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The Bounded Beam Search algorithm for the Block Relocation Problem

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Tiziano Bacci, Sara Mattia, Paolo Ventura

Other Computer Science · Submitted 22 Jun 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We use it within a bounded beam search algorithm to solve the Block Relocation Problem and show that the considered heuristic approach outperforms the other existing algorithms on most of the instances in the literature.

how it is stated · asserted 1.00 read the record →

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arXiv ID2206.12303 (version 1)
Pith IDZWWWJYW2, Pith's permanent record of this paper and its review
CitationPaolo Ventura, Sara Mattia, Tiziano Bacci. "The Bounded Beam Search algorithm for the Block Relocation Problem." arXiv:2206.12303 (2022). https://pith.science/paper/ZWWWJYW2
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,151 input tokens.

What the abstract itself states, as probabilities: claims first 0.08 · states limitation 0.09 · human participants 0.02 · controlled comparison 0.48 · randomized assignment 0.01 · states quantitative result 0.17 · compares with prior results 0.97 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In this paper we deal with the restricted Block Relocation Problem. We present a new lower bound and a heuristic approach for the problem. The proposed lower bound can be computed in polynomial time and it is provably better than some previously known lower bounds. We use it within a bounded beam search algorithm to solve the Block Relocation Problem and show that the considered heuristic approach outperforms the other existing algorithms on most of the instances in the literature. In order to test the approaches on real-size dimensions, new large instances of the Block Relocation Problem are also introduced.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In this paper we deal with the restricted Block Relocation Problem. background 0.98 0.46 0.02 asserted 0.85
s1 We present a new lower bound and a heuristic approach for the problem. contribution 1.00 0.94 0.08 asserted 0.99
s2 The proposed lower bound can be computed in polynomial time and it is provably better than some previously known lower bounds. contribution 0.50 0.89 0.10 asserted 1.00
s3 We use it within a bounded beam search algorithm to solve the Block Relocation Problem and show that the considered heuristic approach outperforms the other existing algorithms on most of the instances in the literature. result 0.72 0.95 0.75 asserted 1.00
s4 In order to test the approaches on real-size dimensions, new large instances of the Block Relocation Problem are also introduced. contribution 0.60 0.93 0.03 asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.93.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In this paper we deal with the restricted Block Relocation Problem. We present a new lower bound and a heuristic approach for the problem. The proposed lower bound can be computed in polynomial time and it is provably better than some previously known lower bounds. We use it within a bounded beam search algorithm to solve the Block Relocation Problem and show that the considered heuristic approach outperforms the other existing algorithms on most of the instances in the literature. In order to test the approaches on real-size dimensions, new large instances of the Block Relocation Problem are also introduced.

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