CLP Approach to 2D Angle Placement

From Simple Sci Wiki
Revision as of 02:59, 24 December 2023 by SatoshiNakamoto (talk | contribs) (Created page with "Title: CLP Approach to 2D Angle Placement Research Question: How can the CLP (Constraint Logic Programming) approach be applied to solve the 2D angle placement problem more efficiently? Methodology: The researchers proposed two CLP approaches to 2D angle placement: the classical (rectangular) cumulative global constraint and the new trapezoidal cumulative global constraint. These approaches were applied to a specific problem of packing high-current enclosed conductors...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search

Title: CLP Approach to 2D Angle Placement

Research Question: How can the CLP (Constraint Logic Programming) approach be applied to solve the 2D angle placement problem more efficiently?

Methodology: The researchers proposed two CLP approaches to 2D angle placement: the classical (rectangular) cumulative global constraint and the new trapezoidal cumulative global constraint. These approaches were applied to a specific problem of packing high-current enclosed conductors and high-current enclosed bus bars into long-load trailers.

Results: The study found that both CLP approaches were successful in solving the 2D angle placement problem. The classical cumulative approach divided angles into rectangles and used the gen_rect/4 predicate to generate the rectangles. The trapezoidal cumulative approach, on the other hand, did not require additional constraints as it used the standard cumulative_trapeze predicate embedded in CHIP.

Implications: The results suggest that the CLP approach can be an effective method for solving complex packing problems. The classical cumulative approach may be more suitable for problems with fixed angle orientations, while the trapezoidal cumulative approach can handle problems without rotation. This study provides valuable insights into the application of CLP for solving real-world problems and may inspire further research in this area.

Link to Article: https://arxiv.org/abs/0109066v1 Authors: arXiv ID: 0109066v1