Abstract:
The main focus of this thesis will be on the constrained two dimensional
guillotine-cut cuffing stock (C2DGC) problem. Stock cutting involves the
process of cutting certain small demand items from a larger object. During this
process, waste material is generated, which is called trim loss. The cutting
stock problem presents itself in many industrial processes where the cutting of
material is concerned, for instance the cutting of wood in the furniture
industry, the cutting of glass and plastic sheets in the glass industry, the
cutting of paper in the cardboard industry and the cutting of steel bars in
metallurgy, to name but a few. The cutting stock problem aims to find one or
more solutions to a cutting problem so that the optimal amount of the stock
sheet is utilized. This, in turn, implies that the trim loss (waste) will be kept to
a minimum.
Artificial intelligence search methods as well as existing exact C2DGC
problem solution methods are investigated and evaluated critically. Different
artificial intelligence search methods are then combined with the existing
C2DGC problem solution methods, forming feasible algorithms to solve
C2DGC problems. Existing C2DGC problem solution methods are also
enhanced using innovative ideas. Numerical tests are then conducted to test
the effectiveness and efficiency of each original and enhanced algorithm.
