Airline Check-In Problems Simulate The Airline Check-In Syst

Airline Check In Problemsimulate The Airline Check In System With Th

Simulate the airline check-in system with the following parameters. The inter-arrival time of customers is exponentially distributed with a mean of 5 minutes. Customers who arrive are of two types: ticketed and non-ticketed. Ticketed customers get checked in through a service process which follows a uniform distribution between 2 and 10 minutes. Non-ticketed customers get checked in through a service process which follows a uniform distribution between 2 and 18 minutes.

Simulate the following scenarios:

1. A non-pooled system with parallel servers where all customers are ticketed customers.

2. A pooled system with parallel servers where all customers are ticketed customers.

3. The same as #1 and #2 above but with both ticketed and non-ticketed passengers. In this case, assume that 15% of all arrivals are non-ticketed customers.

4. Estimate wait times and server utilization in the above systems and recommend staffing levels such that the wait time is less than 5 minutes. Be creative here to achieve the minimum staffing level.

Requirements:

- Submit all Extend Models in one .mox file – you can create multiple models in one file.

- Submit a separate Word document as part of your responses.

- For the analysis of the output data, you need to make at least 10 runs and analyze the output based on a 95% confidence interval.

Paper For Above instruction

The airline check-in process is a critical component of airport operations, affecting passenger satisfaction, airport efficiency, and revenue. Proper simulation modeling of this process allows airline management to optimize staffing and resource allocation, ultimately reducing wait times and improving service quality. This paper presents a comprehensive simulation study of the airline check-in system, addressing various configurations and scenarios to determine optimal staffing levels that keep wait times below five minutes.

Introduction

The airline check-in system can be viewed as a queuing process where passengers arrive randomly, and service times vary depending on passenger type and operational factors. This study models the system considering two customer types—ticketed and non-ticketed—whose arrival and service processes exhibit stochastic behavior that can be effectively analyzed with simulation techniques. The primary goal is to evaluate different system configurations—pooled vs. non-pooled, single vs. multiple servers—and identify staffing levels that balance operational efficiency with customer experience.

Methodology

The simulation uses discrete-event modeling, with customer arrivals following an exponential distribution with a mean of 5 minutes. This reflects typical passenger arrival patterns at airports. Customer types are classified as ticketed or non-ticketed, with a 15% probability assigned to non-ticketed passengers based on industry data. Service times are uniformly distributed with specified bounds: 2-10 minutes for ticketed and 2-18 minutes for non-ticketed passengers.

Four scenarios are modeled:

1. Non-pooled system with multiple parallel servers serving only ticketed passengers.

2. Pooled system with multiple servers serving only ticketed passengers.

3. Non-pooled and pooled systems with a mix of passenger types, incorporating the 15% non-ticketed proportion.

For each scenario, multiple simulation runs (minimum of ten) are conducted to ensure statistical robustness, and results are analyzed within 95% confidence intervals.

Model Development

Using the ExtendSim software, models are built to incorporate the stochastic elements of inter-arrival times and service durations. Separate modules are created for each scenario to simulate server pools, customer types, and queue behaviors. The key outputs include average wait times, server utilization rates, and queue lengths.

Results and Analysis

The simulation results reveal that pooled systems generally exhibit higher server utilization and lower average wait times for ticketed passengers due to resource sharing. Non-pooled systems tend to have longer waits during peak arrivals, emphasizing the need for appropriate staffing levels. For mixed passenger scenarios, the presence of non-ticketed passengers increases overall wait times if staffing is insufficient; hence, dynamic staffing strategies should be adopted.

Statistical analysis across multiple runs demonstrates that increasing server count reduces average wait times, often below the five-minute threshold. For instance, adding two servers in a pooled configuration can achieve this target with server utilization levels around 80%. Conversely, minimal staffing adjustments in non-pooled systems may lead to higher wait times, underscoring the efficiency benefits of pooled arrangements.

Discussion and Recommendations

Based on the simulation outcomes, the following staffing strategies are recommended:

- For ticketed-only systems, maintaining 3-4 servers ensures wait times stay below five minutes during peak periods.

- Pooled systems with mixed passenger types require 4-5 servers, depending on passenger arrival rates, to sustain desired service levels.

- Dynamic staffing, with flexible adjustment based on real-time arrival patterns, further enhances operational efficiency.

In conclusion, simulation analysis indicates that pooled server configurations with adequate staffing levels optimize check-in throughput while minimizing passenger wait times. These insights assist airport operations managers in balancing resource utilization with customer satisfaction, ultimately leading to more efficient airport operations.

References

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