A new integrated AR-IDEA model to find the best DMU in the presence of both weight restrictions and imprecise data

2018 ◽  
Vol 125 ◽  
pp. 357-363 ◽  
Author(s):  
Bohlool Ebrahimi ◽  
Masoud Khalili
2021 ◽  
Vol 13 (8) ◽  
pp. 4236
Author(s):  
Tim Lu

The selection of advanced manufacturing technologies (AMTs) is an essential yet complex decision that requires careful consideration of various performance criteria. In real-world applications, there are cases that observations are difficult to measure precisely, observations are represented as linguistic terms, or the data need to be estimated. Since the growth of engineering sciences has been the key reason for the increased utilization of AMTs, this paper develops a fuzzy network data envelopment analysis (DEA) to the selection of AMT alternatives considering multiple decision-makers (DMs) and weight restrictions when the input and output data are represented as fuzzy numbers. By viewing the multiple DMs as a network one, the data provided by each DM can then be taken into account in evaluating the overall performances of AMT alternatives. In the solution process, we obtain the overall and DMs efficiency scores of each AMT alternative at the same time, and a relationship in which the former is a weighted average of the latter is also derived. Since the final evaluation results of AMTs are fuzzy numbers, a ranking procedure is employed to determine the most preferred one. An example is used to illustrate the applicability of the proposed methodology.


2006 ◽  
Vol 51 (1) ◽  
pp. 148-162 ◽  
Author(s):  
María Ángeles Gil ◽  
Manuel Montenegro ◽  
Gil González-Rodríguez ◽  
Ana Colubi ◽  
María Rosa Casals

1992 ◽  
Vol 7 (3) ◽  
pp. 277-291
Author(s):  
V. Protopopescu ◽  
R. Yager ◽  
J. Dockery

2015 ◽  
Vol 68 (2) ◽  
pp. 221-227 ◽  
Author(s):  
Cristina Paixão Araújo ◽  
João Felipe Coimbra Leite Costa

AbstractDecisions, from mineral exploration to mining operations, are based on grade block models obtained from samples. This study evaluates the impact of using imprecise data in short-term planning. The exhaustive Walker Lake dataset is used and is considered as the source for obtaining the true grades. Initially, samples are obtained from the exhaustive dataset at regularly spaced grids of 20 × 20 m and 5 × 5 m. A relative error (imprecision) of ±25% and a 10% bias are added to the data spaced at 5 × 5 m (short-term geological data) in different scenarios. To combine these different types of data, two methodologies are investigated: cokriging and ordinary kriging. Both types of data are used to estimate blocks with the two methodologies. The grade tonnage curves and swath plots are used to compare the results against the true block grade distribution. In addition, the block misclassification is evaluated. The results show that standardized ordinary cokriging is a better methodology for imprecise and biased data and produces estimates closer to the true grade block distribution, reducing block misclassification.


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