Intelligent decision support system for multi-objective 3D container loading using genetic algorithm combined with artificial bee colony

Efficient container loading is a complex and critical logistics challenge, especially when dealing with strongly heterogeneous boxes in three dimensions. This study proposes an intelligent decision support system that addresses the 3D Single Container Loading Problem (3D-SCLP) using a hybrid meta-heuristic approach combining Genetic Algorithm (GA) and Artificial Bee Colony (ABC). The system introduces rotation constraints as a decision variable and optimizes for two objectives: maximizing profit and minimizing unused space. A mathematical model based on the bottom-left fill (BLF) method was developed to ensure feasible loading with non-overlapping placements and valid rotations. Experimental results on 15 real-world and 225 synthetic test cases demonstrate the superiority of the proposed GA+ABC method over standalone algorithms in both solution quality and robustness. The system achieves the lowest hypervolume metric (119.28), indicating better convergence to Pareto-optimal fronts, and provides practical feasibility for real-world logistics optimization.