EconPapers    
Economics at your fingertips  
 

Adapting Thurstone’s Law of Comparative Judgment to fuse preference orderings in manufacturing applications

F. Franceschini () and D. Maisano
Additional contact information
F. Franceschini: Politecnico di Torino
D. Maisano: Politecnico di Torino

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 2, No 9, 387-402

Abstract: Abstract A rather common problem in the manufacturing field includes: (i) a collection of objects to be compared on the basis of the degree of some attribute, (ii) a set of judges that individually express their subjective judgments on these objects, and (iii) a single collective judgment, which is obtained by fusing the previous subjective judgments. The goal of this contribution is to develop a new technique that combines the Thurstone’s Law of Comparative Judgment with an ad hoc response mode based on preference orderings. Apart from being relatively practical and user-friendly, this technique allows to express the collective judgment of objects on a ratio scale and is applicable to a variety of practical contexts in the field of manufacturing. The description of the proposed technique is integrated with the application to a practical case study.

Keywords: Manufacturing; Quality engineering/management; Decision making; Preference ordering; Paired comparison; Law of comparative judgment; Scaling; Ratio scale (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-018-1452-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:31:y:2020:i:2:d:10.1007_s10845-018-1452-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-018-1452-5

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:joinma:v:31:y:2020:i:2:d:10.1007_s10845-018-1452-5