AI RESEARCH

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

arXiv CS.AI

ArXi:2604.01732v2 Announce Type: replace 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 constraints are activated only for copies assigned to the same sheet. We also.