基于改进人工蜂群算法的爆炸品集装箱配载优化

doi:10.16097/j.cnki.1009-6744.2026.05.021

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (5): 248-259.DOI: 10.16097/j.cnki.1009-6744.2026.05.021

• 智能交通系统与信息技术 • 上一篇下一篇

基于改进人工蜂群算法的爆炸品集装箱配载优化

任鸿翔^*1，钟鑫¹，李业²

1. 大连海事大学，航海学院，辽宁大连116026；2.大连港引航站，辽宁大连116026

收稿日期:2026-05-04 修回日期:2026-07-01 接受日期:2026-07-08 出版日期:2026-10-25 发布日期:2026-10-21
作者简介:任鸿翔（1974— ），男，黑龙江肇东人，教授，博士。
基金资助:
国家自然科学基金 (52571404)；广西科技重大专项(桂科AA23062053)。

Optimization of Explosives Container Stowage Plan Based on Improved Artificial Bee Colony Algorithm

REN Hongxiang^*1, ZHONG Xin¹, LI Ye²

1. School of Navigation, Dalian Maritime University, Dalian 116026, Liaoning, China; 2. Dalian Pilot Station, Dalian 116026, Liaoning, China

Received:2026-05-04 Revised:2026-07-01 Accepted:2026-07-08 Online:2026-10-25 Published:2026-10-21
Supported by:
Nationa lNatural Science Foundation of China (52571404)；Guangxi Science and Technology Major Project (桂科AA23062053)。

摘要/Abstract

摘要： 科学合理的配载方案是保障海上危险品集装箱运输安全的前提和基础。以集装箱船吃水差、初稳性高度和倒箱次数为优化目标，依据《国际海运危险货物规则》中第1类爆炸品积载与隔离要求，构建同兼容性组别爆炸品混合积载多目标优化模型，并提出基于适应度分割搜索策略的粒子蜂群算法。该算法融合人工蜂群算法的全局搜索能力与粒子群算法的局部寻优能力，通过适应度分割搜索和粒子蜂更新与异常解处理机制，提高可行解筛选效率和收敛精度。多港口仿真实验结果表明：在满足爆炸品积载、隔离和船舶安全约束的前提下，所提算法较对比算法获得更高适应度，计算耗时缩短8%~16%，倒箱量减少20.0%~38.5%，体现了算法的高效性和针对性，可为爆炸品集装箱安全高效配载提供理论参考。

关键词: 水路运输, 集装箱配载优化, 改进人工蜂群算法, 爆炸品集装箱, 启发式规则

Abstract: A scientifically sound and rational stowage plan is the prerequisite and foundation for ensuring the safety of maritime dangerous goods container transport. Taking the draft difference, initial stability height, and number of container shifts as optimization objectives, this study constructs a multi-objective optimization model for mixed stowage of explosives within compatible groups in accordance with the stowage and segregation requirements for Class 1 explosives specified in the International Maritime Dangerous Goods Code. A Particle Bee Colony algorithm is then proposed based on a fitness-segmentation search strategy. The algorithm integrates the global search capability of the Artificial Bee Colony algorithm with the local optimization capability of Particle Swarm Optimization, and improves feasible-solution screening efficiency and convergence accuracy through fitness-segmentation search, particle-bee updating, and infeasible-solution handling mechanisms. Multi-port simulation experiments show that, under the premise of satisfying explosive stowage, segregation, and ship safety constraints, the proposed algorithm achieves higher fitness than the comparison algorithms. It reduces computational time by 8%~16%, and decreases the number of container shifts by 20.0%~38.5%. The results demonstrate the efficiency and applicability of the proposed algorithm, and provide a reference for safe and efficient explosives container stowage.

Key words: waterway transportation, container stowage optimization, improved artificial bee colony algorithm, explosives container, heuristic rules

中图分类号:

U695.2+2

任鸿翔, 钟鑫, 李业. 基于改进人工蜂群算法的爆炸品集装箱配载优化[J]. 交通运输系统工程与信息, 2026, 26(5): 248-259.

REN Hongxiang, ZHONG Xin, LI Ye. Optimization of Explosives Container Stowage Plan Based on Improved Artificial Bee Colony Algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(5): 248-259.

