The authors recently presented a technique (denominated “ZM”) to fuse multiple (subjective) preference rankings of some objects of interest - in manufacturing applications - into a common unidimensional ratio scale (Franceschini, Maisano 2019). Although this technique can be applied to a variety of decision-making problems in the Manufacturing field, it is limited by a response mode requiring the formulation of complete preference rankings, i.e. rankings that include all objects. Unfortunately, this model is unsuitable for some practical contexts – such as decision-making problems characterized by a relatively large number of objects, field surveys, etc. – where respondents can barely identify the more/less preferred objects, without realistically being able to construct complete preference rankings. The purpose of this paper is to develop a new technique (denominated “ZMII”) which also “tolerates” incomplete preference rankings, e.g., rankings with the more/less preferred objects only. This technique borrows the underlying postulates from the Thurstone’s Law of Comparative Judgment and uses the Generalized Least Squares method to obtain a ratio scaling of the objects of interest, with a relevant uncertainty estimation. Preliminary results show the effectiveness of the new technique even for relatively incomplete preference rankings. Description is supported by an application example concerning the design of a coach-bus seat.

Fusing incomplete preference rankings in design for manufacturing applications through the ZM II-technique / Franceschini, Fiorenzo; Maisano, DOMENICO AUGUSTO FRANCESCO. - In: INTERNATIONAL JOURNAL, ADVANCED MANUFACTURING TECHNOLOGY. - ISSN 0268-3768. - STAMPA. - 103:9-12(2019), pp. 3307-3322. [10.1007/s00170-019-03675-5]

Fusing incomplete preference rankings in design for manufacturing applications through the ZM II-technique

Fiorenzo Franceschini;Domenico Maisano
2019

Abstract

The authors recently presented a technique (denominated “ZM”) to fuse multiple (subjective) preference rankings of some objects of interest - in manufacturing applications - into a common unidimensional ratio scale (Franceschini, Maisano 2019). Although this technique can be applied to a variety of decision-making problems in the Manufacturing field, it is limited by a response mode requiring the formulation of complete preference rankings, i.e. rankings that include all objects. Unfortunately, this model is unsuitable for some practical contexts – such as decision-making problems characterized by a relatively large number of objects, field surveys, etc. – where respondents can barely identify the more/less preferred objects, without realistically being able to construct complete preference rankings. The purpose of this paper is to develop a new technique (denominated “ZMII”) which also “tolerates” incomplete preference rankings, e.g., rankings with the more/less preferred objects only. This technique borrows the underlying postulates from the Thurstone’s Law of Comparative Judgment and uses the Generalized Least Squares method to obtain a ratio scaling of the objects of interest, with a relevant uncertainty estimation. Preliminary results show the effectiveness of the new technique even for relatively incomplete preference rankings. Description is supported by an application example concerning the design of a coach-bus seat.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2745055
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