Column-sparse packing problems arise in several contexts in both deterministic and stochastic discrete optimization. We present two unifying ideas, (non-uniform) attenuation and multiple-chance algorithms, to obtain improved approximation algorithms for some well-known families of such problems. As three main examples, we attain the integrality gap, up to lower-order terms, for known LP relaxat...

We study the parallel complexity of polynomial heuristics for the bin packing problem. We show that some well-known (and simple) methods like first-fit-decreasing are P-complete, and it is hence very unlikely that they can be efficiently parallelized. On the other hand, we exhibit an optimal NC algorithm that achieves the same performance bound as does FFD. Finally, we discuss parallelization o...

In rectangle packing problems we are given the task of positioning some axis-aligned rectangles inside a given plane region, so that they do not overlap with each other. In the Maximum Weight Independent Set of Rectangles (MWISR) problem, their position is already fixed and we can only select which rectangles to choose, while trying to maximize their total weight. In the Strip Packing problem, ...

The present work proposes new heuristics and algorithms for the 3D Cutting and Packing class of problems. Specifically the cutting stock problem and a real-world application from the retail steel distribution industry are addressed. The problem being addressed for the retail steel distribution industry is the retail steel cutting problem, which is how to cut steel in order to satisfy the custom...