HANDLING UNCERTAIN INFORMATION IN WHOLE LIFE COSTING - A COMPARATIVE STUDY
|
|
- Mildred Cross
- 6 years ago
- Views:
Transcription
1 HANDLING UNCERTAIN INFORMATION IN WHOLE LIFE COSTING - A COMPARATIVE STUDY Mohammed Kishk, Assem Al-Hajj and Robert Pollock Scott Sutherland School, The Robert Gordon University, Aberdeen AB10 7QB, UK. ABSTRACT A number of recently developed algorithms to handle uncertain information in whole life costing (WLC) are explained and validated in the context of two example applications. In the first example application, the proposed methodology is compared to the sensitivity analysis technique. The break-even point has been correctly identified in almost all cases. Furthermore, it has been shown how the employed fuzzy methodology may be seen as a generalised sensitivity approach to which has been added a measure of the precision with which input variables are known to the decision-maker. In the second example application, the proposed methodology is compared to two probabilistic techniques: the confidence index method and the Monte Carlo simulation (MCS) technique. The proposed methodology correctly identified the most uncertain cost items and portrayed well the confidence in ranking. Besides, all predicted net present values were in close agreement with those obtained by the MCS technique. This showed once more the robustness of various measures and concepts employed in the developed algorithms. INTRODUCTION Whole life costing (WLC) is a technique that is used to facilitate the effective choice between a number of competing alternatives that differ not only in their initial costs but in their subsequent maintenance and operational costs as well. The method consists of two processes. First, all costs and revenues associated with the acquisition, use and maintenance and disposal of various alternatives are discounted to the present time. Then, these discounted costs of each alternative are summed up to calculate the net present value (NPV) of that alternative. In the second process, various alternatives are ranked in order of ascendant magnitude according to their NPVs and the best alternative is selected such that it has the lowest NPV. For results that are deterministic, there is no ambiguity in ranking the alternatives and the decision is straightforward. WLC, however, deals with the future and the future is unknown. Thus, it is crucial to risk assess the results before the final decision is made. In doing so, either a sensitivity analysis (SA) or a probabilistic risk assessment technique, usually the Monte Carlo simulation (MCS), is employed. The SA is used to identify the impact of a change in the value of a single risky independent parameter on the dependent variable, i.e. the NPV in a WLC exercise. The objective is usually to determine the break-even point defined as the value of the input-data element that causes the NPV of the least-cost alternative to equal that of the next-lowest-cost alternative (Kirk and Dell Isola, 1995). The major advantage of the SA is that it explicitly shows the robustness of the ranking of alternatives (Flanagan and Norman, 1993, Woodward, 1995). However, it has two limitations. First, it is a univariate approach, i.e., only one parameter can be varied at a time. Thus, it is effective only when the uncertainty in one input-data element is predominant (Kirk and Dell Isola, 1995). Secondly, it does not aim to quantify risk but rather to identify factors that are risk sensitive (Flanagan et al., 1989). Thus, it does not provide a definitive method of making the decision.
