Optimal Solution Obtained is Infeasible!

Originally posted by: Bahman

Hello Everyone,

I am solving a simple LP, where I have obtained the optimal solution. However, one the process of solving an stochastic model, I have to solve the model again with fixed vector of optimal first stage variables. Thus, I ran the code, obtained the optimal vector, fixed it as a data (float, rather than a dvar) then, solved it again. It shows conflicts and relaxations.

I wonder, how an optimal solution to a problem can be infeasible if we fix it as a given vector.

MWE

{int} C = asSet(1..4);  {int} T = asSet(1..5); 

float r[C] = [0.0, 250, 510, 700];                                                {int} S = asSet(1..2); 

float p[S] = [1/3, 2/3];  float intact[S] = [0.90, 0.90]; 

dvar float+ x[C][T][S];  dvar float+ y[C][T][S];

maximize sum(t in T, s in S, c in C) p[s] * r[c] * x[c][t][s];  

forall(s in S, c in C)   ct0: x[c][1][s]