• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于启发式算法的滚装船车辆配载

Heuristic-based vehicle arrangement for ro-ro ships.

作者信息

Zhai Mingyuan, Jin Zhongyuan, Yan Zelin, Gu Zhengmin, Li Zhenni, Xiao Dong

机构信息

Information Science and Engineering School, Northeastern University, Shenyang, 110819, Liaoning, China.

Flight Control Department, Shenyang Aircraft Design and Research Institute, Shenyang, 110035, Liaoning, China.

出版信息

Sci Rep. 2024 Dec 28;14(1):30889. doi: 10.1038/s41598-024-81234-z.

DOI:10.1038/s41598-024-81234-z
PMID:39730516
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11681190/
Abstract

In this paper, a two-level search strategy fused with an improved no-fit polygon algorithm and improved bat algorithm is proposed to obtain the layout points of multiple vehicles. Additionally, a space-time scheduling strategy is proposed using the Improved DLite Algorithm (IDLite) and improved Bezier curve to generate the trajectories of individual vehicles. Furthermore, a conflict resolution strategy is introduced to address the collision conflict problem during multi-vehicle scheduling. The proposed strategies aim to achieve the objectives of shortest scheduling time and smallest array space in order to reduce the time and space cost of vehicle loading on ro-ro carriers. Through a comparison with other algorithms (e.g., at high loading pressure, Scenario 3), the strategies presented in this paper demonstrate advantages such as higher deck utilization (92.234%), shorter path length, fewer collision conflicts, and more balanced weighting. Mainstream methods only focus on a certain part of vehicle transportation, we not only use more stable parking spot generation algorithm and parking order generation algorithm, but also more high-speed multi-vehicle path-finding algorithm, path optimization strategy, multi-vehicle scheduling strategy, which innovatively form a complete vehicle planning process, and at the same time, as an algorithmic framework that can be generalized to any 2d or 3d scenarios.

摘要

本文提出了一种融合改进的无拟合多边形算法和改进的蝙蝠算法的两级搜索策略,以获取多辆车的布局点。此外,还提出了一种时空调度策略,利用改进的DLite算法(IDLite)和改进的贝塞尔曲线来生成单车的轨迹。此外,还引入了一种冲突解决策略,以解决多车调度过程中的碰撞冲突问题。所提出的策略旨在实现调度时间最短和阵列空间最小的目标,以降低滚装船上车辆装载的时间和空间成本。通过与其他算法进行比较(例如,在高装载压力下的场景3),本文提出的策略具有更高的甲板利用率(92.234%)、更短的路径长度、更少的碰撞冲突和更平衡的权重等优点。主流方法仅关注车辆运输的某一部分,我们不仅使用了更稳定的停车位生成算法和停车顺序生成算法,还使用了更高速的多车路径查找算法、路径优化策略、多车调度策略,创新性地形成了一个完整的车辆规划过程,同时,作为一个可以推广到任何二维或三维场景的算法框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/050826c0abd9/41598_2024_81234_Fig23_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/e5b0e62b571c/41598_2024_81234_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/c0dc191a8466/41598_2024_81234_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/50650bcb3e75/41598_2024_81234_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/3819fa09644a/41598_2024_81234_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/66044c5e6e77/41598_2024_81234_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/e6ac59677072/41598_2024_81234_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/628bb937f497/41598_2024_81234_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/1a0562f5cf6b/41598_2024_81234_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/22c66a026c6a/41598_2024_81234_Figb_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/a2c6bf594651/41598_2024_81234_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/18e5e74fda35/41598_2024_81234_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/2f10619ad226/41598_2024_81234_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/961c89efaeca/41598_2024_81234_Figc_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/702cb2420be8/41598_2024_81234_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/be9fcfb0c7b9/41598_2024_81234_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/cc62092fb11b/41598_2024_81234_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/ee577d0a380e/41598_2024_81234_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/de0371060cb2/41598_2024_81234_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/a6da72ddae76/41598_2024_81234_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/beadb771b9ab/41598_2024_81234_Fig17_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/c15fb8a424fb/41598_2024_81234_Fig18_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/4c929dcec0ab/41598_2024_81234_Fig19_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/5ccc839eb9de/41598_2024_81234_Fig20_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/e4b9f823bbac/41598_2024_81234_Fig21_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/f4946a653f39/41598_2024_81234_Fig22_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/050826c0abd9/41598_2024_81234_Fig23_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/e5b0e62b571c/41598_2024_81234_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/c0dc191a8466/41598_2024_81234_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/50650bcb3e75/41598_2024_81234_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/3819fa09644a/41598_2024_81234_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/66044c5e6e77/41598_2024_81234_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/e6ac59677072/41598_2024_81234_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/628bb937f497/41598_2024_81234_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/1a0562f5cf6b/41598_2024_81234_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/22c66a026c6a/41598_2024_81234_Figb_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/a2c6bf594651/41598_2024_81234_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/18e5e74fda35/41598_2024_81234_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/2f10619ad226/41598_2024_81234_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/961c89efaeca/41598_2024_81234_Figc_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/702cb2420be8/41598_2024_81234_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/be9fcfb0c7b9/41598_2024_81234_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/cc62092fb11b/41598_2024_81234_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/ee577d0a380e/41598_2024_81234_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/de0371060cb2/41598_2024_81234_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/a6da72ddae76/41598_2024_81234_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/beadb771b9ab/41598_2024_81234_Fig17_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/c15fb8a424fb/41598_2024_81234_Fig18_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/4c929dcec0ab/41598_2024_81234_Fig19_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/5ccc839eb9de/41598_2024_81234_Fig20_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/e4b9f823bbac/41598_2024_81234_Fig21_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/f4946a653f39/41598_2024_81234_Fig22_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bce/11681190/050826c0abd9/41598_2024_81234_Fig23_HTML.jpg

