Making data structures persistent
STOC '86 Proceedings of the eighteenth annual ACM symposium on Theory of computing
Approximation algorithms for bin packing: a survey
Approximation algorithms for NP-hard problems
Optimal time-critical scheduling via resource augmentation (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Speed is as powerful as clairvoyance
Journal of the ACM (JACM)
Combinatorial Algorithms: For Computers and Hard Calculators
Combinatorial Algorithms: For Computers and Hard Calculators
A deterministic (2 - 2/(k+ 1))n algorithm for k-SAT based on local search
Theoretical Computer Science
The Ordered Open-End Bin-Packing Problem
Operations Research
Assembly line balancing as generalized bin packing
Operations Research Letters
Multidimensional on-line bin packing: Algorithms and worst-case analysis
Operations Research Letters
On the use of human-guided evolutionary algorithms for tackling 2D packing problems
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
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We introduce a novel variant of the well known d-dimensional bin (or vector) packing problem. Given a sequence of non-negative d-dimensional vectors, the goal is to pack these into as few bins as possible, of the smallest possible size. In the classical problem, the bin size vector is given and the sequence can be partitioned arbitrarily. We study a variation where the vectors have to be packed in the order in which they arrive, and the bin size vector can be chosen once in the beginning. This setting gives rise to two combinatorial problems: one in which we want to minimize the number of bins used for a given total bin size, and one in which we want to minimize the total bin size for a given number of bins. We prove that both problems are NP-hard, and propose an LP based bicriteria (1@e,11-@e)-approximation algorithm. We give a 2-approximation algorithm for the version with a bounded number of bins. Furthermore, we investigate properties of natural greedy algorithms, and present an easy to implement heuristic, which is fast and performs well in practice. Experiments with the heuristic and an ILP formulation yield promising results on real world data.