A Decision Support System to Improve Performances of Airport Check-In Services

Publication Date

2019

Abstract

The recent remarkable increase in air passenger traffic has been fostering considerable congestion of airport facilities. In this context, traditional procedures employed for check-in operations have been supported by alternative methods, based on the use of self-service options (kiosks, web services, apps for mobile phones, etc). However, even if such innovations are contributing to improving the service level provided to passengers, field investigations suggest that traditional procedures will be employed also in the future, especially for medium and long-haul flights, where baggage dropping is required. For this reason, the passenger allocation problem at check-in counters is attracting growing attention by the scientific community and several decision support tools, involving both optimization and simulation methods, have been proposed. Most of the available approaches aim at deciding the optimal number of check-in counters to be activated in such a way as to balance operative costs and passenger waiting times. Such approaches assume that the service capacity (in terms of available check-in operators and counters) is given and determined on the basis of physical constraints (related to the available space in the terminal) and of staff scheduling decisions made at a tactical level. The present contribution tries to overcome this limitation by proposing a decision support system, based on a mathematical model, capable of designing optimal check-in policies by also incorporating staff scheduling decisions. The model is tested on some real-world case studies; computational results are evaluated, along with the practical usability of the approach.

Publisher

Springer Verlag

Creator

Giuseppe Bruno, et al.

Sponsor

Citation

Bruno, G., Diglio, A., Genovese, A., and Piccolo, C. 2019. A decision support system to improve performances of airport check-in services. Soft Computing: A Fusion of Foundations, Methodologies, and Applications, 23(9): 2877-2886. https://doi-org.ezproxy.lib.vt.edu/10.1007/s00500-018-3301-z

Identifier

ISSN 14327643

Type

text

Category

Journal Article

Language (ISO)

en_US

Subject (LLC)

TA