# The Complete Guide to Cut Optimization
Cut optimization — technically known as the Cutting Stock Problem — is one of the most practically useful problems in combinatorial mathematics. For a project with 20 pieces and 5 bars of stock, there are millions of possible arrangements. A computational algorithm finds the near-optimal solution in milliseconds, reducing typical 15% material waste to under 5%.# Linear 1D vs Panel 2D Optimization
Linear Optimization for Bars and Profiles
Linear (1D) optimization handles material that is optimized by length alone: timber, aluminium profiles, steel sections, copper pipe, and threaded rod. The algorithm packs all required pieces onto the minimum number of stock lengths, accounting for saw kerf at every cut.
Panel Optimization for Sheet Materials
Panel (2D) optimization handles sheet materials such as plywood, MDF, chipboard, glass, acrylic, and sheet metal. It uses a guillotine-cut algorithm, meaning every cut runs edge to edge — matching how table saws and panel saws actually operate.
# Understanding Kerf: The Hidden Material Loss
The kerf is the width of material removed by the blade per cut. A standard table saw blade removes 3.0 to 3.2mm per pass. A circular hand saw removes 1.5 to 2.5mm. A laser cutter removes just 0.1 to 0.3mm. On a project with 10 cuts from a 2400mm board, a 3mm kerf costs you 30mm of usable material — enough to lose a whole small piece.# The Best Fit Decreasing Algorithm
How the Optimizer Works Under the Hood
The core algorithm uses the Best Fit Decreasing (BFD) strategy: pieces are sorted from largest to smallest, since large pieces are hardest to fit. For each piece, the algorithm scans all existing offcuts and places it in the one that leaves the least remaining space. Only when no existing offcut can accommodate the piece is a new stock bar opened. This approach typically achieves 95% or better material efficiency.