2 Kishk, Al-Hajj and Pollock The MCS has been used in WLC modelling by many authors including Flanagan et al. (1987, 1989), Ko et al. (1998) and Goumas et al. (1999). In a MCS, every uncertain variable is represented by a probability distribution function (PDF). The resulting NPVs become random variables represented by PDFs. As noted by Flanagan et al. (1989), this provides the decision-maker with a wider view in the final choice between alternatives but will not remove the need for the decision-maker to apply judgement and there will be, inevitably, a degree of subjectivity in this judgement. Moreover, many researchers (Woodward, 1995; Chau, 1997; Byrne, 1997; Edwards and Bowen, 1998; among others) have criticized simulation techniques for their complexity and their expense in terms of computation time and expertise required to extract the knowledge. The confidence index (CI) is a simplified probabilistic approach. It is based on two assumptions (Kirk and Dell Isola, 1995): (1) the uncertainties in all cost data are normally distributed; and (2) the high and low 90% estimates for each cost do in fact correspond to the true 90% points of the normal PDF for that cost. Obviously, the CI approach tackles some of the difficulties of MCS. However, the necessary assumption of normally distributed data and the above two restrictions limit its generality. The main assumption in probabilistic risk assessment techniques is that all uncertainties follow the characteristics of random uncertainty. This implies that all uncertainties are due to stochastic variability or to measurement or sampling error; and consequently are expressible by means of PDFs that are best derived from significant data. However, historic data for WLC within the construction industry is sparse (Bull, 1993; Ashworth, 1996, 1999; Wilkinson, 1996; Sterner, 2000, among others). Besides, facets of uncertainty in WLC data are not only random but also of a judgmental nature (Kishk, 2001). Due to the above limitations of existing risk assessment models in handling uncertain data in WLC modelling, the authors suggested that the fuzzy set theory (FST) might be more appropriate. This is mainly because it is easier to define fuzzy variables than random variables when no or limited information is available (Kaufmann and Gupta, 1985). Furthermore, mathematical concepts and operations within the framework of FST are much simpler than those within the probability theory (Ferrari and Savoia, 1998). In a series of papers (Kishk and Al-Hajj, 2000a, 2000b, 2000c, 2000d), the authors employed the FST to develop three models and algorithms to handle subjective assessments of input variables. In a subsequent paper (Kishk and Al-Hajj, 2001a), another algorithm has been developed to handle stochastic data and expert assessments as represented by probability density functions (PDFs) and fuzzy numbers (FNs), respectively, within the same model calculation. Recently, a WLC-based decision support algorithm has also been proposed (Kishk and Al-Hajj, 2001b). This algorithm systematically analyses uncertain input data and provides the decision-maker with a better impression of their validity and usability by the employment of two sets of measures. The first set includes two confidence measures CI 1 and CI 2, to evaluate the rank ordering of various competing alternatives. These factors may be interpreted as measures of the confidence in the two statements: A is better than B and A is at least as better as B, respectively, where A and B are two competing alternatives. The second set includes two uncertainty measures U and F to identify the significance of various costs regarding the ambiguity of the decision. In this paper, these algorithms are explained and compared to existing assessment techniques in the context of two example applications. This is done to highlight some of their unique features and to identify their relation to the SA, CI and MCS techniques.
3 Handling uncertain information in whole life costing 2.0 EXAMPLE (1) In this section, an example problem given in Kirk and Dell Isola (1995) is solved. A student recreation centre including a gymnasium was to be designed and it was required to undertake a feasibility study for the use of a solar-assisted domestic hot water system. Cost data of the two design alternatives are summarised in Table (1). Because the uncertainty of the economic life of the solar-energy system, T, a SA of varying its value from 10 to 25 with a best estimate of 18 years using a discount rate of 7% has been requested. Kirk and Dell Isola (1995) solved the problem by calculating the present worth savings from using the solar energy system and a break-even point of 16 years was obtained. To ease the comparison with the proposed algorithms, the problem is resolved by calculating the net present values of both alternatives. Again, the break-even point is 16 years and the corresponding net present value is $144,723 as shown in Fig. (1). Table (1): Cost data for example (1). Cost Fuel System Solar System Initial construction cost Baseline $ 129,000 Annual Maintenance and repair costs Baseline $ 2,720 Annual fuel cost $ 15,320 0 Salvage value 0 $ 29, Net Present Value ($1000s) Solar. Fuel T Figure (1): The sensitivity test using the net present values. The net present value (NPV) algorithm (Kishk and Al-Hajj, 2000b) was also employed to solve this example problem. Low, best and high estimates were used to define the membership function of the life cycle, T. Five different MFs were considered: a triangular fuzzy number (TFN), a trapezoidal fuzzy number (TrFN) and three normal PDFs. Figure (2) depicts the resulting NPVs of both alternatives for the TFN and TrFN cases. For the TFN case, the same break-even point is calculated by the algorithm at net present value of $144,723 with a membership value of This value corresponds to an 3
4 Kishk, Al-Hajj and Pollock economic life of 16 years (Fig. 3) as previously calculated by the sensitivity approach. For the TrFN case, the resulting MFs of the net present values are two crisp sets (intervals) whose boundaries are defined by the NPVs of both alternatives at the corresponding boundaries of the TrFN, i.e. [ 10, 25] years. Thus, a break-even interval, [$ 133,138, $155,274], was obtained. The true break-even point of $144,723 is included in this interval as shown in Fig. (2). Obviously, a single break-even point could not be obtained because the use of a TrFN implies that the low, best and high estimates were given the same membership value, i.e. = Solar Fuel TFN TrFN Figure (2): The net present values for the TFN and TrFN cases (example 1) T, years Figure (3): Break-even point for the TFN case (example 1).
