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# robust optimisation

 P: n/a 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 Aug 5 '08 #1
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 P: n/a sh**************@gmail.com wrote: Dear all, I have a LP model here as follow:... 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? This has nothing to do with Python. You might get an answer on the scipy mailing list, but you should look for a group of list on numerical optimization, or better one specifically on linear programming, which is a somewhat separate subfield. Or find a book on 'robust optimization', whatever that is. tjr Aug 5 '08 #2

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