Question: TRUE OR FALSE (15*2 points = 30 points) (1) If all the values of the input variables in a decision model are known with certainty,

TRUE OR FALSE (15*2 points = 30 points) (1) If all the values of the input variables in a decision model are known with certainty, then the model is considered to be deterministic. (2) A decision model has the following input variables: projected sales data and historical advertising budget. This model is considered to be deterministic. (3) Suppose that the break-even point (BEP) for a given product is 200 units. This means that if 198 units are produced, then this product is not profitable. (4) A linear programming problem has the following two constraints: X1 s 20 and X12 25. This problem is infeasible. (5) It is possible to solve graphically a linear programming model with 4 decision variables. (6) When using Solver, the parameter Changing Cells is typically associated with the objective function (7) Consider the following constraint: X12 40. This implies that at most 40 units of product X1 can be produced. (8) The Transportation" problem involves determining the amount of goods to be transported from a number of origins to a number of destinations. Optimal solutions to linear programming problems are found under probabilistic assumptions. (10) The Sensitivity Report has two parts one part deals with objective function coefficient changes, and the other part deals with constraint coefficient changes, (11) The information provided in the Sensitivity Report assumes that we are considering a change to only a single input data value (12) The set of solution points that satisfies all of a linear programming problem's constraints 34 #*2020/2021 ******** simultaneously is defined as the feasible region in graphical linear programming. (13) An objective function is necessary in a maximization problem but is not required in a minimization problem (14) Sensitivity analysis enables us to look at the effects of changing the coefficients in the objective function, one at a time. (15) The set of solution points that satisfies all of a linear programming problem's constraints simultaneously is defined as the feasible region in graphical linear programming

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