Auto tuning of 3-D packing rules using genetic algorithms

A new approach of how to solve the 3D packing problem automatically is proposed. This problem is well known as a complete combinatorial problem. The authors attempt to realize a mechanism in which the 3D packing rule is automatically tuned, and an optimum packing solution is obtained by applying genetic algorithms which mimic the process of a natural evolution system. The 3D packing strategy is controlled by two evaluation functions which dominate the selection of a next allocation position and a box. To find the near-optimal strategy, the weighted coefficients of the evaluation functions are tuned by applying the genetic operators such as reproduction, crossover and mutation. To use the obtained tuned results as accumulated successful strategies, a 3D packing rule-base is constructed. The rules in this rule-base are composed of a `conditional part´, which expresses the features of the given problem, and a `procedural part´, which gives the packing strategy