5 Handling uncertain information in whole life costing To further investigate the effect of the shape of the membership function, three normal PDFs have been used to model the economic life of the solar system with the best estimate of 18 years as the mean value and standard deviations, σ, of 1, 1.5 and 2. Figure (4) shows the resulting NPVs for these three cases. In all cases, the correct break-even point of $144,723 was predicted with associated membership values of 11, 74 and 04 for σ = 1,1.5 and 2, respectively. Again, these membership values correspond to the same economic life of 16 years as shown in Figure (5). This shows the robustness of the transformation algorithm Solar Fuel PDF, σ = PDF, σ = 1.5 PDF, = 2.0 σ Figure (4): MFs for the net present values for PDF cases (example 1) σ = σ = 1.5 σ = T, years Figure (5): Break-even points for PDF cases (example 1). 5
6 Kishk, Al-Hajj and Pollock Figure (4) shows also how the uncertainty in information is reflected in the predicted solution as the spread of the calculated NPVs increases with the uncertainty of data, i.e. as the value of the standard deviation increases. As expected, the spread is zero for the certain case, σ = 0, shown with solid lines in the figure. In the TrFN case, the fuel system was ranked first, while the solar-assisted system was recommended in all other cases. The confidence measure in ranking for all studied cases are summarised in Table (2). All these measures are in general agreement with common sense which shows the effectiveness of the ranking algorithm and the employed confidence measures. Table (2): Confidence measures of ranking (example 1). Case CI 1 CI 2 Crisp Normal PDF, σ = Normal PDF, σ = Normal PDF, σ = TFN TrFN EXAMPLE (2) Cost estimates of two alternative floor finishes for an administrative facility are summarised in Table (3). It is required to choose the best option for an analysis period of 25 years and a discount rate of 10%. Kirk and Dell Isola (1995) used the confidence index approach to solve this example. Alternative L was ranked first with a confidence index of which indicates low confidence in the choice of alternative L for implementation. Besides, their computations indicated also that the high uncertainty of annual M&R are the main cause to this low confidence in ranking. Because the CI approach does not give detailed NPVs, this problem is re-solved using MCS using the Crystal Ball 2000 simulation software (Decisioneering, 2000) as shown in Fig. (6). The fuzzy NPV algorithm was also used and its results are depicted with the thick curves in Fig. (7). Results of the MCS and fuzzy solutions are summarised in Table (4). To allow for a clearer comparison between the MCS and fuzzy results, the relative frequencies in Fig. (6) were transformed to possibility distributions (Kishk and Al-Hajj, 2001a) and are plotted in Fig. (7) with thin curves. As shown, fuzzy results agreed well with those obtained from MCS. Table (3): Cost estimates for example (2). Alternatives Estimates High Best Low 1. Alternative (L): Initial construction costs $ 7,600 $ 7,200 $ 6,800 Maintenance and repair annual cost. $ 2,700 $ 1,800 $ Alternative (H): Initial construction costs $ 14,500 $ 14,500 $ 13,700 Maintenance and repair annual cost. $ 2,000 $ 1,200 $ 400 Alternative L was ranked first and the confidence measures in this ranking are summarised in Table (5). These relatively low measures reflect low confidence in this ranking. Besides, annual M&R costs were identified to have the dominant contribution to the ambiguity of ranking as clearly indicated by the calculated measures of uncertainty in Table (6). These results are in general agreement with those obtained by the confidence index approach.
