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Comet Dry Cleaners specializes in same-day dry cleaning. Customers drop off their garments early in the morning and expect them to be ready for pickup on their way home from work. There is a risk, however, that not all garment will be cleaned on the same day that they are dropped off. If a garment is held overnight, it will cost the company $25 per garment. The manager is contemplating whether they should expand the facility to reduce or total eliminate backlog. To determine if it is cost-effective to expand the facility, the owner wants to determine if the cost of expansion is consistently less than the holding cost per day. In terms of the company’s operations, there are two random events. The first is that the company does not know how many garments will be brought in daily for cleaning. The second is that the company does not know how many garments can actually be processed in a given day. Therefore, the owners have collected data and have provided the following probability distribution tables. 1. Based on the Daily Garment Demand Probability Table, calculate the lower and upper bounds of the probability distribution. 2. Based on the Daily Processing Capacity Table, calculate the lower and upper bounds of the probability distribution. 3. Create a simulation for Comet Dry Cleaners for 30 days. Use the probability distribution tables to generate values for Daily Garment Demand and Daily Processing Capacity randomly. Assume for the first day of the simulation, that zero garments are in the queue. HINT: You are free to use IF, MIN, or MAX formulas for the remaining formulas that are needed for the simulation. Be sure to calculate Holding Cost. This cost should be charged on the day the unit was not completed. In other words, if 10 garments were in the queue at the end of the first day, then the Holding Cost for day 1 will be 10*$25, or $250.