A Framework for Efficient Approximation Schemes on Geometric Packing Problems of $d$-dimensional Fat Objects

TL;DR

This paper introduces a versatile framework for designing efficient approximation schemes for a broad class of geometric packing problems involving $d$-dimensional convex fat objects, addressing multiple classic problems with added features.

Contribution

The paper presents the first general framework for approximation algorithms in geometric packing, capable of handling various problems and additional constraints with a unified approach.

Findings

Framework effectively addresses multiple packing problems.

Handles item multiplicity, rotation, and constraints.

Provides nearly decomposable integer programming formulations.

Abstract

We investigate approximation algorithms for several fundamental optimization problems on geometric packing. The geometric objects considered are very generic, namely $d$-dimensional convex fat objects. Our main contribution is a versatile framework for designing efficient approximation schemes for classic geometric packing problems. The framework effectively addresses problems such as the multiple knapsack, bin packing, multiple strip packing, and multiple minimum container problems. Furthermore, the framework handles additional problem features, including item multiplicity, item rotation, and additional constraints on the items commonly encountered in packing contexts. The core of our framework lies in formulating the problems as integer programs with a nearly decomposable structure. This approach enables us to obtain well-behaved fractional solutions, which can then be efficiently rounded. By modeling the problems in this manner, our framework offers significant flexibility, allowing it to address a wide range of problems and incorporate additional features. To the best of our knowledge, prior to this work, the known results on approximation algorithms for packing problems were either highly fixed for one problem or restricted to one class of objects, mainly polygons and hypercubes. In this sense, our framework is the first result with a general toolbox flavor in the context of approximation algorithms for geometric packing problems. Thus, we believe that our technique is of independent interest, being possible to inspire further work on geometric packing.