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.
A Combinatorial Optimization Approach to the Selection of Statistical Units / Bruni, Renato; Bianchi, Gianpiero; Reale, Alessandra. - In: JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION. - ISSN 1547-5816. - STAMPA. - 12:2(2016), pp. 515-527. [10.3934/jimo.2016.12.515]
A Combinatorial Optimization Approach to the Selection of Statistical Units
BRUNI, Renato
;BIANCHI, GIANPIERO;REALE, ALESSANDRA
2016
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.File | Dimensione | Formato | |
---|---|---|---|
Bruni_Preprint_A-combinatorial-optimization_2016.pdf
accesso aperto
Note: http://www.aimsciences.org/article/doi/10.3934/jimo.2016.12.515
Tipologia:
Documento in Pre-print (manoscritto inviato all'editore, precedente alla peer review)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
305.77 kB
Formato
Adobe PDF
|
305.77 kB | Adobe PDF | |
Bruni_A-combinatorial-optimization_2016.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
358.9 kB
Formato
Adobe PDF
|
358.9 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.