dykem_capacity_goal

 Constructs the dyke material capacity goal 
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 Date: July 30, 2010, ver01
 By: Eugene Ben-Awuah
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0001 % Constructs the dyke material capacity goal
0002 %--------------------------------------------------------------------------
0003 % Date: July 30, 2010, ver01
0004 % By: Eugene Ben-Awuah
0005 %--------------------------------------------------------------------------
0006 
0007 function [A_dykemG dykemGoal dykemGoal_LB]=...
0008          dykem_capacity_goal(Blocks120, numOfPeriods, dykemTarget, dykemSlack)
0009 
0010 
0011 [numBlocks n] = size([Blocks120.EBV]');
0012 
0013 %Goal2: ore mined should be equal to processing target
0014 DykemTonnage = [Blocks120.dykeTonnage];
0015 
0016 %Check calculated processing target against your set processing target
0017 dykemTarget1=(sum(DykemTonnage))/numOfPeriods
0018 
0019 dykemG = zeros(numOfPeriods,numBlocks*numOfPeriods);
0020 for iPeriods = 1:numOfPeriods
0021     dykemG(iPeriods,((numBlocks*(iPeriods-1)+1):iPeriods*numBlocks))...
0022     = DykemTonnage(1,:);
0023 end
0024 
0025 A_dykemG = sparse(dykemG);
0026 
0027 %Create matrix for processing goal deviational variable, d2. It occurs once in
0028 %every period; resulting in a 5x5 identity matrix. the other deviational
0029 %variables do not occur in this goal, hence we use a 5x5 zero matrix to
0030 %represent each of them.
0031 dykemD3 = eye(numOfPeriods,numOfPeriods);
0032 dykemD3 = sparse(dykemD3);
0033 dykemD4 = eye(numOfPeriods,numOfPeriods)*-1;
0034 dykemD4 = sparse(dykemD4);
0035 
0036 %Creating the processing Goal target matrix: size=(number of contraints x Periods),1
0037 %dykemGoal = repmat(dykemTarget, numOfPeriods,1);
0038 dykemGoal = [dykemTarget]';
0039 dykemGoal = sparse(dykemGoal);
0040 
0041 %Create a lower bound for the dykem target to introduce a slack during
0042 %optimization since the equality constraint in such large problems results
0043 %in infeasibility. You can first try with equality before using this lower
0044 %bound slack. For equality, set Slack to zero.
0045 dykemTarget_LB = dykemTarget-dykemSlack;
0046 dykemGoal_LB = [dykemTarget_LB]';
0047 dykemGoal_LB = sparse(dykemGoal_LB);
0048 
0049 % the mulitplier vector in the goal matrix have different sizes
0050 % and units. (some are tonnage some are grade....). It is necessary to
0051 % transform the constraints matrix in a way that it unit less to be
0052 % solved by the MIP code.
0053 % divide each row vector by its norm [A(i,:)* A(i,:)']^1/2
0054 % A_miningNormVector(m,1) is vertical vector holding the respective
0055 % norm of each row of the A_mining matrix
0056 [m,n] = size(A_dykemG);
0057 
0058 for i = 1:m  
0059     A_dykemNormVector(i,1) = norm(A_dykemG(i,:));     
0060 end
0061     
0062 for i = 1:m   
0063 normA = A_dykemNormVector(i,1); 
0064 A_dykemG(i,:) = A_dykemG(i,:)/normA;
0065 
0066 %normalizing the deviational variable
0067 dykemD3(i,:) = dykemD3(i,:)/normA;
0068 dykemD4(i,:) = dykemD4(i,:)/normA;
0069 
0070 %normalizing the goal
0071 dykemGoal(i)= dykemGoal(i)/normA;
0072 
0073 %normalizing dykem goal lower bound
0074 dykemGoal_LB(i)= dykemGoal_LB(i)/normA;
0075 end
0076 
0077 A_dykemG = [zeros(m, numBlocks*numOfPeriods)...
0078            zeros(m, numBlocks*numOfPeriods),A_dykemG,...
0079            zeros(m, numBlocks*numOfPeriods)...
0080            zeros(m,m), zeros(m,m), dykemD3, dykemD4];
0081 end        
0082 
0083