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Dynamic Allocation of Airline Check-In Counters: A Queueing Optimization Approach
Author(s) -
Mahmut Parlar,
Sharafali Moosa
Publication year - 2008
Publication title -
management science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.954
H-Index - 255
eISSN - 1526-5501
pISSN - 0025-1909
DOI - 10.1287/mnsc.1070.0842
Subject(s) - queueing theory , computer science , queue , operations research , process (computing) , mathematical optimization , population , computer network , mathematics , operating system , demography , sociology
This paper was motivated by an observation in an international airport with regard to allocation of resources for check-in counters. In an exclusive check-in counter system, each flight has a dedicated number of counters that will be open until at least a half-hour before the scheduled departure of that flight. Currently, in many of the airports around the world, the decision to open or close check-in counters is done on an ad hoc basis by human schedulers. In doing so, the schedulers are almost always forced to perform a balancing act in meeting the quality of service stipulated by the airport authority vis-à-vis the optimal allocation of the resources to the counters. There appear to be very few academic and application papers in counter management, and most of those that have looked into this problem have resorted to simulation to study the queue characteristics. Ours is the first paper to show that for a specific flight, this complicated problem is amenable to analytical treatment. We first propose a multicounter queueing model with a special type of arrival process reflecting reality from the population of passengers booked for the flight. Most importantly, we derive the time-dependent operating characteristics to the queueing process under a specified time-window constraint. Then a stochastic dynamic programming model is formulated to determine the optimal numbers of counters to open over the time window specified. A numerical example is provided to illustrate the model solution and gain managerial insights.queues, transient results, dynamic programming, applications, transportation, scheduling

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