The investment group also wants to determine the optimal size of a hotel. The table below shows probabilities of generally reserved number of hotel rooms. Each room will be rented for $100. Statistics from the hospitality industry show that there is a 0.08 probability that guests that have booked a room close to an amusement park will not show up. On the other hand, there is also a 0.25 probability of last-minute reservations, i.e. the chance to rent out a room that was reserved but had a no-show. These rooms bring in $70. If a hotel is overbooked and a guest cannot get a room, $250 have to be paid to set up the guest in a different facility. Any room not sold results in $40 in variable costs. What is the optimal size of a hotel, in order to maximize daily profit? (Note: any other costs are ignored for this model). Note: You can solve this using PSISIMparam Rooms reserved 50 51 52 53 54 55 56 57 58 Probability 0.05 0.15 0.18 0.18 0.19 0.1 0.08 0.04

2) The investment group also wants to determine the optimal size of a hotel. The table below shows probabilities of generally reserved number of hotel rooms. Each room will be rented for $100. Statistics from the hospitality industry show that there is a 0.08 probability that guests that have booked a room close to an amusement park will not show up (and the hotel will lose the $100). On the other hand, there is also a 0.25 probability of last-minute reservations, i.e., the chance to rent out a room that was reserved but had a no-show. These rooms bring in $70. If a hotel is overbooked and a guest cannot get a room, $250 have to be paid to set up the guest in a different facility. Any room not sold results in $40 in variable costs. What is the optimal size of a hotel, in order to maximize daily profit? (Note: any other costs are ignored for this model). Note: You can solve this either as an LP-Optimization problem or using PSISIMparam (one approach is sufficient). Rooms 50 51 52 53 54 55 56 57 58 reserved Probability 0.05 0.15 0.18 0.18 0.19 0.1 0.08 0.04 0.03