Decoupling Geometry from Optimization in 2D Irregular Cutting and Packing Problems: an Open-Source Collision Detection Engine

2025-08-11

AI Summary

The two-dimensional irregular cutting and packing (C&P) problem suffers from tight coupling between geometric feasibility checking and optimization, hindering algorithmic innovation and reproducibility. Method: This paper introduces a geometry–optimization decoupling paradigm and presents *jagua-rs*, the first open-source, domain-specific collision detection engine for irregular C&P. Built in Rust, it leverages exact polygonal modeling and computational geometry theory to deliver high performance and robustness, supporting arbitrary rotations, translations, and complex boundary interactions. Geometric feasibility verification is fully abstracted into standardized, language-agnostic APIs. Contribution/Results: jagua-rs decouples geometric reasoning from optimization logic, enabling researchers to focus exclusively on algorithm design without implementing low-level geometric primitives. It significantly lowers the barrier to entry for irregular C&P research and provides a unified, reliable geometric foundation for heuristic, metaheuristic, and learning-based approaches.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Addressing irregular cutting and packing (C&P) optimization problems poses two distinct challenges: the geometric challenge of determining whether or not an item can be placed feasibly at a certain position, and the optimization challenge of finding a good solution according to some objective function. Until now, those tackling such problems have had to address both challenges simultaneously, requiring two distinct sets of expertise and a lot of research & development effort. One way to lower this barrier is to decouple the two challenges. In this paper we introduce a powerful collision detection engine (CDE) for 2D irregular C&P problems which assumes full responsibility for the geometric challenge. The CDE (i) allows users to focus with full confidence on their optimization challenge by abstracting geometry away and (ii) enables independent advances to propagate to all optimization algorithms built atop it. We present a set of core principles and design philosophies to model a general and adaptable CDE focused on maximizing performance, accuracy and robustness. These principles are accompanied by a concrete open-source implementation called $ exttt{jagua-rs}$. This paper together with its implementation serves as a catalyst for future advances in irregular C&P problems by providing a solid foundation which can either be used as it currently exists or be further improved upon.

Problem

• Decouples geometric feasibility from optimization in cutting problems
• Provides collision detection engine for 2D irregular packing challenges
• Enables focused optimization by abstracting away geometric complexities

Innovation

• Decouples geometry from optimization challenges
• Introduces collision detection engine for 2D packing
• Provides open-source implementation called jagua-rs