[논문]Life Cycle Costing: Maintenance and Repair Costs of Hospital Facilities Using Monte Carlo Simulation

Kim, Tae-Hui; Choi, Jong-Soo; Park, Young Jun; Son, Kiyoung

doi:10.5345/jkibc.2013.13.6.541

Life Cycle Costing: Maintenance and Repair Costs of Hospital Facilities Using Monte Carlo Simulation 원문보기

한국건축시공학회지 = Journal of the Korea Institute of Building Construction, v.13 no.6, 2013년, pp.541 - 548

Kim, Tae-Hui (Department of Architectural Engineering, Mokpo National University) , Choi, Jong-Soo (Department of Architectural Engineering, Dongguk University-Seoul) , Park, Young Jun (Department of Construction Engineering and Environmental Science, Korea Military Academy) , Son, Kiyoung (School of Architecture, University of Ulsan)

Abstract ▼ AI-Helper

During the administration of a construction project, various types of participants are engaged in the project. From the design phase to the maintenance phase, these participants may confront many risks. To avoid these risks, participants should utilize an insurance company or a bond company. The types of risks and liability that a construction manager may face are listed in the construction law or contract. But there are some arguments related to risk transferring and the content of risks. For this reason, construction managers must carefully consider any possible risks in the contract and the construction law. Therefore, for construction managers to deal with risks appropriately, the introduction of a legal requirement to carry professional liability insurance, a defined compensation range for damages, a method of guarantee in the event of defects, a defined compensation claim period for damage, and a method of damage claim were suggested in this study.

주제어

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문제 정의

The findings of this study can provide a standard for M&R costs to help major stakeholders make better-informed decisions for anticipating the future costs of ownership of facilities.
Therefore, the main objective of the study is to develop a predictive model for M&R costs of hospital facilities.
To address this shortcoming, the objective of this study is to propose a predictive model for M&R costs using log-normal distribution method.

가설 설정

3) The standard deviations of inflation and dis–count factors are assumed as one–third of the mean. To prove whether or not there is difference according to different standard de–viation of unscheduled maintenance costs, the sensitivity analysis is conducted with different standard deviations (one–third, one–fifth, one–tenth, one–second) of inflation and discount factors.

제안 방법

To develop a predictive model, a Monte Carlo simulation is performed to analyze how M&R costs are affected given various considered factors, based on the assumption established in this study, which is that the annual costs of M&R for each building system in a hospital facility are normally distributed. Finally, the validity of the results is examined by performing several sensitivity analysis tests.
In order to accomplish the objectives of this study, profile estimates of 50–year maintenance cost for a hospital facility were used.
[7] compared the economic impact of two different roof systems, a green and a built–up roof, by using NPV calculation. In their research, a probabilistic method was used to more precisely predict the life cycle cost of these two different roof systems. By using Monte Carlo simulation, the results showed the variation range of the life cycle costs for each roof system.
In this study, the log-normal distribution was used to predict the M&R costs of a research hospital facility.
To accomplish this objective, the study consists of three parts: (1) predicting the costs of each major building system; (2) predicting costs related to four types of typical M&R activities: preventive, major, replacement, and unscheduled expenses; and (3) analyzing the distribution of Net Present Value (NPV) in expected M&R costs.
To develop a predictive model, a Monte Carlo simulation is performed to analyze how M&R costs are affected given various considered factors, based on the assumption established in this study, which is that the annual costs of M&R for each building system in a hospital facility are normally distributed.
3) The standard deviations of inflation and dis–count factors are assumed as one–third of the mean. To prove whether or not there is difference according to different standard de–viation of unscheduled maintenance costs, the sensitivity analysis is conducted with different standard deviations (one–third, one–fifth, one–tenth, one–second) of inflation and discount factors.

이론/모형

In this respect, the log–normal distribution method is used in this study to predict the M&R costs, since the costs have the same probability at the relative flow of prices like the stock market.
In this study, the log-normal distribution method is applied to estimate the M&R costs for 50 years with unscheduled maintenance costs.

성능/효과

2) Inflation and discount factor are assumed as the mean of 3% and 8%, respectively.
3) Monte Carlo simulations are performed to analyze the distribution of NPV for each of the ten major building systems for each type of activity, and for the combined M&R cost.

참고문헌 (11)

Fuller S. [Internet]. Washington D.C: life-cycle cost analysis(LCCA); 2010 [Updated 2010 Jun 28; cited 2012 Aug 23]. WBDG [1 screen]. Available from: http://www.wbdg.org/resources/lcca.php.
Rahmen S, Vanier DJ. Life cycle cost analysis as a decision support tool for managing municipal infrastructure. CIB 2004 Triennial Congress. 2004 May;2(1):11-8.
Dellisola AJ, Kirk SJ. [Internet]. What is life cycle costing?; 2010 [Updated 2010 Mar 3; cited 2012 Sept 12]. REED Construction Data [1 screen]. Available from: http://www.reedconstructiondata.com/rsmeans/articles/what-is-life-cycle-costing.
Kumar D, Setunge S, Patnaikuni I. Prediction of life-cycle expenditure for different categories of council buildings. Journal of Performance of Constructed Facilities. 2010 Dec;24(6):556-61.

상세보기
Kong JS, Frangopol DM. Life cycle reliability-based maintenance cost optimization of deteriorating structures with emphasis on bridges. Journal of Structure Engineering. 2003 Jun;129(6):818-28.

상세보기
Tighe S. Guidelines for probabilistic pavement life cycle cost analysis. Transportation research record, 2001 Feb; 179(1):28-35.
Muga H, Mukherjee A, Mihelcic J. An integrated assessment of the sustainability of green and built-up roofs. Journal of Green Building. 2008 Mar;3(2):106-27.

상세보기
Herbold K. Using monte carlo simulation for pavement cost analysis. Public Roads, 2000 Dec;63(3):11-23.
Norman JL, Smuel K, Balakrishnan N. Probability and mathematical statistics: applied probability and statistics. 2nd ed. New York: John Wiley & Sons; 1994. p. 24-5.
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상세보기
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