Bi-Level Optimization for De-Icing Position Allocation and Unmanned De-Icing Vehicle Fleet Routing Problem

Area of Science:

• Aviation Operations Research
• Logistics and Supply Chain Management
• Artificial Intelligence in Transportation

Background:

• Aircraft icing poses significant risks, leading to flight delays and safety concerns.
• Current de-icing strategies at medium to large airports require optimization for dynamic flight schedules.
• Inefficiencies in de-icing allocation and scheduling impact overall airport operational performance.

Purpose of the Study:

• To develop a centralized de-icing methodology using unmanned de-icing vehicles.
• To minimize flight delay times through optimized de-icing allocation and scheduling.
• To enhance overall airport de-icing efficiency.

Main Methods:

• Formulation of a mixed-integer bi-level programming model for allocation and scheduling.
• Development of a two-stage algorithm: Mixed Variable Neighborhood Search Genetic Algorithm (MVNS-GA) and Multi-Strategy Enhanced Heuristic Greedy Algorithm (MSEH-GA).
• Simulation and empirical validation at a major Chinese airport.

Main Results:

• The proposed algorithms (MVNS-GA and MSEH-GA) demonstrate effectiveness and competitiveness in horizontal comparisons.
• The centralized methodology successfully optimizes the allocation and collaborative scheduling of multiple unmanned de-icing vehicles.
• Model simulations confirm significant mitigation of flight delays and improvements in de-icing operations.

Conclusions:

• The research provides a practical and efficient solution for aircraft de-icing at medium to large airports.
• The integration of unmanned de-icing vehicles and optimized scheduling enhances airport resilience and operational performance.
• This approach offers a pathway to more reliable air travel during icing conditions.