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  • In the bin packing problem we are given an instance consisting of a sequence of items with sizes between $0$ and $1$. The objective is to pack these items into the smallest possible number of bins of unit size. {\sc FirstFit} and {\sc BestFit} algorithms are simple online algorithms introduced in early seventies, when it was also shown that their asymptotic approximation ratio is equal to $1.7$. We present a simple proof of this bound and survey recent developments that lead to the proof that also the absolute approximation ratio of these algorithms is exactly $1.7$. More precisely, if the optimum needs $\OPT$ bins, the algorithms use at most $\lfloor1.7\cdot\mbox{\sc OPT}\rfloor$ bins and for each value of $\OPT$, there are instances that actually need so many bins. We also discuss bounded-space bin packing, where the online algorithm is allowed to keep only a fixed number of bins open for future items. In this model, a variant of {\sc BestFit} also has asymptotic approximation ratio $1.7$, although it is possible that the bound is significantly smaller if also the offline solution is required to satisfy the bounded-space restriction.
  • In the bin packing problem we are given an instance consisting of a sequence of items with sizes between $0$ and $1$. The objective is to pack these items into the smallest possible number of bins of unit size. {\sc FirstFit} and {\sc BestFit} algorithms are simple online algorithms introduced in early seventies, when it was also shown that their asymptotic approximation ratio is equal to $1.7$. We present a simple proof of this bound and survey recent developments that lead to the proof that also the absolute approximation ratio of these algorithms is exactly $1.7$. More precisely, if the optimum needs $\OPT$ bins, the algorithms use at most $\lfloor1.7\cdot\mbox{\sc OPT}\rfloor$ bins and for each value of $\OPT$, there are instances that actually need so many bins. We also discuss bounded-space bin packing, where the online algorithm is allowed to keep only a fixed number of bins open for future items. In this model, a variant of {\sc BestFit} also has asymptotic approximation ratio $1.7$, although it is possible that the bound is significantly smaller if also the offline solution is required to satisfy the bounded-space restriction. (en)
Title
  • Online bin packing: Old algorithms and new results
  • Online bin packing: Old algorithms and new results (en)
skos:prefLabel
  • Online bin packing: Old algorithms and new results
  • Online bin packing: Old algorithms and new results (en)
skos:notation
  • RIV/00216208:11320/14:10286486!RIV15-GA0-11320___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GBP202/12/G061)
http://linked.open...vai/riv/dodaniDat
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  • 34586
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  • RIV/00216208:11320/14:10286486
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  • bin packing; approximation algorithms (en)
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  • [89000C77E133]
http://linked.open...v/mistoKonaniAkce
  • Budapest, Hungary
http://linked.open...i/riv/mistoVydani
  • Berlin
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  • 10th Conference on Computability in Europe (CiE)
http://linked.open...in/vavai/riv/obor
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http://linked.open...vavai/riv/projekt
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  • Sgall, Jiří
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 0302-9743
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-319-08019-2_38
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  • Springer-Verlag
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  • 978-3-319-08018-5
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  • 11320
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