Managing Risks of Supply-Chain Disruptions: Dual ... - CiteSeerX

% Delay Local Production when disruptionCONSTANTS.Delay=0; [in % of production/period]---------------------------------------------------------------------------------------------------------------------------------------------------------------------- GENERATION OF BINOMIAL TREE -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------function [TREE] = GenBinTree(CONSTANTS)% Period lengthd_t= CONSTANTS.Tlife_y/CONSTANTS.num_period;TREE.dt = d_t;% Variation of revenues at each periodd_Y = sqrt(CONSTANTS.sigma^2*d_t + (CONSTANTS.alpha-CONSTANTS.sigma^2/2)^2*d_t^2);% Probability of a step upstep_up_p = 0.5 * (1 + (CONSTANTS.alpha-CONSTANTS.sigma^2/2)*d_t/d_Y);TREE.step_up_p = step_up_p;% Values at the end point which is the decision point (possible values of X at decision point)TREE.Y(1,1)= CONSTANTS.bintree_init;for k=2:1:CONSTANTS.num_period +1TREE.K(k) = (k-1)*d_t;for m=1:1:kTREE.Y(m,k) = TREE.Y(1,1) + (k-1)*d_Y - 2*(m-1)*d_Y;endendfor k=1:1:CONSTANTS.num_period+1for m=k+1:1:CONSTANTS.num_period+1TREE.Y(m,k) = NaN;endendTREE.X = exp(TREE.Y);--------------------------------------------------------------------------------------------------------------------------------------------------------------------- GENERATION OF NORMAL PROBA -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------function [NormalProba] = GenNormalProba(CONSTANTS,TREE)% Period lengthd_t= CONSTANTS.Tlife_y/CONSTANTS.num_period;NormalProba.dt = d_t;% Probability of going from (m,k,j)/normal to (m,k+1,j)/normalNormalProba.P1=(1-CONSTANTS.lambda(1)*d_t)*TREE.step_up_p;% Probability of going from (m,k,j)/normal to (m,k+1,j)/disruptionNormalProba.P2=CONSTANTS.lambda(1)*d_t*TREE.step_up_p;91