# Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools
## 2 Conceptual Framework and Design Principles of the Simulation Model
* The model is not calibrated to simulate any specific airport but rather serves as a generalized framework that can be adapted to diverse operational contexts. These assumptions and limitations define the modeling boundaries and clarify the scope of the presented approach. They support the objective to demonstrate how the core stages of the passenger handling process can be effectively simulated, observed, and optimized using a transparent, spreadsheet-based method. More advanced models may build upon this foundation by incorporating additional behavioral rules, real-world datasets, or complex decision-making logic, all of which can be implemented within Excel or expanded into specialized simulation tools. While Microsoft Excel and similar spreadsheet tools offer convenience and flexibility for data handling, their widespread use also raises serious cybersecurity concerns. Given the sensitivity of the data often stored in Excel, it is essential to implement strong security measures. This includes preventive measures like strong passwords and encryption, two-factor authentication (2 FA), controlled user permissions, secure macro handling, regular data backups, and active audit trails. To illustrate the basic decision-making logic and process flow of the simulation, a simplified flowchart is provided (Figure 1). It outlines the sequential movement of passengers through key processing points from arrival at the airport, through check-in and security screening, to entry into the departure hall. While this diagram does not capture computational variables or simulation parameters, it serves to clarify the modeled logic behind passenger routing and resource assignment. A detailed description of phase configurations, service capacities, and implementation logic follows. The simulation model presented in this paper is built around a representative configuration of airport passenger handling. The core structure includes two staffed check-in desks, with one queue and four security scanners, each operating independently with its own queue. This configuration was selected to reflect a mid-sized terminal setting and to enable the comparison of passenger flow and queuing dynamics under varying system loads. The process begins with the arrival of passengers at the airport. Passengers do not enter the system all at once but arrive over time, following a normal distribution that simulates real-world arrival variability. Each passenger is assigned an individual arrival time based on this distribution, incorporating natural randomness (e.g., early or late arrival relative to peak time). Figure 1: Algorithm schema of the sample handling process simulation model. Once a passenger arrives, they proceed immediately toward the check-in area. The walking time from arrival to check-in is also modeled using a normal distribution, with parameters (mean and standard deviation) based on a typical terminal layout and assumptions about walking speed. This allows the simulation to account for variability in terminal navigation while preserving simplicity--for instance, faster or slower passengers, different entry points, or architectural constraints. Upon reaching the check-in area, the system evaluates the availability of the two staffed desks. If one or both desks are free, the passenger is assigned to the first available one and begins service immediately. If both desks are occupied, the passenger enters a common queue and waits until the next desk becomes available. The specific logic used to assign passengers to the two check-in desks from a shared queue is illustrated in Figure 2, accompanied by a detailed explanation. Passengers are always served in the order of arrival, following a FIFO (first-in, first-out) discipline. The duration of each check-in process is generated using an exponential distribution, reflecting stochastic service behavior, i.e., some passengers are processed more quickly due to luggage type, staff efficiency, or documentation readiness, while others take longer.