Examples |

Example 1.Policy 1 is selected in the calculation with imperfect repair cost C _{q}=863.0 and replacement cost C_{0}=6000. |

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 C

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 C_{q}=863.0 and replacement cost C_{0}=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 C_{0}=6000.0 and repair cost C_{q}=863.0

Number of Monte Carlo trials is 10000

**Output:**

Waiting time before last failure T_{3}^{∗}: 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.