Solving the Two-dimensional single stock size Cutting Stock Problem with SAT and MaxSAT

📰 ArXiv cs.AI

Solving the 2D-CSSP using SAT and MaxSAT to minimize waste in cutting stock problems

advanced Published 6 Apr 2026

Action Steps

Formulate the 2D-CSSP as a SAT problem by expanding item types by demand
Assign sheet-assignment variables to each item copy
Implement non-overlap constraints to prevent item overlap
Use MaxSAT to find the optimal solution that minimizes waste

Who Needs to Know This

This research benefits software engineers and AI researchers working on optimization problems, as well as manufacturers seeking to improve their cutting stock processes

Key Insight

💡 The 2D-CSSP can be effectively solved using SAT and MaxSAT techniques, reducing waste in manufacturing processes

Key Takeaways

Solving the 2D-CSSP using SAT and MaxSAT to minimize waste in cutting stock problems

Full Article

Title: Solving the Two-dimensional single stock size Cutting Stock Problem with SAT and MaxSAT

Abstract:
arXiv:2604.01732v2 Announce Type: replace Abstract: Cutting rectangular items from stock sheets to satisfy demands while minimizing waste is a central manufacturing task. The Two-Dimensional Single Stock Size Cutting Stock Problem (2D-CSSP) generalizes bin packing by requiring multiple copies of each item type, which causes a strong combinatorial blow-up. We present a SAT-based framework where item types are expanded by demand, each copy has a sheet-assignment variable and non-overlap constraint

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