A simulation-based optimization workforce allocation method to improve airport security screening operations

Abstract

Abstract

Efficiently allocating transportation security officers in airport security screening checkpoints is critical, yet complicated by fluctuating passenger arrivals, diverse passenger types, and the operational complexities of multiple checkpoints. This research tackles the challenge of suboptimal resource allocation by developing and validating a Simio-OptQuest-Guided Heuristic method. Unlike conventional approaches, Simio-OptQuest-Guided Heuristic is specifically designed to navigate the large decision space inherent in scheduling transportation security officers across two distinct security screening checkpoints considering six heterogeneous passenger categories. An extensive computational study, using validated simulation models based on Phoenix Sky Harbor Airport operations, confirms the efficacy of Simio-OptQuest-Guided Heuristic. Results show our method can achieve near-optimal performance, comparable to a theoretical “full-capacity” scenario, while utilizing up to 30% fewer transportation security officer hours, highlighting its significant economic advantages. This work offers a robust, experimentally validated methodology for strategic transportation security officer allocation, demonstrating substantial improvements in operational efficiency and passenger service.