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In PC Tech's product mix problem, assume there is another PC model, the VXP, that the company can produce in addition to Basics and XPs. Each VXP requires eight hours for assembling, three hours for testing, \$275 for component parts, and sells for $560. At most 50 VXPs can be sold. 1. Modify the spreadsheet model to include this new product, and use Solver to find the optimal product mix. 2. You should find that the optimal solution is not integer-valued. If you round the values in the changing cells to the nearest integers, is the resulting solution still feasible? If not, how might you obtain a feasible solution that is at least close to optimal? a) You need to modify the spreadsheet provided to include the VXP computers. Then you need to find the new optimal solution. I will look at the optimal number of computers to produce and at the value of the objective function. (10 points). b) Round the values of the optimal number of computers from part a. Is the solution still feasible? Basically check if the number of hours used is still less than the number of hours available ( 5 points). If the rounded solution was not feasible, look for integer values closed to the ones obtained on part a (by trial and error), until you find an optimal combination that maximizes the value of the objective function AND is feasible. (5 points). In PC Tech's product mix problem, assume there is another PC model, the VXP, that the company can produce in addition to Basics and XPs. Each VXP requires eight hours for assembling, three hours for testing, \$275 for component parts, and sells for $560. At most 50 VXPs can be sold. 1. Modify the spreadsheet model to include this new product, and use Solver to find the optimal product mix. 2. You should find that the optimal solution is not integer-valued. If you round the values in the changing cells to the nearest integers, is the resulting solution still feasible? If not, how might you obtain a feasible solution that is at least close to optimal? a) You need to modify the spreadsheet provided to include the VXP computers. Then you need to find the new optimal solution. I will look at the optimal number of computers to produce and at the value of the objective function. (10 points). b) Round the values of the optimal number of computers from part a. Is the solution still feasible? Basically check if the number of hours used is still less than the number of hours available ( 5 points). If the rounded solution was not feasible, look for integer values closed to the ones obtained on part a (by trial and error), until you find an optimal combination that maximizes the value of the objective function AND is feasible. (5 points)