On Generalized Comparison-Based Sorting Problems

2013 ◽  
pp. 164-175 ◽  
Author(s):  
Jean Cardinal ◽  
Samuel Fiorini
Keyword(s):  
Sorting Problems

scholarly journals AHPSort: an AHP-based method for sorting problems

2012 ◽  
Vol 50 (17) ◽  
pp. 4767-4784 ◽  
Author(s):  
Alessio Ishizaka ◽  
Craig Pearman ◽  
Philippe Nemery
Keyword(s):  
Sorting Problems

scholarly journals Classification Approach towards Ranking and Sorting Problems

2003 ◽  
pp. 301-312 ◽  
Author(s):  
Shyamsundar Rajaram ◽  
Ashutosh Garg ◽  
Xiang Sean Zhou ◽  
Thomas S. Huang
Keyword(s):  
Classification Approach ◽  
Sorting Problems

scholarly journals Multiple criteria hierarchy process for sorting problems under uncertainty applied to the evaluation of the operational maturity of research institutions

Omega ◽  
2020 ◽  
pp. 102381
Author(s):  
Renata Pelissari ◽  
Alvaro José Abackerli ◽  
Sarah Ben Amor ◽  
Maria Célia Oliveira ◽  
Kleber Manoel Infante
Keyword(s):  
Multiple Criteria ◽  
Research Institutions ◽  
Sorting Problems ◽  
Hierarchy Process

An approach to color-based sorting of bulk materials with automated estimation of system parameters

2015 ◽  
Vol 82 (3) ◽  
Author(s):  
Matthias Richter ◽  
Thomas Längle ◽  
Jürgen Beyerer
Keyword(s):  
Feature Construction ◽  
Construction Process ◽  
Bulk Materials ◽  
Training Images ◽  
Sorting Machine ◽  
Color Features ◽  
System Operator ◽  
Sorting Problems ◽  
Intuitive Interpretation ◽  
Count Statistic

AbstractIn this paper, we present a flexible method for color-based sorting of bulk materials. It is based on semantically meaningful color features that are constructed from a set of training images. First, estimates of color-occurrence frequencies of different materials are derived from the training images and fused into color classes, which are then used to classify individual pixels. An object descriptor is built as count statistic over the color classes appearing in the object image. This descriptor has many advantages: it is compact and very fast to compute, invariant to scale and rotation, has a very clear, intuitive interpretation, and can be used with simple rule-based classifiers. However, tuning the parameters that govern the feature construction process is laborious and requires a lot of experience on part of the system operator. To overcome this shortcoming, we automatically learn the parameters using genetic algorithms. We apply our method to wine grape sorting problems to show that this approach outperforms a human expert. At the same time, it takes considerably less effort on the human part and frees the expert to attend to other tasks. Furthermore, the system allows non-experts to successfully put a sorting machine in operation.

scholarly journals Automatic Enhancement of the Reference Set for Multi-Criteria Sorting in The Frame of Theseus Method

2014 ◽  
Vol 39 (2) ◽  
pp. 57-77 ◽  
Author(s):  
Eduardo Fernandez ◽  
Jorge Navarro ◽  
Eduardo Salomon
Keyword(s):  
Performance Measures ◽  
Test Results ◽  
Sorting Method ◽  
Automatic Mode ◽  
Reference Information ◽  
Reference Set ◽  
Sorting Problems ◽  
Manageable Level ◽  
Valid Information

Abstract Some recent works have established the importance of handling abundant reference information in multi-criteria sorting problems. More valid information allows a better characterization of the agent’s assignment policy, which can lead to an improved decision support. However, sometimes information for enhancing the reference set may be not available, or may be too expensive. This paper explores an automatic mode of enhancing the reference set in the framework of the THESEUS multi-criteria sorting method. Some performance measures are defined in order to test results of the enhancement. Several theoretical arguments and practical experiments are provided here, supporting a basic advantage of the automatic enhancement: a reduction of the vagueness measure that improves the THESEUS accuracy, without additional efforts from the decision agent. The experiments suggest that the errors coming from inadequate automatic assignments can be kept at a manageable level.

scholarly journals FlowSort-GDSS – A novel group multi-criteria decision support system for sorting problems with application to FMEA

2015 ◽  
Vol 42 (17-18) ◽  
pp. 6342-6349 ◽  
Author(s):  
Francesco Lolli ◽  
Alessio Ishizaka ◽  
Rita Gamberini ◽  
Bianca Rimini ◽  
Michael Messori
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Sorting Problems

scholarly journals UMA REVISÃO DE TRÊS PACOTES DE SOFTWARE DEDICADOS A PROBLEMAS MULTIATRIBUTO

Engevista ◽  
2015 ◽  
Vol 17 (3) ◽  
pp. 288
Author(s):  
João Carlos Namorado Clímaco ◽  
Luis Dias ◽  
Luis Alçada Almeida
Keyword(s):  
Robustness Analysis ◽  
Additive Model ◽  
Critical Discussion ◽  
Value Functions ◽  
Software Packages ◽  
Electre Tri ◽  
Electre I ◽  
Sorting Problems ◽  
Simple Additive Weighting ◽  
Vip Analysis

In this work we outline and make a critical discussion of three software packages dedicated to multiattribute models, based on a learning oriented paradigm and involving in its development researchers of INESC-Coimbra. Namely, VIP-Analysis Dias and Clímaco 2000) based on the additive model in order to aggregate value functions, but just requiring imprecise information on the scale coefficients; the IRIS system (Interactive Robustness analysis and parameters Inference for multicriteria Sorting problems - Dias e Mousseau, 2003) dedicated to the sorting problematic, according to a pre-defined set of categories, based on the ELECTRE TRI (Yu 1992), but not requiring the decision maker to fix precise values for all parameters of the method; and, finally , an web interface, called “MATRIX”, for a base of methods including: : Simple Additive Weighting and TOPSIS (Yoon and Hwang, 1995), VIKOR (Duckstein and Opricovic, 1980), ELECTRE I (Benayoun et al., 1966; Roy, 1968), ELECTRE III (Roy, 1978), ELECTRE IV (Roy and Hugonnard, 1982), ELECTRE TRI (Yu, 1992) e AHP (Saaty, 1980).

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