Best-Worst Method(BWM) in Matlab - linprog - matlab

Question

I need a guidance on a problem with linprog. I have an optimization problem that weights and the minimum value of the objective function are needed, but I can't allocate the parameters properly. The form of problem and subjects are like this. The picture of the problem is attached.
Any help regarding understanding the problem and getting a solution would be much appreciated. The picture below contains the formula for the problem.
IMAGE
P.S.: The object here is to code BWM(Best Worst Method) By Dr. Jafar Rezaei in matlab.
Ws and Ksi are the optimal weights and objective function we want to obtain and As are the vector that its values are present.

Answer 1

Here is the code in matlab.
clc;clear;close all;
delete Data.mat
DATA = xlsread("Data.xlsx");
NumOfExperts = size(DATA , 1) / 2;
NumOfCriteria = size(DATA , 2);
BestData = DATA( 1:NumOfExperts , : );
WorstData = DATA( NumOfExperts + 1: end , :);
WorstData = WorstData';
ConsistencyIndex = [0 , .44 , 1 , 1.63 , 2.3 ,3 , 3.73 , 4.47 , 5.23];
DATA = struct;
for i = 1:NumOfExperts
IndexOfBest = find(BestData(i , :) == 0);
IndexOfWorst = find(WorstData(: , i) == 0);
BestData(i , IndexOfBest) = 1;
WorstData(IndexOfWorst , i) = 1;
DATA(i).Best = BestData(i , :);
DATA(i).Worst = WorstData(: , i);
DATA(i).BestIndex = IndexOfBest;
DATA(i).WorstIndex = IndexOfWorst;
end
clear BestData .. WorstData .. IndexOfBest .. IndexOfWorst;
MeanWeight = zeros(1 , NumOfCriteria);
for i = 1:NumOfExperts
Data = DATA(i);
save Data
EqualCoefftMat = ones(1 , NumOfCriteria);
InitialPoints = rand(1 , NumOfCriteria);
LowerBound = zeros(1 , NumOfCriteria);
UpperBound = ones(1 , NumOfCriteria);
options = optimoptions(#fmincon , 'Algorithm' , 'interior-point' , 'MaxFunctionEvaluations' , 50000 , 'MaxIterations' , 5000 );
[DATA(i).Weights , DATA(i).Ksi] = fmincon(#Epsilon , InitialPoints , [] , [] , EqualCoefftMat , 1 , LowerBound , UpperBound , [], options);
DATA(i).ConsistencyRatio = DATA(i).Ksi/ConsistencyIndex(max(max(DATA(i).Best) , max(DATA(i).Worst)));
MeanWeight = MeanWeight + DATA(i).Weights;
end
MeanWeight = MeanWeight/NumOfExperts;
bar(MeanWeight);
xlabel('Criterias');
ylabel('Weights');
title(['Mean Of Weights is: ', num2str(mean(MeanWeight))]);
[Result.Weights , Result.IndexOfWeight] = sort(MeanWeight , 'descend');
function Out = Epsilon(x)
load Data
for i = 1:NumOfCriteria
f(i) = abs(x(Data.BestIndex)/x(i) - Data.Best(i));
g(i) = abs(x(i)/x(Data.WorstIndex) - Data.Worst(i));
end
Out = (sum(f) + sum(g) - g(Data.BestIndex))*2/NumOfCriteria;
end