PARTICLE SWARM OPTIMIZATION FOR CUTTING ALUMINUM STOCK AND ITS COMPARISON WITH THE EXACT METHOD

The Cutting Stock Problem (CSP) is a common challenge in many industries, involving the optimization of material cutting to minimize waste while meeting customer demands. Various methods can be used to address this issue. This paper applies the heuristic Particle Swarm Optimization (PSO) method to solve CSP in the case of one-dimensional aluminum roll cutting. First, we identify feasible cutting pattern combinations. A mathematical model and constraints are then formulated based on these patterns. Next, the PSO algorithm is employed to determine the optimal combination of cutting patterns, minimizing material waste. The results yield the optimal aluminum roller cutting pattern. Furthermore, we compare the results between the PSO method and the exact method.