A new and improved algorithm for online bin packing

TL;DR

This paper introduces an improved online bin packing algorithm that achieves an asymptotic competitive ratio below 1.58, utilizing advanced class-based packing strategies and simplified analysis methods.

Contribution

The paper presents a novel algorithm with a new packing approach and simplified analysis, achieving a competitive ratio below 1.58 for online bin packing.

Findings

Achieved an asymptotic competitive ratio of 1.5783

Developed a new method for class-based item packing

Simplified the analysis using standard weight functions

Abstract

We revisit the classic online bin packing problem. In this problem, items of positive sizes no larger than 1 are presented one by one to be packed into subsets called "bins" of total sizes no larger than 1, such that every item is assigned to a bin before the next item is presented. We use online partitioning of items into classes based on sizes, as in previous work, but we also apply a new method where items of one class can be packed into more than two types of bins, where a bin type is defined according to the number of such items grouped together. Additionally, we allow the smallest class of items to be packed in multiple kinds of bins, and not only into their own bins. We combine this with the approach of packing of sufficiently big items according to their exact sizes. Finally, we simplify the analysis of such algorithms, allowing the analysis to be based on the most standard weight functions. This simplified analysis allows us to study the algorithm which we defined based on all these ideas. This leads us to the design and analysis of the first algorithm of asymptotic competitive ratio strictly below 1.58, specifically, we break this barrier and provide an algorithm AH (Advanced Harmonic) whose asymptotic competitive ratio does not exceed 1.5783.