Comparison of two metaheuristics to solve a 2-D cutting stock problem with set-up cost in the paper industry

Abstract

This paper deals with the two-dimensional cutting stock problem with set-up cost (2CSP-S). This problem is composed of three optimisation sub-problems: a 2-D bin packing (2BP) problem (to place images on patterns), a linear programming (LP) problem (to find for each pattern the number of stock sheets to be printed) and a combinatorial problem (to find the number of each image on each pattern). We have already developed two different metaheuristics to solve the 2CSP-S focussing on this third sub-problem: a simulated annealing and a genetic algorithm. In this article, we propose to compare these two approaches. It is important to notice that our approaches are not new packing techniques. This work was conducted for a paper industry company and experiments were realised on real and artificial data sets.