7 Handling uncertain information in whole life costing 4 Alternative L. Alternative H Relative frequency Frequency (200,000 trials) Figure (6): Simulation results (example 2). Alternative L. Alternative H. Alternative Figure (7): MFs of Net present values (example 2 ) Table (4): Summary of results (example 2). MCS 7 Fuzzy Minimum Maximum Mean Minimum Maximum Removal L $14,992 $32,095 $23,535 $14,967 $32,105 $23,536 H $17,345 $33,407 $25,407 $17,331 $33,454 $25,392
8 Kishk, Al-Hajj and Pollock Table (5): Measures of confidence (example 2). Rank Alternatives Alternative L Alternative H CI 1 CI 2 CI 1 CI 2 1 Alternative L Alternative H Table (6): Measures of uncertainty (example 2). Cost and value items Alternative H Alternative L (discounted & normalised) U F U F Initial cost Maintenance and repair annual costs Kirk and Dell Isola re-solved this example problem using closer estimates for annual M&R costs (Table 7). Alternative L was ranked first with a confidence index of 59 indicating high confidence in the choice of alternative L for implementation. Again, both the Crystal Ball 2000 simulation software (Decisioneering, 2000) and the NPV algorithms were used to obtain the revised NPVs of both alternatives. These revised results are summarised in Table (8). Figure (8) shows the results of the simulation exercise (200,000 trials) and their equivalent MFs are depicted with the thin lines in Fig. (9). As shown in Fig. (9) and Table (8), the revised results obtained from the proposed fuzzy algorithms agreed well with those obtained from MCS and are slightly more conservative. Again, alternative L was ranked first and the confidence measures in this ranking are summarised in Table (9). These relatively high measures reflect higher confidence in this ranking as previously indicated by the confidence index technique. These results indicate again the robustness of various measures employed in the proposed algorithms. Table (7): Revised cost estimates (example 2). Alternatives Estimates High Best Low 1. Alternative (L): Initial construction costs 7,600 7,200 6,800 Maintenance and repair annual cost. 2,300 1,800 1, Alternative (H): Initial construction costs 14,500 14,500 13,700 Maintenance and repair annual cost. 1,800 1, Table (8): Summary of revised results (example 2). Alternative MCS Fuzzy Minimum Maximum Mean Minimum Maximum Removal L $18,612 $28,464 $23,533 $18,597 $28,474 $23,536 H $19,151 $31,630 $25,394 $19,146 $31,639 $25,392 Table (9): Revised measures of confidence (example 2). Rank Alternatives Alternative L Alternative H CI 1 CI 2 CI 1 CI 2 1 Alternative L Alternative H
9 4 Alternative L. Alternative H. Handling uncertain information in whole life costing 8000 Relative frequency Frequency (200,000 trials) Figure (8): Simulation results (example 2, revised case). Alternative L. Alternative H Figure (9): MFs of NPVs (example 2, revised case). 4.0 CONCLUSIONS AND FUTURE RESEARCH A number of recently developed algorithms have been illustrated using two case studies. Typical results showed a well agreement with traditional risk assessment techniques. Besides, smooth output MFs have been obtained despite the relatively small number of intervals employed, illustrating the robustness and computational efficiency of the implemented algorithms. Furthermore, these algorithms not only have the desirable features of existing techniques but also have more advantages as follows. 9
10 Kishk, Al-Hajj and Pollock Compared to the SA technique, they have the desirable property of transparency. Besides, they can deal with the uncertainty of multiple variables simultaneously rather than being a univariate approach. Compared to the MCS technique, they have the desirable property of producing the distribution of all possible values of the output variable but in a less complex and a more effective way. Compared to the CI approach, they have the desirable property of ranking competing alternatives and giving confidence measures in this ranking but they are not limited to a specific type of distributions of input variables. Because of these unique features, these algorithms will be employed to develop IT applications of whole life costing to support life-cycle decision-making in the design and management of construction assets. Part of this objective will be achieved through an ongoing EPSRC funded research project (Al-Hajj et al., 2001; Aouad et al., 2001) undertaken by a joint collaboration with another team from the University of Salford. 