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Related Experiment Video

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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Heuristic-based vehicle arrangement for ro-ro ships.

Mingyuan Zhai1,2, Zhongyuan Jin1, Zelin Yan1

  • 1Information Science and Engineering School, Northeastern University, Shenyang, 110819, Liaoning, China.

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Summary

This study introduces a novel multi-vehicle planning system for efficient ro-ro carrier loading. It optimizes vehicle layout and trajectories, reducing costs and preventing collisions.

Keywords:
Bat algorithm (BA)Dynamic star Lite (D* Lite)No-fit polygon (NFP)Vehicle location

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Area of Science:

  • Logistics and Supply Chain Management
  • Operations Research
  • Artificial Intelligence

Background:

  • Efficient loading of roll-on/roll-off (ro-ro) carriers is crucial for reducing transportation costs.
  • Existing methods often address only specific aspects of vehicle transportation, leading to suboptimal overall efficiency.
  • Collision conflicts and inefficient space utilization are significant challenges in multi-vehicle scheduling.

Purpose of the Study:

  • To develop an integrated strategy for multi-vehicle layout and trajectory planning on ro-ro carriers.
  • To minimize scheduling time and spatial requirements for vehicle loading.
  • To create a generalized algorithmic framework applicable to various 2D and 3D scenarios.

Main Methods:

  • A two-level search strategy combining an improved no-fit polygon algorithm and an improved bat algorithm for vehicle layout.
  • A space-time scheduling strategy utilizing the Improved D*Lite Algorithm (ID*Lite) and improved Bezier curves for trajectory generation.
  • A conflict resolution strategy to mitigate collisions during multi-vehicle scheduling.

Main Results:

  • Achieved higher deck utilization (92.234%) compared to mainstream methods, particularly under high loading pressure.
  • Demonstrated shorter path lengths and fewer collision conflicts in simulations.
  • Successfully integrated parking spot generation, parking order generation, path-finding, path optimization, and scheduling into a complete planning process.

Conclusions:

  • The proposed integrated strategies significantly reduce time and space costs for vehicle loading on ro-ro carriers.
  • The developed algorithmic framework offers a comprehensive solution for multi-vehicle planning, adaptable to diverse environments.
  • This approach enhances overall efficiency and safety in complex vehicle transportation logistics.