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July 21, 2026International Journal of Computational Intelligence SystemsOpen Access

Hybrid Evolutionary-Heuristic Framework for Multi-Objective 3D Container Loading Problem

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Authors

NYNdikuriyo YvesDFDung Davou FombYZYinggui Zhang

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Overview

Randomized trial demonstrates improved packing efficiency in container loading, indicating enhanced logistics optimization.

Key Points

  • This research aims to improve how cargo is packed into containers, focusing on efficiency and resource use.
  • Proposed a hybrid framework separating loading sequence optimization and geometric feasibility.
  • Integrated NSGA-II for sequence evolution and an enhanced DBLF heuristic for space-aware decoding.
  • Conducted experiments on benchmark and synthetic instances to assess performance using Pareto-based metrics.
  • Achieved space utilization above 87% in heterogeneous conditions, outperforming competing algorithms.
  • Showed a 7-10% improvement in space utilization with E-DBLF compared to standard DBLF.
  • Demonstrated well-distributed Pareto fronts, indicating robust optimization capabilities.

Cite This Study

Yves et al. (2026) studied this question.

synapsesocial.com/papers/6a5f0b6586a4235cc16191d1https://doi.org/10.1007/s44196-026-01471-0
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