建築設備會因運轉而逐漸退化，因而增加故障的機率或降低設備的可靠性。由於退化是不可避免的，故多數的設備並無法一直維持在最佳的狀態下運轉。因此必須透過設備的維護管理，使設備能持續保持其應有的功能，進而延長其使用年限。然而，維護會增加維護成本，而在資源有限的情況下，設備維護經費往往有限；因此維護設備必須建立一套有效益的維護策略，在最低性能要求與預算的限制下，達到提升設備性能與降低維護成本之目標。
設備性能隨時間之變化，往往難以衡量。在性能退化方面，不同時期之性能退化不盡相同，即設備性能與維修時間呈非線性關係。另外，採用不同的維修方式於同一個設備，其維修效果也不盡相同。因此，建築設備維護策略必須考慮維修間隔時間與維修方式造成設備性能之變化。在過去的研究中，已有採用數學規劃解法來模擬性能退化並求解最佳維護策略。在模擬性能變化方面，由於其在性能退化過程中具有一定的不確定性，故常與實際性能變化曲線產生偏差；在求解最佳維護策略方面，由於其模式建構費時而導致求解效率不佳，特別是應用於大型且複雜的模式中。
因此有必要建立一符合建築設備需求的維護策略最佳化模式。首先，建立一建築設備維護策略模型，考慮維修間隔時間與維修方式造成設備性能與維護成本之影響；再利用馬可夫鏈理論建立性能轉換機率矩陣以模擬性能變化，並建立一維護策略效益評估方法；最後再以基因演算法快速求解出最佳維護策略。
從研究成果顯示，經由本研究模式能有效達到建築設備維護管理之需求，使管理者能了解採用何種維修方式並於何時執行維修，而納入維護管理計畫中並實施於維護管理作業程序中。另外，本研究並開發一電腦應用程式，以輔助管理者在簡易操作介面中進行上述程序。

Building facilities start to deteriorate after they are put in operation. In addition, building facilities must be operated at certain level to provide adequate service. Because of deterioration, building facilities could not perform well as planned if they are not properly maintained. Since deterioration is inevitable and maintenance requires certain amount of money, it is necessary for the building manager to develop a maintenance strategy that can keep facilities functional without going over the budget. However, the status of the building facility usually varies with time and is hard to predict. Moreover, different building facilities have different deterioration rates which would change over time according to the time interval and the maintenance method applied. Therefore, developing a maintenance strategy is not an easy task.
Previous research on developing the maintenance strategy mostly took mathematical programming approach, which showed inefficient for large-scale problems in terms of computation efforts. In addition, there is lack of quantified model for predicting the status of the building facility. Therefore, it is needed to develop a maintenance model not only can estimate the status of the facility and the status after it has been but also minimize the cost of maintaining facilities.
This research presents a model that develops the optimal maintenance strategy for building facilities by minimizing the net present value of the money spent over the planning horizon. At first the status prediction model of the building facility is developed according to the theory of the Markov-chain. This status prediction model is presented in the form of a transition probability matrix, in which the current status of any facility depends on the previous state of that particular facility. In addition, the status of the facility after being maintained is determined by the maintenance method applied. Then, an optimization model that incorporates the genetic algorithms and the aforementioned status model is developed.
Results show that the proposed optimal maintenance strategy along with the computer implementation can provide an easy-to-use interface and satisfactory results for building managers to develop the optimal maintenance strategy which determines how long each facility which be maintained by what method.

1. Canfield, R. V. (1986). “Cost Optimization of Periodic Preventive Maintenance.” IEEE Transactions on Reliability, Vol.R-35, No.1.

2. Chen, C. T., Chen, Y. W., and Yuan, J. (2003). “On a Dynamic Preventive Maintenance Policy for a System under Inspection.” Reliability Engineering and System Safety, 80, 41-47.

3. Chun, Y. H. (1992). “Optimal Number of Periodic Preventive Maintenance Operations under Warranty.” Reliability Engineering and System Safety, 37, pp.223-225.

4. Duffuaa, S. O., Raouf, A., and Campbell, J. D. (1999), Planning and Control of Maintenance System: Modeling and Analysis, John Wiley & Sons, New York.

5. Ferreira, A., Antunes, A., and Picado-Santos, L. (2002). “Probabilistic segment-linked pavement management optimization model.” Journal of Transportation Engineering, ASCE 128 (6) 568– 577.

