I am trying to solve Logarithmic Fuzzy Preference Programming (LFPP) for criteria weight evaluation, based on fuzzy comparisons between criteria, and I am solving it with Gurobi in Python 2.7. It is a basic operational research thing. The model that solves this kind of problems is very simple ($n$ is the number of the criteria): The problem is that Gurobi isn't able to solve this accurately. I have some proven solutions for a specific configuration, but my program does not yield the adequate solution. Here is the literature example that i am trying to solve: The $x_i$ values from my models' output are very large (for example 420699.256539 instead of 0.6). Does anyone has an advice how to approach this? I have solved many complex and more complicated MILP, MIQP and MP problems so far, but never had an issue like this.