Simulation-based Selectee Lane queueing design for passenger checkpoint screening

Xiaofeng Nie

doi:10.1016/j.ejor.2011.12.012

Return

Simulation-based Selectee Lane queueing design for passenger checkpoint screening

2012-05-01

PRE

DOI：10.1016/j.ejor.2011.12.012

Original

Original request for help

Save

Abstract

En 中文

There are two kinds of passenger checkpoint screening lanes in a typical US airport: a Normal Lane and a Selectee Lane that has enhanced scrutiny. The Selectee Lane is not effectively utilized in some airports due to the small amount of passengers selected to go through it. In this paper, we propose a simulation-based Selectee Lane queueing design framework to study how to effectively utilize the Selectee Lane resource. We assume that passengers are classified into several risk classes via some passenger pre-screening system. We consider how to assign passengers from different risk classes to the Selectee Lane based on how many passengers are already in the Selectee Lane. The main objective is to maximize the screening system's probability of true alarm. We first discuss a steady-state model, formulate it as a nonlinear binary integer program, and propose a rule-based heuristic. Then, a simulation framework is constructed and a neighborhood search procedure is proposed to generate possible solutions based on the heuristic solution of the steady-state model. Using the passenger arrival patterns from a medium-size airport, we conduct a detailed case study. We observe that the heuristic solution from the steady-state model results in more than 4% relative increase in probability of true alarm with respect to the current practice. Moreover, starting from the heuristic solution, we obtain even better solutions in terms of both probability of true alarm and expected time in system via a neighborhood search procedure. (C) 2011 Elsevier B.V All rights reserved.

Keywords:

Passenger checkpoint screening

Selectee Lane

Queueing design

Nonlinear binary integer programming

Simulation

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.