Expert Systems with Applications

Instances of distributed reentrant permutation flow shop scheduling problem. The data is in Matlab file format.

Original data.

The data for our experiments is 285 stocks from the Shanghai Stock Exchange, and the total number of each stock is 1210 collected from trading days ranged from January 1st, 2016 to November 3rd, 2017.

data

The dataset consists of 118 daily time series, representing the demand of pasta from 01/01/2014 to 31/12/2018. Besides univariate time series data, the quantity sold is integrated by information on the presence or the absence of a promotion.

There are 4 databases, which includes four wind turbines' fault data.

A comprehensive cyberbullying dataset including social media features.

The Lingo codes of the new proposed model and classical risk measures with Z-number theory are presented.

Data sheets with the set of defects used for training and testing the classiffiers. Columns B to BC (if read with Excel) are the value of 54 descriptors. Following two columns are the defect labelling given by an expert during the supervised training (both as a char or as an index). char E -> 6 fish-eye char I -> 0 gel char O -> 1 hole char N -> 2 Black Speck char B -> 3 Bug/Insect char A -> 4 Wrinkle char G -> 5 Rubber char D -> 7 Oil Drop char L -> 8 Light dirt char F -> 9 Fail Transmission First column is the name of the defect. Its first letter should be coherent with last two columns for defects with the following exceptions: - defects named as JXXXX.bmp are defects that can be considered as gel or rubber - defects named as KXXXX.bmp are defects that can be considered as gel or black speck - defects named as MXXXX.bmp are defects that can be considered as rubber or black speck

The attachement is the quesionaire we performed for airport evaluation.