Probabilistic bounds for dual bin-packing
Acta Informatica
An improved lower bound for on-line bin packing algorithms
Information Processing Letters
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
Approximation algorithms for bin packing: a survey
Approximation algorithms for NP-hard problems
Online computation and competitive analysis
Online computation and competitive analysis
Competitive non-preemptive call control
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
The Accommodating Function: A Generalization of the Competitive Ratio
SIAM Journal on Computing
On-line Packing and Covering Problems
Developments from a June 1996 seminar on Online algorithms: the state of the art
Fast algorithms for packing problems.
Fast algorithms for packing problems.
Seat reservation allowing seat changes
Journal of Algorithms
The relative worst order ratio for online algorithms
ACM Transactions on Algorithms (TALG)
| Hi-index | 0.00 |
We consider the On-Line Dual Bin Packing problem where we have a fixed number n of bins of equal size and a sequence of items. The goal is to maximize the number of items that are packed in the bins by an on-line algorithm. An investigation of First-Fit and an algorithm called Log shows that, in the special case where all sequences can be completely packed by an optimal off-line algorithm, First-Fit has a constant competitive ratio, but Log does not. In contrast, if there is no restriction on the input sequences, Log is exponentially better than First-Fit. This is the first separation of this sort with a difference of more than a constant factor. We also design randomized and deterministic algorithms for which the competitive ratio is constant on sequences which the optimal off-line algorithm can pack using at most αn bins, if α is constant and known to the algorithm in advance.