Dear all, I have a LP model here as follow:
Min = .42*x1 + .56*x2 + .70*x3; S.t. x1 + x2 + x3 = 900; x1 <= 400 * y1; x2 <= 700 * y2; x3 <= 600 * y3; 30*x1 <= 12500; 40*x2 <= 20000; 50*x3 <=15000; .15*x1 + .2*x2 +.15*x3 >= 100; .2*x1 + .05*x2 + .2*x3 >= 100; .25*x1 + .15*x2+ .05*x3 >= 150; y1+y2+y3 = 2; xi>=0, yi=0, if x=o yi=1, if x>=o The constraints .15*x1 + .2*x2 +.15*x3 >= 100; .2*x1 + .05*x2 + .2*x3 >= 100; .25*x1 + .15*x2+ .05*x3 >= 150; have uncertainties in x1, x2, and x3 coefficients. I want to know how can I make a robust optimisation model for this LP model? for example, if we know that all the coefficients have variations about 30%. Thank you, Shab -- http://mail.python.org/mailman/listinfo/python-list
