A combinatorial optimization approach to the selection of statistical units

Renato Bruni, Gianpiero Bianchi, Alessandra Reale

Research output: Contribution to journalArticle

Abstract

In the case of some large statistical surveys, the set of units that will constitute the scope of the survey must be selected. We focus on the real case of a Census of Agriculture, where the units are farms. Surveying each unit has a cost and brings a different portion of the whole information. In this case, one wants to determine a subset of units producing the minimum total cost for being surveyed and representing at least a certain portion of the total information. Uncertainty aspects also occur, because the portion of information corresponding to each unit is not perfectly known before surveying it. The proposed approach is based on combinatorial optimization, and the arising decision problems are modeled as multidimensional binary knapsack problems. Experimental results show the effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)515-527
Number of pages13
JournalJournal of Industrial and Management Optimization
Volume12
Issue number2
DOIs
StatePublished - 2016

Fingerprint

Combinatorial optimization
Surveying
Costs
Unit
Agriculture
Farms
Census
Uncertainty
Farm
Knapsack problem
Decision problem
Binary
Subset
Experimental results

Keywords

  • Data mining
  • Discrete optimization
  • Knowledge management

ASJC Scopus subject areas

  • Business and International Management
  • Strategy and Management
  • Control and Optimization
  • Applied Mathematics

Cite this

A combinatorial optimization approach to the selection of statistical units. / Bruni, Renato; Bianchi, Gianpiero; Reale, Alessandra.

In: Journal of Industrial and Management Optimization, Vol. 12, No. 2, 2016, p. 515-527.

Research output: Contribution to journalArticle

Bruni, Renato; Bianchi, Gianpiero; Reale, Alessandra / A combinatorial optimization approach to the selection of statistical units.

In: Journal of Industrial and Management Optimization, Vol. 12, No. 2, 2016, p. 515-527.

Research output: Contribution to journalArticle

@article{b093d40861ec4cd0a27ea7e9d10c9846,
title = "A combinatorial optimization approach to the selection of statistical units",
abstract = "In the case of some large statistical surveys, the set of units that will constitute the scope of the survey must be selected. We focus on the real case of a Census of Agriculture, where the units are farms. Surveying each unit has a cost and brings a different portion of the whole information. In this case, one wants to determine a subset of units producing the minimum total cost for being surveyed and representing at least a certain portion of the total information. Uncertainty aspects also occur, because the portion of information corresponding to each unit is not perfectly known before surveying it. The proposed approach is based on combinatorial optimization, and the arising decision problems are modeled as multidimensional binary knapsack problems. Experimental results show the effectiveness of the proposed approach.",
keywords = "Data mining, Discrete optimization, Knowledge management",
author = "Renato Bruni and Gianpiero Bianchi and Alessandra Reale",
year = "2016",
doi = "10.3934/jimo.2016.12.515",
volume = "12",
pages = "515--527",
journal = "Journal of Industrial and Management Optimization",
issn = "1547-5816",
publisher = "American Institute of Mathematical Sciences",
number = "2",

}

TY - JOUR

T1 - A combinatorial optimization approach to the selection of statistical units

AU - Bruni,Renato

AU - Bianchi,Gianpiero

AU - Reale,Alessandra

PY - 2016

Y1 - 2016

N2 - In the case of some large statistical surveys, the set of units that will constitute the scope of the survey must be selected. We focus on the real case of a Census of Agriculture, where the units are farms. Surveying each unit has a cost and brings a different portion of the whole information. In this case, one wants to determine a subset of units producing the minimum total cost for being surveyed and representing at least a certain portion of the total information. Uncertainty aspects also occur, because the portion of information corresponding to each unit is not perfectly known before surveying it. The proposed approach is based on combinatorial optimization, and the arising decision problems are modeled as multidimensional binary knapsack problems. Experimental results show the effectiveness of the proposed approach.

AB - In the case of some large statistical surveys, the set of units that will constitute the scope of the survey must be selected. We focus on the real case of a Census of Agriculture, where the units are farms. Surveying each unit has a cost and brings a different portion of the whole information. In this case, one wants to determine a subset of units producing the minimum total cost for being surveyed and representing at least a certain portion of the total information. Uncertainty aspects also occur, because the portion of information corresponding to each unit is not perfectly known before surveying it. The proposed approach is based on combinatorial optimization, and the arising decision problems are modeled as multidimensional binary knapsack problems. Experimental results show the effectiveness of the proposed approach.

KW - Data mining

KW - Discrete optimization

KW - Knowledge management

UR - http://www.scopus.com/inward/record.url?scp=84955249301&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84955249301&partnerID=8YFLogxK

U2 - 10.3934/jimo.2016.12.515

DO - 10.3934/jimo.2016.12.515

M3 - Article

VL - 12

SP - 515

EP - 527

JO - Journal of Industrial and Management Optimization

T2 - Journal of Industrial and Management Optimization

JF - Journal of Industrial and Management Optimization

SN - 1547-5816

IS - 2

ER -