A Logistics Loading Optimization Approach Considering Cargo Density Differences

DOI

10.2991/978-94-6239-758-3_58How to use a DOI?

Keywords

Logistics Optimization; Vector Bin Packing; Density Matching; Heuristic Algorithm

Abstract

In logistics loading, the heterogeneity of cargo items in weight and volume leads to inefficient resource utilization. This paper addresses a bin packing problem with dual-capacity constraints. A scalarized single-objective model is proposed to minimize container usage and reduce resource utilization imbalance. An Adaptive Density Matching Heuristic (ADMH) is developed, which categorizes cargo by density deviation and dynamically adjusts the loading strategy based on container capacity and residual space utilization. The proposed method integrates adaptive decision rules to enhance loading efficiency across different demand structures. Numerical experiments show that ADMH improves resource utilization in balanced scenarios and maintains stability under unbalanced conditions, while consistently achieving competitive performance compared with baseline methods. These results demonstrate its effectiveness and robustness for practical less-than-truckload (LTL) operations.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Zhanxu Zhu
PY  - 2026
DA  - 2026/09/08
TI  - A Logistics Loading Optimization Approach Considering Cargo Density Differences
BT  - Proceedings of the 2026 7th International Conference on Management Science and Engineering Management (ICMSEM 2026)
PB  - Atlantis Press
SP  - 590
EP  - 596
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-758-3_58
DO  - 10.2991/978-94-6239-758-3_58
ID  - Zhu2026
ER  -