A simple proof of Liang's lower bound for on-line bin packing and the extension to the parametric case

In this note we present a simplified proof of a lower bound for on-line bin packing. This proof also covers the well-known result given by Liang in Inform. Process Lett. 10 (1980) 76–79.

An average-case analysis of bin packing with uniformly distributed item sizes

We analyze the one-dimensional bin-packing problem under the assumption that bins have unit capacity, and that items to be packed are drawn from a uniform distribution on [0,1]. Building on some recent work by Frederickson, we give an algorithm which uses n/2+0(n^½) bins on the average to pack n items. (Knodel has achieved a similar result.) The analysis involves the use of a certain 1-dimensional random walk. We then show that even an optimum packing under this distribution uses n/2+0(n^1/2) bins on the average, so our algorithm is asymptotically optimal, up to constant factors on the amount of wasted space. Finally, following Frederickson, we show that two well-known greedy bin-packing algorithms use no more bins than our algorithm; thus their behavior is also in asymptotically optimal in this sense

A simple proof of Liang's lower bound for on-line bin packing and the extension to the parametric case

In this note we present a simplified proof of a lower bound for on-line bin packing. This proof also covers the well-known result given by Liang in Inform. Process Lett. 10 (1980) 76–79

Probabilistic alternatives for competitive analysis

In the last 20 years competitive analysis has become the main tool for analyzing the quality of online algorithms. Despite of this, competitive analysis has also been criticized: it sometimes cannot discriminate between algorithms that exhibit significantly different empirical behavior or it even favors an algorithm that is worse from an empirical point of view. Therefore, there have been several approaches to circumvent these drawbacks. In this survey, we discuss probabilistic alternatives for competitive analysis.operations research and management science;