Implement the following LP Minimization model in Python.

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S$.$t$.$

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a$)(10$ points$)$ Report the optimal solution for decision variables and the objective value.

b$)(10$ points$)$ Report and interpret the reduced cost$[1]$ value of each decision variable.

$[1]$ Reduced cost can be positive or negative. For both maximization or minimization the mechanism is the same. The interpretation is based on changing: the coefficient of interest in the objective function minus the reduced cost. So if reduced cost is negative, we add to the coefficient, and if the reduced cost is positive we subtract from the coefficient to make the associated decision variable cost$-$efficient $($or profitable$).$

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