Comparing several heuristics for a packing problem
This work addresses packing efficiency for logistics or manufacturing, but it is incremental as it compares existing methods without introducing new ones.
The paper compared a greedy algorithm and a hybrid genetic algorithm for the two-dimensional bin packing problem, finding that the hybrid genetic algorithm performed better on various data sizes.
Packing problems are in general NP-hard, even for simple cases. Since now there are no highly efficient algorithms available for solving packing problems. The two-dimensional bin packing problem is about packing all given rectangular items, into a minimum size rectangular bin, without overlapping. The restriction is that the items cannot be rotated. The current paper is comparing a greedy algorithm with a hybrid genetic algorithm in order to see which technique is better for the given problem. The algorithms are tested on different sizes data.