Cargo Aircraft Capacity Optimization: A Hybrid Approach Comprising a Genetic Algorithm and Large Neighborhood Search

Abstract

Air transportation has accelerated international trade, and the efficient use of cargo aircraft capacity supports logistics operations, reduces expenses, and benefits the environment. In this study, we formulate a mathematical programming model to solve the cargo aircraft capacity optimization problem and propose simplified approaches for practical applications. We investigate Mixed-Integer Linear Programming (MILP), Genetic Algorithm (GA), and Large Neighborhood Search (LNS) techniques. MILP yields optimal solutions for small instances but cannot handle large-scale, real-world problems due to excessive computation time; therefore, we combine the GA and LNS. The GA provides acceptable solutions rapidly, and LNS refines them by exploring larger solution spaces. Thus, this hybrid approach leverages the GA's exploration capability and LNS's exploitation ability to produce high-quality solutions efficiently. Our experimental results show that the hybrid GA-LNS method outperforms the MILP and single approaches in terms of capacity usage, loading duration, and computational time. This study provides an applicable model with practical constraints and guidelines for air cargo and cost reduction, operational efficiency, and safety.