Approximation and online algorithms for multidimensional bin packing: A survey

The bin packing problem is a well-studied problem in combinatorial optimization. In the classical bin packing problem, we are given a list of real numbers in (0,1] and the goal is to place them in a minimum number of bins so that no bin holds numbers summing to more than 1. The problem is extremely...

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Published in:Computer science review Vol. 24; pp. 63 - 79
Main Authors: Christensen, Henrik I., Khan, Arindam, Pokutta, Sebastian, Tetali, Prasad
Format: Journal Article
Language:English
Published: Elsevier Inc 01.05.2017
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ISSN:1574-0137, 1876-7745
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Abstract The bin packing problem is a well-studied problem in combinatorial optimization. In the classical bin packing problem, we are given a list of real numbers in (0,1] and the goal is to place them in a minimum number of bins so that no bin holds numbers summing to more than 1. The problem is extremely important in practice and finds numerous applications in scheduling, routing and resource allocation problems. Theoretically the problem has rich connections with discrepancy theory, iterative methods, entropy rounding and has led to the development of several algorithmic techniques. In this survey we consider approximation and online algorithms for several classical generalizations of bin packing problem such as geometric bin packing, vector bin packing and various other related problems. There is also a vast literature on mathematical models and exact algorithms for bin packing. However, this survey does not address such exact algorithms. In two-dimensional geometric bin packing, we are given a collection of rectangular items to be packed into a minimum number of unit size square bins. This variant has a lot of applications in cutting stock, vehicle loading, pallet packing, memory allocation and several other logistics and robotics related problems. In d-dimensional vector bin packing, each item is a d-dimensional vector that needs to be packed into unit vector bins. This problem is of great significance in resource constrained scheduling and in recent virtual machine placement in cloud computing. We also consider several other generalizations of bin packing such as geometric knapsack, strip packing and other related problems such as vector scheduling, vector covering etc. We survey algorithms for these problems in offline and online setting, and also mention results for several important special cases. We briefly mention related techniques used in the design and analysis of these algorithms. In the end we conclude with a list of open problems.
AbstractList The bin packing problem is a well-studied problem in combinatorial optimization. In the classical bin packing problem, we are given a list of real numbers in (0,1] and the goal is to place them in a minimum number of bins so that no bin holds numbers summing to more than 1. The problem is extremely important in practice and finds numerous applications in scheduling, routing and resource allocation problems. Theoretically the problem has rich connections with discrepancy theory, iterative methods, entropy rounding and has led to the development of several algorithmic techniques. In this survey we consider approximation and online algorithms for several classical generalizations of bin packing problem such as geometric bin packing, vector bin packing and various other related problems. There is also a vast literature on mathematical models and exact algorithms for bin packing. However, this survey does not address such exact algorithms. In two-dimensional geometric bin packing, we are given a collection of rectangular items to be packed into a minimum number of unit size square bins. This variant has a lot of applications in cutting stock, vehicle loading, pallet packing, memory allocation and several other logistics and robotics related problems. In d-dimensional vector bin packing, each item is a d-dimensional vector that needs to be packed into unit vector bins. This problem is of great significance in resource constrained scheduling and in recent virtual machine placement in cloud computing. We also consider several other generalizations of bin packing such as geometric knapsack, strip packing and other related problems such as vector scheduling, vector covering etc. We survey algorithms for these problems in offline and online setting, and also mention results for several important special cases. We briefly mention related techniques used in the design and analysis of these algorithms. In the end we conclude with a list of open problems.
Author Tetali, Prasad
Khan, Arindam
Pokutta, Sebastian
Christensen, Henrik I.
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  givenname: Arindam
  orcidid: 0000-0001-7505-1687
  surname: Khan
  fullname: Khan, Arindam
  email: arindam.khan@supsi.ch
  organization: Istituto Dalle Molle di studi sull’Intelligenza Artificiale (IDSIA), Scuola universitaria professionale della Svizzera italiana (SUPSI), Università della Svizzera italiana (USI), Switzerland
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  givenname: Sebastian
  surname: Pokutta
  fullname: Pokutta, Sebastian
  email: sebastian.pokutta@isye.gatech.edu
  organization: Georgia Institute of Technology, Atlanta, USA
– sequence: 4
  givenname: Prasad
  surname: Tetali
  fullname: Tetali, Prasad
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  organization: Georgia Institute of Technology, Atlanta, USA
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Keywords Bin packing
Online algorithms
Approximation algorithms
Multidimensional packing and covering
Multidimensional scheduling
Geometric packing
Language English
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Snippet The bin packing problem is a well-studied problem in combinatorial optimization. In the classical bin packing problem, we are given a list of real numbers in...
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StartPage 63
SubjectTerms Approximation algorithms
Bin packing
Geometric packing
Multidimensional packing and covering
Multidimensional scheduling
Online algorithms
Title Approximation and online algorithms for multidimensional bin packing: A survey
URI https://dx.doi.org/10.1016/j.cosrev.2016.12.001
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