check-in counter assignments. However, their models did not take into consideration the fluctuations in number of check-in counters available. Using binary integer planning and simulation in combination, Dijk and Sluis (2006) computed and optimized the number of check-in counters and the duration of open time needed for each flight. Bruno and Genovese (2010) proposed some models for determining the optimal number of check-in counters to be opened for departing flights, so as to balance operation costs of the service and passenger waiting time at the terminal. Stolletz (2010) addressed operational models for workforce planning for check-in systems at airports. He characterized different tasks of the hierarchical workforce planning problem with time-dependent demand. Finally, the assignment and reassignment of check-in counters to flights were analyzed with respect to archiving service levels (see Duin and Van der Sluis, 2006, Parlar and Sharafali, 2008; Yan, Tang, & Chen, 2008).

From the overview above, it is evident that past studies focused mainly on analyzing the allocation of counters and staff for passenger check-in and seldom explored different check-in facilities and various types of check-in services required by departing passengers. According to the four types of check-in services, namely ticket purchase, check-in, boarding pass, and checking baggage, this study developed seven combinations of services required by departing passengers. Taking into consideration current check-in facilities, including counter, kiosk, online and barcode check-in, we developed a model for allocation of check-in counters and self-service check-in kiosks at different time points according to the criteria of maximum waiting time and lowest service counter utilization rate allowed. Applying the model can enable passengers to spend less time on check-in and help airlines save human resources and operation costs.

This study reviews currently available check-in facilities and analyzes the operation strategies of airline operators. A model is formulated for dynamic allocation of check-in facilities and assignment of departing passengers to minimize waiting time for check-in and to reduce operation costs of airlines. The developed model is validated on its feasibility and applied at the Taoyuan International Airport, Taiwan. The results obtained can serve as useful references for airline and airport operators. The rest of the paper is organized as follows: Section 2 details the model formulation; Section 3 validates the developed model, and Section 4 presents its application. Finally, Section 5 contains the conclusion of this study and suggestions for future research.

Section snippets

Model formulation

This study explores the operation and planning of check-in facilities with different durations of open times for check-in. A model was developed for dynamic allocation of various check-in facilities and dynamic assignment of passengers with the target of minimizing waiting time for passengers. Let d denote a check-in facility and D denote all check-in facilities provided by an airline. With reference to the literature and operations of different airlines, current check-in facilities include

Model validation

To validate the contribution and value of the developed model in a practical application, a comparison is made between actual selection of check-in facilities by departing passengers and dynamic assignment of passengers according to the model. Data regarding check-in facilities actually selected by departing passengers at Taoyuan International Airport were collected through an on-site questionnaire survey conducted from April 5–25, 2010. Counter check-in was available for service from 3 h to 40 

Model application

The developed model was applied to dynamic assignment of check-in facilities for China Airline at the Taoyuan International Airport. The departure lounge of the Taoyuan International Airport is located on Concourse 1F of Terminal 1 and Concourse 3F of Terminal 2. At Terminal 1, China Airline has nine self-service check-in kiosks for passengers to choose seats, print boarding passes, and drop baggage. Departing flights of China Airline have an average passenger load factor of 77%, with an

Conclusions and suggestions

This study explored the operation and planning of check-in facilities from the standpoint of airline companies. A model was developed for dynamic allocation of facilities and dynamic assignment of passengers with the target of minimizing waiting time for passengers. The feasibility of the developed model is validated by case analysis by comparing actual data from free selection of check-in facilities by passengers and dynamic assignment of passengers to check-in facilities. Results of the

Acknowledgement

The authors thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC 98-2221-E-009-105-MY3.

References (22)

  • IATA (2006). Simplifying the business: 2006 StB horizontal campaign. Montreal: International Air Transport...
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