Risk-based life cycle cost analysis using a two-level multi-objective genetic algorithm

Figures & data

Table 1. Sample of generated operating cost (OC) data (Al-Chalabi 2018)

Fan number	Fan’s power (kW)	Working hours (h)	Energy price (SEK/kWh)	Operating cost/fan.day (SEK)
1	70	7.9	2.88	1593
2	75	6	2.88	1295
3	32	7.5	2.88	695
4	75	7.6	2.88	1654
5	60	7.7	2.88	1332
6	30	6.2	2.88	539
7	40	6.8	2.88	790
8	55	6.52	2.88	1034
9	78	7.56	2.88	1700
No. of days	OC/day (SEK)	Months-year	No. of months	OC/month (SEK)
1	128621	Apr-05	1	3856485
2	129322	Maj-05	2	3931435
3	129725	Jun-05	3	3855434
4	120832	Jul-05	4	3793487
5	132193	Aug-05	5	3870663
6	134786	Sep-05	6	3846868
7	122774	Okt-05	7	3828002
8	131767	Nov-05	8	3839732
9	13307	Dec-05	9	3822851

Figure 1. Two-level system of MOGAs

Figure 2. Normal distribution of cumulative TTF

Figure 3. Risk cost estimation

Figure 4. ORT of 121 fans as one system with a 2% risk cost

Figure 5. ORT of 121 fans as one system with a 250% risk cost

Figure 6. Risk cost analysis of 121 fans as one system

Figure 7. ORT of 121 fans as one system for the optimal result

Figure 8. Example of worst ORT of 121 fans as one system

Al-Chalabi, H. 2018. “Life Cycle Cost Analysis of the Ventilation System in Stockholm’s Road Tunnels.” Journal of Quality in Maintenance Engineering 24 (3): 358–375. (just-accepted), 00-00. doi: 10.1108/JQME-05-2017-0032.

 Web of Science ®Google Scholar