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http://www.tseit.org.cn/CN/Y2026/V26/I5/248

参考文献

[1]张启堂,任鸿翔,杨晓,等.舰船专用舱室危险品的三维装箱问题研究与优化[J]. 中国航海,2025, 48(S1): 146-154. [ZHANG Q T, REN H X, YANG X, et al. Research and optimization of the three-dimensional container packing problem for dangerous goods in special-purpose ship holds[J]. Navigation of China, 2025, 48(Sup. 1): 146-154.]

[2] 常祎妹.集装箱港口船舶配载计划研究综述[J].交通运输工程学报,2023, 23(5): 85-103. [CHANG Y M. Review on research of containership stowage planning of container terminals[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 85-103.]

[3]TWILLER V J, SIVERTSEN A, PACINO D, et al. Literature survey on the container stowage planning problem[J]. European Journal of Operational Research, 2024, 317(3): 841-857.

[4] 李俊,张煜,计三有,等.多箱型内河集装箱船舶配载决策研究[J]. 交通运输系统工程与信息,2019,19(1): 200-207. [LI J, ZHANG Y, JI S Y, et al. Inland container ship stowage planning decision with multiple container types[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(1): 200-207.]

[5]ZHOU S, GUO Z, CHEN J, et al. Large containership stowage planning for maritime logistics: A novel meta heuristic algorithm to reduce the number of shifts[J]. Advanced Engineering Informatics, 2025, 64: 102962 102977.

[6]WU J, PANG H, JIANG S, et al. A model for container stowage planning considering stability constraints[J]. Journal of the Operational Research Society, 2025, 76(8): 1612-1629.

[7]刘志雄,钱翰文,颜家岚. 集装箱码头船舶多贝位配载与堆场取箱协同优化研究[J].系统仿真学报, 2021, 33(7): 1689-1698. [LIU Z X, QIAN H W, YAN J L. Collaborative optimization of ship stowage plan and yard container unloading for container terminal[J]. Journal of System Simulation, 2021, 33(7): 1689-1698.]

[8]宋丽英,邓琨琦,宁武,等.进口集装箱堆存决策的两阶段强化学习方法[J]. 交通运输系统工程与信息, 2026, 26(1): 283-294. [SONG L Y, DENG K Q, NING W, et al. Two-stage reinforcement learning method for stacking decisions of import container[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(1): 283-294.]

[9] WANG Y, HE J, YU H, et al. Optimization of yard remarshalling operations in automated container terminals[J]. Advanced Engineering Informatics, 2024, 61: 102552-102566.

[10] BEIZHEN J, KJETIL F. Step-wise stowage planning of roll-on roll-off ships transporting dangerous goods[J]. Maritime Transport Research, 2021, 2: 49-70.

[11] AMBROSINO D, SCIOMACHEN A. A shipping line stowage-planning procedure in the presence of hazardous containers[J]. Maritime Economics & Logistics, 2021, 23 (1): 49-70.

[12] BILICAN S M, KARATAS M, ZHENG J Y, et al. A survey of shipping line container stowage planning problems[J]. Expert Systems with Applications, 2024, 255 (PA): 124408-124429.

[13] 王继超,李擎,崔家瑞,等.一种改进的人工蜂群算法: 粒子蜂群算法[J]. 工程科学学报, 2018, 40(7): 871 881. [WANG J C, LI Q, CUI J R, et al. An improved artificial bee colony algorithm: Particle bee colony[J]. Chinese Journal of Engineering, 2018, 40(7): 871-881.]

[14] 曹阳, 沈世杰.基于适应度分割机制和自适应搜索策略的人工蜂群算法[J].计算机应用,2025,45(S1):170 176. [CAO Y, SHEN S J. Artificial bee colony algorithm based on fitness segmentation mechanism and adaptive search strategy[J]. Journal of Computer Applications, 2025, 45(S1): 170-176.]

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基于改进人工蜂群算法的爆炸品集装箱配载优化

Optimization of Explosives Container Stowage Plan Based on Improved Artificial Bee Colony Algorithm

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