Problem 2 Simulation

Concerned about recent weather-related disasters, fires, and other calamities at universities around the country, university administrators at DIU Dresden have initiated several planning projects to determine how effectively local emergency facilities can handle such situations. One of these projects has focused on the transport of disaster victims from campus to the five major hospitals in the area: Hospital Friedrichstadt, Hospital Neustadt, Hospital St. Joseph, Hospital Carl Carus, and Hospital Diakonissen. The project team would like to determine how many victims each hospital might expect in a disaster and how long it would take to transport victims to the hospitals. However, one of the problems the project team faces is the lack of data on disasters, since they occur so infrequently. The project team has looked at disasters at other schools and has estimated that the minimum number of victims that would qualify an event as a disaster for the purpose of initiating a disaster plan is 10. The team has further estimated that the largest number of victims in any disaster would be 200, and based on limited data from other schools, they believe the most likely number of disaster victims is approximately 50. Because of the lack of data, it is assumed

that these parameters best define a triangular distribution. The emergency facilities and ca- pabilities at the five area hospitals vary. It has been estimated that in the event of a disaster

situation, the victims should be dispersed to the hospitals on a percentage basis based on the

hospitals' relative emergency capabilities, as follows: 25% should be sent to Hospital Frie- drichstadt (FR), 30% to Hospital Neustadt (NR), 15% to Hospital St. Joseph (SF), 10% to Hospital

Carl Carus (CC), and 20% to Hospital Diakonissen (UMC). The proximity of the hospitals to DIU

Dresden also varies. It is estimated that transport times to each of the hospitals is exponen- tially distributed with an average time of the minutes given in the table below. Use the time

in the row of your DIU ID. (It is assumed that each hospital has two emergency vehicles, so

that one leaves DIU Dresden when the other leaves the hospital, and consequently, one ar- rives at DIU Dresden when the other arrives at the hospital. Thus, the total transport time will

be the sum of transporting each victim to a specific hospital.)

Problem 2 Simulation

Hospital (FR) (NR) (SF) (CC) (UMC)

DIU ID 2023-2 7012950 4 13 23 22 16 7012951 8 9 23 18 12 7013311 7 10 23 16 14 7013729 8 11 19 22 16 7013730 6 12 20 23 13 7013827 8 9 20 21 17 7013864 8 13 20 17 13 7013927 8 8 15 22 16 7013999 6 10 20 15 15 7014018 7 8 21 18 13 7014028 8 11 16 16 15 7014035 7 13 17 17 13 7014036 7 13 21 23 13 7014055 4 13 19 16 13 7014066 6 12 15 19 15 7014071 8 10 23 20 17 7014093 4 11 21 17 16 7014094 7 11 17 20 14 7014114 7 8 23 23 16 7014123 4 13 18 15 14 7014139 5 12 18 24 14 7014154 4 9 15 17 16 7014199 8 13 15 21 16 7014201 7 8 18 17 14 7014204 5 9 20 24 13 7014225 5 8 22 17 16 7014230 8 10 21 24 12 7014230 7 13 23 22 15 7014258 8 12 24 25 12 7014291 4 10 22 20 16 2023-1 7013298 4 13 16 24 14 7013806 6 11 22 19 15 7013807 4 11 25 20 16 2022-2 7013312 5 8 25 25 13 7013469 6 8 17 20 17

Problem 2 Simulation A. Perform a simulation analysis (1000 runs) to determine the average number of victims that can be expected at each hospital. And only 1 simulation run to calculate the average total time required to transport victims to each hospital.

B. Suppose that the project team believes they cannot confidently assume that the transpor- tation time to the hospitals follow an exponentially distribution using the parameters they

have estimated. Instead, they believe that the transportation time to the hospitals follow an exponentially distribution, now they assume that the transportation time follows a normal distribution with the following parameters for each hospital:

Perform a simulation analysis using this revised information.

Hospital mean std. dev. mean std. dev. mean std. dev. mean std. dev. mean std. dev.

DIU ID 2023-2 8 2 12 6 23 10 20 11 15 3 8 2 9 2 21 9 22 9 18 6 7012950 7 5 12 5 22 8 25 10 18 5 7012951 8 2 14 4 23 12 19 11 12 3 7013311 8 3 12 2 23 7 17 10 15 7 7013729 7 3 13 4 18 10 18 8 18 6 7013730 8 3 11 5 23 12 19 10 12 4 7013827 8 3 10 3 23 9 24 12 18 5 7013864 7 4 9 3 17 11 23 11 15 3 7013927 6 2 12 3 19 11 17 9 13 6 7013999 7 2 14 3 25 8 24 8 16 5 7014018 6 5 11 2 18 11 24 12 16 6 7014028 8 4 9 5 21 10 20 11 17 3 7014035 8 3 14 5 20 12 23 8 14 5 7014036 7 2 9 3 19 12 23 10 18 3 7014055 8 2 12 4 24 9 25 9 17 5 7014066 8 3 11 3 24 11 17 11 18 3 7014071 7 5 12 5 17 12 17 8 18 7 7014093 7 5 14 3 17 11 25 10 17 5 7014094 8 4 13 5 24 8 24 8 13 6 7014114 7 2 11 4 25 9 20 12 15 4 7014123 8 3 13 2 24 12 17 12 18 6 7014139 7 4 9 3 19 7 17 11 17 5 7014154 6 5 14 6 25 9 17 11 14 5 7014199 7 4 12 2 22 11 23 11 18 4 7014201 6 3 13 6 19 7 18 10 15 7 7014204 6 2 14 3 20 12 21 8 18 5 7014225 6 5 10 2 19 12 18 9 15 5 7014230 7 5 13 2 25 9 23 12 13 7 7014230 7 2 10 3 22 10 24 12 13 6 7014258 6 2 9 4 24 10 25 10 18 4 7014291 7 2 9 4 23 12 23 10 16 6 2023-1 7013298 7 3 10 6 25 9 24 11 17 4 7013806 6 3 12 5 17 10 20 12 15 4 7013807 8 2 9 4 20 9 25 11 14 5 2022-2 7013312 7 2 13 4 21 7 22 8 16 4 7013469 7 2 13 4 17 12 18 11 13 4 (FR) (NR) (SF) (CC) (UMC)

Problem 2 Simulation

C. Discuss how this information might be used for planning purposes. How might the simula- tion be altered or changed to provide additional useful information?