5.0 REFERENCES Al-Hajj, A., Pollock, R., Kishk, M., Aouad, G., Sun, M. and Bakis, N. (2001) On the requirements for effective information management in whole life costing within an integrated environment. Proceedings of the Annual Conference of the RICS Research Foundation (COBRA 2001), Glasgow Caledonian University, 3-5 September, 2, Aouad, G., Bakis, N., Amaratunga, D., Osbaldiston, S., Sun, M., Kishk, M., Al-Hajj, A. and Pollock, R. (2001) An integrated life cycle costing database a conceptual framework. Proceedings of the 17th Annual Conference of the Association of Researchers in Construction Management (ARCOM 2001), University of Salford, 5-7 September, 1, Ashworth, A. (1996) Estimating the life expectancies of building components in life cycle costing calculations, Structural Survey, 14 (2), 4-8. Ashworth, A. (1999) Cost studies of buildings, Longman. Bull, J. W. (1993) The way ahead for life cycle costing in the construction industry. In Bull, J. W. (ed.) Life Cycle Costing for Construction. Blackie Academic & Professional, Glasgow, UK. Byrne, P. (1997) Fuzzy DCF: a contradiction in terms, or a way to better investment appraisal? Proceedings of Cutting Edge 97, RICS. Chau, K. W. (1997) Monte Carlo simulation of construction costs using subjective data: response, Construction Management and Economics, 15, Decisioneering (2000) Crystall Ball 2000 User Manual. Decisioneering, Denver, USA. Edwards, P.J. and Bowen, P. A. (1998) Practices, barriers and benefits of risk management process in building services cost estimation: comment, Construction Management and Economics, 16, Goumas, M. G., Lygerou, V. A. and Papayannakis, L. E. (1999) Computational methods for planning and evaluating geothermal energy projects, Energy Policy, 27, Flanagan, R., Kendell, A., Norman, G. and Robinson, G. (1987) Life Cycle Costing and Risk Management, Construction Management and Economics, 5, Flanagan, R., Norman, G., Meadows, J. and Robinson, G. (1989) Life Cycle Costing - Theory and Practice. BSP Professional Books.
11 Handling uncertain information in whole life costing Flanagan, R. and Norman, G. (1993) Risk management and construction. Blackwell Scientific Publications. Kirk, S., J. and Dell Isola, A. J. (1995) Life Cycle Costing for Design Professionals. McGrew-Hill Book Company, New York. Kishk, M. (2001) An Integrated fuzzy approach to whole life costing based decision making. Unublished PhD Thesis. Scott Sutherland School, The Robert Gordon University, Aberdeen. Kishk M. and Al-Hajj A. (2000a) A fuzzy approach to model subjectivity in life cycle costing. Proceedings of the BF2000 national conference of postgraduate research in the built and human environment, The University of Salford, 9-10 March, Kishk M. and Al-Hajj A. (2000b) A fuzzy model and algorithm to handle subjectivity in life cycle costing based decision-making. Journal of financial management of property and construction, 5, 1-2, August, Kishk M. and Al-Hajj A. (2000c) Handling linguistic assessments in life cycle costing - a fuzzy approach. Proceedings of the construction and building research conference of the RICS research foundation (COBRA2000), The University of Greenwich, 30 August - 1 September, 2000, Kishk M. and Al-Hajj A. (2000d) Modelling of life cycle costs of alternatives with different lives. Proceedings of the 16th annual conference of the Association of Researchers in Construction Management (ARCOM 2000), Glasgow Caledonian University, 6-8 September, Kishk M. and Al-Hajj A. (2001a) Integrating Subjective and Stochastic Data in Life Cycle Costing Calculations. Proceedings of the first international postgraduate research conference in the