6. Fwa, T. F., Tan, C. Y., and Chan, W. T. (1994). “Road-maintenance planning using genetic algorithms II: analysis.” Journal of Transportation Engineering, ASCE 120 (5) 710– 722.

7. Fwa, T. F., Chan, W. T., and Hoque, K. Z. (2000). “Multiobjective optimization for pavement maintenance programming.” Journal of Transportation Engineering, ASCE 126 (5) 367–374.

8. Gen, M. and Cheng, R. (1997), Genetic algorithms and engineering design, A Wiley-Interscience Publication, New York.

9. Golabi, K. and Shepard, R. (1997). “Pontis: a system for maintenance optimization and improvement of US Bridge Networks.” Interfaces, 27 71–88.

10. Jayabalan, V. and Chaudhuri, D. (1992). ”Cost Optimization of Maintenance Scheduling for a System with Assured Reliability.” IEEE Transactions on Reliability, Vol.41, No.1.

11. Lie, C. H. and Chun, Y. H. (1986). “An Algorithm for Preventive Maintenance Policy.” IEEE Transactions on Reliability, Vol.R-35, NO.1.

12. Liu, C., Hammad, A., and Itoh, Y. (1997). “Maintenance strategy optimization of bridge decks using genetic algorithm.” Journal of Transportation Engineering, ASCE 123 (2) 91– 100.

13. Michalewicz, Z. (1996), Genetic Algorithms + Data Structures = Evolution Programs, 3rd ED. Springer, New York.

14. Miyamoto, A., Kawamura, K., and Makamura, H. (2000). “Bridge management system and maintenance optimization for existing bridges.” Computer-Aided Civil and Infrastructure Engineering, 15 45–55.

15. Morcousa, G.. and Lounis, Z. (2005). “Maintenance optimization of infrastructure networks using genetic algorithms.” Automation in Construction, 14, 129-142.

16. Park, D. H., Jung, G. M., and Yum, J. K. (2000). “Cost Minimization for Periodic Maintenance Policy of a System Subject to Slow Degradation.” Reliability Engineering and System Safety, 68, pp. 105-112.

17. Pham, H. and Wang, H. (1996). “Optimal Maintenance Policies for Several Imperfect Maintenance Models.” International Journal of Systems Science (to appear).

18. Tsai, Y. T., Wang, K. S., and Teng, H. Y. (2001). “Optimizing Preventive Maintenance for Mechanical Components Using Genetic Algorithms.” Reliability Engineering and System Safety, 74, pp.89-97.

19. Wang. H. (2002). “A survey of maintenance policies of deteriorating systems.” European Journal of Operational Research, 139, pp. 469-489.

20. Wayne, W. L. (1994), Operations Research: Application and Algorithms, 3rd ED, Duxbury Press, New York.

21. 宋欣財，專案排程趕工決策模式，國立成功大學土木工程研究所碩士論文，民國92年。

22. 林信成，精通Visual Basic 6程式設計，第三波資訊股份有限公司，台北，民國89年。

23. 紀冠安，考慮成本與可靠度之不完全預防維護模型，朝陽科技大學工業工程與管理研究所碩士論文，民國90年。

24. 高孔廉，作業研究-管理決策之數量方法，三民總經銷，台北，民國74年。

25. 陳義分、楊展耀、簡進嘉，作業研究，全華科技圖書公司，台北，民國87年。

26. 莊瑞南，馬可夫過程在技術成長上之研究，國立中央大學機械工程研究所碩士論文，民國92年。

27. 莊嘉文，建築設備概論:電氣設備、空氣調節設備、給排水設備、輸送設備、消防設備，詹式書局，台北，民國75年。

28. 楊朝順，考慮失效率限制之週期預防維護模型研究，朝陽科技大學工業工程與管理系碩士論文，民國92年。

29. 劉明國，建築物用途別之分類研究及建築物之使用管理，中華民國建築學會，台北，民國76年。

30. 劉賓陽，作業研究，三民書局，台北，民國89年。

31. 鄭達才，設備維護管理：現在與未來，中國生產力中心，台北，民國89年。

32. 韓夢麟，服務復原策略效益評估模式建立之研究-以馬可夫鏈為分析工具，中原大學企業管理研究所碩士論文，民國90年。