Approximation algorithms for bin packing: a survey
Approximation algorithms for NP-hard problems
A Strip-Packing Algorithm with Absolute Performance Bound 2
SIAM Journal on Computing
Online computation and competitive analysis
Online computation and competitive analysis
Speed is as powerful as clairvoyance
Journal of the ACM (JACM)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Resource augmentation for online bounded space bin packing
Journal of Algorithms
On-line Packing and Covering Problems
Developments from a June 1996 seminar on Online algorithms: the state of the art
Reverse-Fit: A 2-Optimal Algorithm for Packing Rectangles
ESA '94 Proceedings of the Second Annual European Symposium on Algorithms
Improved approximation algorithms for multidimensional bin packing problems
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Dynamic bin packing of unit fractions items
Theoretical Computer Science
Online bin packing with resource augmentation
Discrete Optimization
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We study d-dimensional dynamic bin packing for general d-dimensional boxes, for d≥2. This problem is a generalization of the bin packing problem in which items may arrive and depart dynamically. Our main result is a 3d-competitive online algorithm. We further study the 2- and 3-dimensional problem closely and improve the competitive ratios. Technically speaking, our d-dimensional result is due to a space efficient offline single bin packing algorithm, which is a variant of d-dimensional NFDH. We introduce an interesting notion of d-dimensional L-shape bin and show that effective offline packing into L-shape bin leads to effective online dynamic packing into unit-sized bins. We also investigate the resource augmentation version of the problem where the online algorithm can use d-dimensional bins of size s1 ×s2 ×⋯×sd for si≥1 while the optimal offline algorithm uses unit-sized bins. We give conditions for the online algorithm to match the performance of the optimal offline algorithm, i.e., 1-competitive.