A manufacturing system contains m machines, each subject to randomly occurring breakdowns. A machine runs for an amount of time that is an exponential random variable with mean 8 hours before breaking down. There are s (where s is a fixed, positive integer) repairmen to fix broken machines, and it takes one repairman an exponential amount of time with mean 2 hours to complete the repair of one machine; no more than one repairman can be assigned to work on a broken machine even if there are other idle repairmen. If more than s machines are broken down at a given time, they form a FIFO “repair” queue and wait for the first available repairman. Further, a repairman works on a broken machine until it is fixed, regardless of what else is happening in the system. Assume that it costs the system $50 for each hour that each machine is broken down and $10 an hour to employ each repairman. (The repairmen are paid an hourly wage regardless of whether they are actually working.) Assume that m = 5, but write general code to accommodate a value of m as high as 20 by changing an input parameter. Simulate the system for exactly 800 hours for each of the employment policies s = 1, 2, …, 5 to determine which policy results in the smallest expected average cost per hour. Assume that at time 0 all machines have just been “freshly” repaired.
We need at least 10 more requests to produce the solution.
0 / 10 have requested this problem solution
The more requests, the faster the answer.