Client: NDA

Optimizing Airport Stand Assignment for Efficiency and Business Impact

Case study details

Airports face constant challenges in planning and assigning aircraft to stands (the equivalent of gates), and balancing operational efficiency with business objectives. This case study examines the development and implementation of a stand optimization system designed to address these challenges by integrating rule-based constraints with business value-driven AI decision-making.

Challenge

Resource Allocation for Revenue Optimization: Identifying and prioritizing high-value stands to increase traffic through areas with greater business potential.
Passenger Connectivity: Ensuring smooth and timely transitions for passengers with connecting flights, minimizing delays and disruptions.
Sustainability Goals: Reducing environmental impact by optimizing stand placement to minimize aircraft travel distances and fuel consumption

Approach

The solution employed a dual-layered methodology:

Rule-Based Framework: Adhering to predefined operational constraints, including safety, aircraft type compatibility, and regulatory requirements.
Business-Oriented Optimization: Incorporating business KPIs such as revenue potential and passenger flow data to inform dynamic decision-making.

Goal

The primary goal was to enhance airport operations through a sophisticated tool that enabled efficient aircraft assignment to stands. By combining established operational rules with business key performance indicators (KPIs), the system aimed to maximize revenue generation, improve passenger experience, and support environmental sustainability.

Outcome

The implementation of the optimization system led to measurable improvements in key areas:

Operational Efficiency: Reduced aircraft travel distances and turnaround times.
Revenue Growth: Increased utilization of high-value stands.
Enhanced Passenger Experience: Improved on-time performance and smoother connectivity.
Sustainability Benefits: Decreased fuel consumption and carbon emissions through smarter stand placement.

Approach

Data-Driven Insights for Strategic Stand Optimization

Historical data analysis played a pivotal role, enabling the system to identify patterns in passenger movement, high-revenue zones, and operational bottlenecks. By combining these insights with real-time operational inputs, the tool provided strategic recommendations for stand assignments that balanced efficiency and profitability.

Outcome

AI solutions for airport

This case study demonstrates the potential of combining rule-based systems with business intelligence to create impactful optimization tools. By addressing complex operational challenges and aligning decisions with business goals, the stand optimization system not only streamlined airport operations but also delivered tangible business and environmental benefits.

Before

Inefficient stand assignments, causing delays and disruptions.
Missed revenue opportunities from underutilized high-value stands.
High fuel consumption and carbon emissions.

After

Improved efficiency with reduced turnaround times and smoother connectivity.
Increased revenue from high-value stand utilization.
Lower fuel usage and emissions through optimized assignments.

About Addepto

Addepto, a growing company developing innovative data-based and AI-related solutions, supports increasing efficiency with intelligent computer vision system.

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