Examples
Example 1.
Policy 1 is selected in the calculation with imperfect repair cost Cq=863.0 and replacement cost C0=6000.
Input:
Weibull shape parameter beta=1.64; Scale parameter eta=1.0;
Kijima model 1 with restoration factor q=0.5
Maintenance policy 1 was selected
with replacement cost C0=6000.0 and repair cost Cq=863.0
Number of Monte Carlo trials is 10000

Output:
Length of cycle: 5.4484267; Number of failures: 11.5118;
Minimal cost per unit time: 2921.72; Corresponding standard error: 4.7144675

According to the calculated result, the item should be replaced by a new one at time 5.4484267.
Expected number of failures at this time is 11.5118.
Expected minimal cost per unit time is 2921.72. It is calculated by the Monte Carlo method with standard error 5.4484267.

Example 2.
Policy 3 is selected in this calculation with the same input as above: imperfect repair cost Cq=863.0 and replacement cost C0=6000.
Input:
Weibull shape parameter beta=1.64; Scale parameter eta=1.0;
Kijima model 1 with restoration factor q=0.5
Maintenance policy 3 was selected
with replacement cost C0=6000.0 and repair cost Cq=863.0
Number of Monte Carlo trials is 10000

Output:
Waiting time before last failure T3: 5.130092; Length of cycle: 5.4274025;
Minimal cost per unit time: 2779.8958; Corresponding standard error: 5.0198402

According to the obtained result, the item should be replaced at the first failure after time 5.130092.
Expected time to this failure is 5.4274025.
Expected minimal cost per unit time is 2779.8958, which is 6% less than in Example 1 (Policy 1).
It is calculated by the Monte Carlo method with standard error 5.4484267.