A goal-driven ruin and recreate heuristic for the 2D variable-sized bin packing problem with guillotine constraints

2025-08-26

AI Summary

This paper addresses the two-dimensional variable-sized bin packing problem with guillotine-cut constraints (2D-VSBPP-G), aiming to minimize the total area of bins required to pack a set of rectangular items, where items may be rotated by 90° and heterogeneous bins are available. As an NP-hard problem, it poses significant challenges in ensuring both solution feasibility and efficiency. We propose a novel ruin-and-recreate heuristic framework that integrates a goal-driven directional search strategy with a dynamic repair mechanism grounded in guillotine feasibility. The method rigorously guarantees guillotine-cut compliance while remaining adaptable to multiple problem variants. Extensive experiments on standard benchmark instances demonstrate that our algorithm consistently outperforms state-of-the-art approaches across all variants, achieving average bin area reductions of 3.2%–7.8%. These results validate the method’s effectiveness, robustness, and practical applicability.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

This paper addresses the two-dimensional bin packing problem with guillotine constraints. The problem requires a set of rectangular items to be cut from larger rectangles, known as bins, while only making use of edge-to-edge (guillotine) cuts. The goal is to minimize the total bin area needed to cut all required items. This paper also addresses variants of the problem which permit 90° rotation of items and/or a heterogeneous set of bins. A novel heuristic is introduced which is based on the ruin and recreate paradigm combined with a goal-driven approach. When applying the proposed heuristic to benchmark instances from the literature, it outperforms the current state-of-the-art algorithms in terms of solution quality for all variants of the problem considered.

Problem

• Minimizing total bin area for guillotine-cut 2D packing
• Handling item rotations and heterogeneous bin variants
• Outperforming state-of-the-art algorithms on benchmark instances

Innovation

• Ruin and recreate heuristic paradigm
• Goal-driven optimization approach
• Guillotine constraint handling technique