Probabilistic Analysis of the Dual Next-Fit Algorithm for Bin Covering

TL;DR

This paper analyzes the probabilistic performance of the Dual Next-Fit online algorithm for bin covering, showing it performs better on average than the worst-case ratio, with bounds depending on item distributions.

Contribution

It establishes probabilistic bounds for the Dual Next-Fit algorithm's expected competitive ratio across various distributions, improving understanding of its average-case performance.

Findings

Expected competitive ratio is at least 1/2 + ε for all distributions.

Upper bound of 2/3 on the expected competitive ratio.

Expected ratio equals the random-order ratio for many distributions.

Abstract

In the bin covering problem, the goal is to fill as many bins as possible up to a certain minimal level with a given set of items of different sizes. Online variants, in which the items arrive one after another and have to be packed immediately on their arrival without knowledge about the future items, have been studied extensively in the literature. We study the simplest possible online algorithm Dual Next-Fit, which packs all arriving items into the same bin until it is filled and then proceeds with the next bin in the same manner. The competitive ratio of this and any other reasonable online algorithm is $1/2$. We study Dual Next-Fit in a probabilistic setting where the item sizes are chosen i.i.d.\ according to a discrete distribution and we prove that, for every distribution, its expected competitive ratio is at least $1/2+\epsilon$ for a constant $\epsilon>0$ independent of the distribution. We also prove an upper bound of $2/3$ and better lower bounds for certain restricted classes of distributions. Finally, we prove that the expected competitive ratio equals, for a large class of distributions, the random-order ratio, which is the expected competitive ratio when adversarially chosen items arrive in uniformly random order.