Built and Human Environment, University of Salford, March, Kishk, M.; and Al-Hajj, A. (2001b) An innovative approach to integrating the analysis of uncertainty into life cycle costing. Proceedings of the first international conference of Innovation in Architecture, Engineering and Construction, Loughborough University, July, Ko, W. I., Choi, J. W., Kang, C. H. and Lee, J. S. (1998) Nuclear fuel cycle cost analysis using a probabilistic simulation technique, Annals of Nuclear Energy, 25, Sterner, E. (2000) Life-cycle costing and its use in the Swedish building sector, Building Research & Information, 28 (5/6), Wilkinson, S. (1996) Barriers to LCC Use in the New Zealand Construction Industry. Proceedings of the 7 th International Symposium on Economic Management of Innovation, Productivity and Quality in Construction, Zagreb, Woodward, D. G. (1995) Use of sensitivity analysis in Build-Own-Operate-Transfer project evaluation, International Journal of Project Management, 13 (4),
Stochastic Budget Simulation
PERGAMON International Journal of Project Management 18 (2000) 139±147 www.elsevier.com/locate/ijproman Stochastic Budget Simulation Martin Elkjaer Grundfos A/S, Thorsgade 19C, Itv., 5000 Odense C, Denmark
More informationTowards development of a whole life costing based model for evaluation of building designs
Towards development of a whole life costing based model for evaluation of building designs John Muhumuza Kakitahi 1, Henry Alinaitwe 2, Dan Tindiwensi 3 1 Graduate Student, Faculty of Technology, Makerere
More informationLife Cycle Cost Optimization Within Decision Making on Alternative Designs Shiven Jiten Sompura 1, Aakash Goyal 1 and Hakob Avetisyan, Ph.D.
1 Life Cycle Cost Optimization Within Decision Making on Alternative Designs Shiven Jiten Sompura 1, Aakash Goyal 1 and Hakob Avetisyan, Ph.D. 2 1 Graduate Research Assistant at the Department of Civil
More informationProbabilistic Benefit Cost Ratio A Case Study
Australasian Transport Research Forum 2015 Proceedings 30 September - 2 October 2015, Sydney, Australia Publication website: http://www.atrf.info/papers/index.aspx Probabilistic Benefit Cost Ratio A Case
More informationSTOCHASTIC COST ESTIMATION AND RISK ANALYSIS IN MANAGING SOFTWARE PROJECTS
Full citation: Connor, A.M., & MacDonell, S.G. (25) Stochastic cost estimation and risk analysis in managing software projects, in Proceedings of the ISCA 14th International Conference on Intelligent and
More informationSTOCHASTIC COST ESTIMATION AND RISK ANALYSIS IN MANAGING SOFTWARE PROJECTS
STOCHASTIC COST ESTIMATION AND RISK ANALYSIS IN MANAGING SOFTWARE PROJECTS Dr A.M. Connor Software Engineering Research Lab Auckland University of Technology Auckland, New Zealand andrew.connor@aut.ac.nz
More informationCost Overrun Assessment Model in Fuzzy Environment
American Journal of Engineering Research (AJER) e-issn : 2320-0847 p-issn : 2320-0936 Volume-03, Issue-07, pp-44-53 www.ajer.org Research Paper Open Access Cost Overrun Assessment Model in Fuzzy Environment
More informationFeasibility Analysis Simulation Model for Managing Construction Risk Factors
Feasibility Analysis Simulation Model for Managing Construction Risk Factors Sang-Chul Kim* 1, Jun-Seon Yoon 2, O-Cheol Kwon 3 and Joon-Hoon Paek 4 1 Researcher, LG Engineering and Construction Co., Korea
More informationPrioritization of Climate Change Adaptation Options. The Role of Cost-Benefit Analysis. Session 8: Conducting CBA Step 7
Prioritization of Climate Change Adaptation Options The Role of Cost-Benefit Analysis Session 8: Conducting CBA Step 7 Accra (or nearby), Ghana October 25 to 28, 2016 8 steps Step 1: Define the scope of
More informationDecision Support Models 2012/2013
Risk Analysis Decision Support Models 2012/2013 Bibliography: Goodwin, P. and Wright, G. (2003) Decision Analysis for Management Judgment, John Wiley and Sons (chapter 7) Clemen, R.T. and Reilly, T. (2003).
More informationRISK BASED LIFE CYCLE COST ANALYSIS FOR PROJECT LEVEL PAVEMENT MANAGEMENT. Eric Perrone, Dick Clark, Quinn Ness, Xin Chen, Ph.D, Stuart Hudson, P.E.
