1. The objective function (you can use after the first 2 x_ijs and before the last 2 x_ijs) if customers may be dropped by the manufacturer

2. The basic (i.e., shipping) constraint for plant 4

3. The basic (i.e., demand) constraint for customer E assuming all customers will continue receiving shipments

4. The demand constraint for customer D assuming that customers may be dropped

A manufacturer of face masks mass produces them at 4 of its plants for its 5 different retail customers. Unfortunately, it is not able to produce enough face masks to meet the demands of all its customers and it is now considering no longer producing these face masks for all 5 of these customers. Currently, the following table contains most of the information necessary to make manufacturing decisions for the upcoming planning period (3 months)... Profits/1000 masks ($): Plant retail customer A B CD E capacities (1000's). 600 585 2 640 70. 3 850 740 730 4 700 720 620 795 Demands (1000's) 90 1 810 40 675 725 725 785 730 655 725 620 600 70 95. 45. 80 100 60. Additional information: it costs $1500 per planning period for keeping customers A and B as customers and $1000 per period for keeping customers C, D and E.. a customer must be guaranteed at least 1000 masks if it is not dropped from the manufacturer. Using X's (in 1000's) as the amounts shipped from planti (row i) to customer j (columnj), and A, B, C, D and E as the variables to determine whether or not to keep customers A, B, ... , formulate the following in order to maximize total profits