A tabu thresholding implementation for the irregular stock cutting problem

The complexity of the irregular stock-cutting problem makes an ideal candidate for solution using search methods such as simulated annealing or tabu search. Although both were orginally intended as generic problem solvers it is now generally accepted that they benefit from the injection of problem-specific knowledge. This paper looks at some of the ways in which such knowledge can be incorporated into a tabu search approach to the problem. Computational experience using data with a variety of characteristics is used to show how successive improvments can be obtained, culminating in a solution procedure that is more than competitive with many other generic approaches reported in the literature.