Optimizing and simulating dynamic check-in counter opening with passenger allocation considerations

Abstract

With the increasing adoption of self-service bag drop facilities, modern airports necessitate check-in counter optimization models that strategically allocate passengers between staffed and self-service facilities while balancing operational costs and service quality. This study develops a two-tiered decision framework integrating dynamic integer programming and discrete-event simulation (DES) to minimize manual counter-staffing costs while guaranteeing service level agreements. We first formulate an integer programming model that allocates passengers to manual or self-service channels based on passengers’ profiles. To address queue overflow during peak hours, a dynamic capacity scaling factor is introduced to the integer programming model. The DES module then iteratively validates queueing dynamics and feeds back queueing time to optimization layer, triggering increase in the capacity scaling factor and reallocations when predicted wait times exceed 15-min thresholds. Applied to Baiyun Airport Terminal 2, the model results show a 9.2% increase in operational cost (from 8824.5 to 9633.75) but a 30.9% reduction in average manual check-in wait time (from 8.1 to 5.6 min), reducing peak-period congestion. This study provides decision support for optimizing check-in counter operations at airports.