相似文献

1
Heuristic-based vehicle arrangement for ro-ro ships.基于启发式算法的滚装船车辆配载
Sci Rep. 2024 Dec 28;14(1):30889. doi: 10.1038/s41598-024-81234-z.
2
A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment.一种用于未知障碍物环境下的无人巡航船的混合多目标路径规划算法。
Sensors (Basel). 2022 Mar 22;22(7):2429. doi: 10.3390/s22072429.
3
Implementation of two-roller scheduling path planning under road construction scenarios.道路施工场景下双滚筒调度路径规划的实现
Sci Rep. 2025 Feb 25;15(1):6767. doi: 10.1038/s41598-025-91107-8.
4
Hybrid Residual Multiexpert Reinforcement Learning for Spatial Scheduling of High-Density Parking Lots.用于高密度停车场空间调度的混合残差多专家强化学习
IEEE Trans Cybern. 2024 May;54(5):2771-2783. doi: 10.1109/TCYB.2023.3312647. Epub 2024 Apr 16.
5
Design of low-carbon planning model for vehicle path based on adaptive multi-strategy ant colony optimization algorithm.基于自适应多策略蚁群优化算法的车辆路径低碳规划模型设计
PeerJ Comput Sci. 2025 Jan 29;11:e2611. doi: 10.7717/peerj-cs.2611. eCollection 2025.
6
A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.一种基于动态遗传算法与蚁群二进制迭代优化的多茶园无人机喷施调度路径规划算法
Front Plant Sci. 2022 Sep 16;13:998962. doi: 10.3389/fpls.2022.998962. eCollection 2022.
7
UAV Path Planning Algorithm Based on Improved Harris Hawks Optimization.基于改进哈里斯鹰优化算法的无人机路径规划
Sensors (Basel). 2022 Jul 13;22(14):5232. doi: 10.3390/s22145232.
8
Optimization of heterogeneous vehicle logistics scheduling with multi-objectives and multi-centers.多目标多中心异构车辆物流调度优化
Sci Rep. 2023 Aug 29;13(1):14169. doi: 10.1038/s41598-023-41450-5.
9
Multi-strategy cooperative scheduling for airport specialized vehicles based on digital twins.基于数字孪生的机场特种车辆多策略协同调度
Sci Rep. 2024 Jul 5;14(1):15533. doi: 10.1038/s41598-024-66350-0.
10
Optimization of scheduling scheme for self-driving vehicles by simulation algorithm.基于仿真算法的自动驾驶车辆调度方案优化
Sci Prog. 2023 Jul-Sep;106(3):368504231188617. doi: 10.1177/00368504231188617.

本文引用的文献

1
Microrobot Path Planning Based on the Multi-Module DWA Method in Crossing Dense Obstacle Scenario.基于多模块DWA方法的微机器人在穿越密集障碍物场景中的路径规划
Micromachines (Basel). 2023 May 31;14(6):1181. doi: 10.3390/mi14061181.
2
Smart Vehicle Path Planning Based on Modified PRM Algorithm.基于改进 PRM 算法的智能车辆路径规划。
Sensors (Basel). 2022 Aug 31;22(17):6581. doi: 10.3390/s22176581.