AbstractIt is well known that the two simple algorithms for the classic bin packing problem, NF and WF both have an approximation ratio of 2. However, WF seems to be a more reasonable algorithm, since it never opens a new bin if an existing bin can still be used.Using resource augmented analysis, where the output of an approximation algorithm, which can use bins of size b>1, is compared to an optimal packing into bins of size 1, we give a complete analysis of the asymptotic approximation ratio of WF and of NF, and use it to show that WF is strictly better than NF for any 1<b<2, while they have the same asymptotic performance guarantee for all b≥2, and for b=1
Low-order polynomial time algorithms for near-optimal solutions to the problem of bin packing are st...
In early seventies it was shown that the asymptotic approximation ratio of BestFit bin packing is eq...
In competitive analysis, we usually do not put any restrictions on the computational complexity of o...
AbstractIt is well known that the two simple algorithms for the classic bin packing problem, NF and ...
AbstractWe revisit three famous bin packing algorithms, namely Next Fit (NF), Worst Fit (WF) and Fir...
In the bin packing problem we are given an instance consisting of a sequence of items with sizes bet...
We give a simple proof and a generalization of the classical result which says that the (asymptotic)...
Abstract. In the bin packing problem we are given an instance consist-ing of a sequence of items wit...
AbstractIn this paper, we present improved bounds for the First Fit algorithm for the bin-packing pr...
In the parametric bin packing problem we must pack a list of items with size smaller than or equal t...
AbstractThe FIRST FIT DECREASING algorithm for bin packing has long been famous for its guarantee th...
We consider the NP Hard problem of online Bin Packing while requiring that larger (or longer) items ...
AbstractThis paper investigates a new version of the on-line variable-sized bin packing problem. Sup...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1973.Vita.Bibliography...
AbstractIn competitive analysis, we usually do not put any restrictions on the computational complex...
Low-order polynomial time algorithms for near-optimal solutions to the problem of bin packing are st...
In early seventies it was shown that the asymptotic approximation ratio of BestFit bin packing is eq...
In competitive analysis, we usually do not put any restrictions on the computational complexity of o...
AbstractIt is well known that the two simple algorithms for the classic bin packing problem, NF and ...
AbstractWe revisit three famous bin packing algorithms, namely Next Fit (NF), Worst Fit (WF) and Fir...
In the bin packing problem we are given an instance consisting of a sequence of items with sizes bet...
We give a simple proof and a generalization of the classical result which says that the (asymptotic)...
Abstract. In the bin packing problem we are given an instance consist-ing of a sequence of items wit...
AbstractIn this paper, we present improved bounds for the First Fit algorithm for the bin-packing pr...
In the parametric bin packing problem we must pack a list of items with size smaller than or equal t...
AbstractThe FIRST FIT DECREASING algorithm for bin packing has long been famous for its guarantee th...
We consider the NP Hard problem of online Bin Packing while requiring that larger (or longer) items ...
AbstractThis paper investigates a new version of the on-line variable-sized bin packing problem. Sup...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1973.Vita.Bibliography...
AbstractIn competitive analysis, we usually do not put any restrictions on the computational complex...
Low-order polynomial time algorithms for near-optimal solutions to the problem of bin packing are st...
In early seventies it was shown that the asymptotic approximation ratio of BestFit bin packing is eq...
In competitive analysis, we usually do not put any restrictions on the computational complexity of o...