Ant Colony Optimisation for Bin Packing and Cutting Stock Problems

The bin packing and the cutting stock problems are two well-known NP-hard combinatorial optimisation problems. Only very small instances can be solved exactly, so for real-world problems we have to rely on heuristic solution methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including genetic algorithms and evolutionary programming. In this dissertation, I try to solve the bin packing and the cutting stock problem using ant colony optimisation, a new class of meta-heuristics introduced by Dorigo in 1992. This meta-heuristic is inspired by the path-finding abilities of real ant colonies. It combines an artificial pheromone trail with simple heuristic information to stochastically build new solutions. I show that this approach gives good results, especially when combined with local search, and that it can outperform existing evolutionary approaches. A disadvantage of the method is that it is quite sensitive to the relative weighing of the heuristic information as opposed to the pheromone trail information.