I want to make a model to solve two interrelated minimization problems. Both problems are largely the same: they are defined in terms of the same sets, are parametrized by the same data, have the same decision variables and largely the same constraints. The only differences between the two problems are:
1. They have different objective functions, let's call them f1 and f2 for problems 1 and 2. 2. Problem 2 has one additional constraint: f1's value in problem 2's solution cannot be more than 10% greater than f1's value in problem 1's solution. Summarizing, problem 1 is: minimize obj1: f1; s.t. a bunch of constraints; And problem 2 is: minimize obj2: f2; s.t. a bunch of constraints; # the same as in problem 1 s.t. deviation: f1 <= 1.1 * (problem 1's optimum); Is there any way to handle these two optimization problems using a single model file? -- Eduardo León
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