RISK BASED LIFE CYCLE COST ANALYSIS FOR PROJECT LEVEL PAVEMENT MANAGEMENT Eric Perrone, Dick Clark, Quinn Ness, Xin Chen, Ph.D, Stuart Hudson, P.E. Texas Research and Development Inc. 2602 Dellana Lane,
More informationVolatility estimation in Real Options with application to the oil and gas industry i
Volatility estimation in Real Options with application to the oil and gas industry i by Jenifer Piesse and Alexander Van de Putte Estimating volatility for use in financial options is a pretty straight
More informationDecision Making Under Conditions of Uncertainty: A Wakeup Call for the Financial Planning Profession by Lynn Hopewell, CFP
Decision Making Under Conditions of Uncertainty: A Wakeup Call for the Financial Planning Profession by Lynn Hopewell, CFP Editor's note: In honor of the Journal of Financial Planning's 25th anniversary,
More informationApplication of Triangular Fuzzy AHP Approach for Flood Risk Evaluation. MSV PRASAD GITAM University India. Introduction
Application of Triangular Fuzzy AHP Approach for Flood Risk Evaluation MSV PRASAD GITAM University India Introduction Rationale & significance : The objective of this paper is to develop a hierarchical
More informationEconomic Decision Making Using Fuzzy Numbers Shih-Ming Lee, Kuo-Lung Lin, Sushil Gupta. Florida International University Miami, Florida
Economic Decision Making Using Fuzzy Numbers Shih-Ming Lee, Kuo-Lung Lin, Sushil Gupta Florida International University Miami, Florida Abstract In engineering economic studies, single values are traditionally
More informationSENSITIVITY ANALYSIS IN CAPITAL BUDGETING USING CRYSTAL BALL. Petter Gokstad 1
SENSITIVITY ANALYSIS IN CAPITAL BUDGETING USING CRYSTAL BALL Petter Gokstad 1 Graduate Assistant, Department of Finance, University of North Dakota Box 7096 Grand Forks, ND 58202-7096, USA Nancy Beneda
More informationValuing Early Stage Investments with Market Related Timing Risk
Valuing Early Stage Investments with Market Related Timing Risk Matt Davison and Yuri Lawryshyn February 12, 216 Abstract In this work, we build on a previous real options approach that utilizes managerial
More informationFAILURE RATE TRENDS IN AN AGING POPULATION MONTE CARLO APPROACH
FAILURE RATE TRENDS IN AN AGING POPULATION MONTE CARLO APPROACH Niklas EKSTEDT Sajeesh BABU Patrik HILBER KTH Sweden KTH Sweden KTH Sweden niklas.ekstedt@ee.kth.se sbabu@kth.se hilber@kth.se ABSTRACT This
More informationFundamentals of Project Risk Management
Fundamentals of Project Risk Management Introduction Change is a reality of projects and their environment. Uncertainty and Risk are two elements of the changing environment and due to their impact on
More informationUsing Monte Carlo Analysis in Ecological Risk Assessments
10/27/00 Page 1 of 15 Using Monte Carlo Analysis in Ecological Risk Assessments Argonne National Laboratory Abstract Monte Carlo analysis is a statistical technique for risk assessors to evaluate the uncertainty
More informationDecision Support Methods for Climate Change Adaption
Decision Support Methods for Climate Change Adaption 5 Summary of Methods and Case Study Examples from the MEDIATION Project Key Messages There is increasing interest in the appraisal of options, as adaptation
More informationHow to Consider Risk Demystifying Monte Carlo Risk Analysis
How to Consider Risk Demystifying Monte Carlo Risk Analysis James W. Richardson Regents Professor Senior Faculty Fellow Co-Director, Agricultural and Food Policy Center Department of Agricultural Economics
More informationFebruary 2010 Office of the Deputy Assistant Secretary of the Army for Cost & Economics (ODASA-CE)
U.S. ARMY COST ANALYSIS HANDBOOK SECTION 12 COST RISK AND UNCERTAINTY ANALYSIS February 2010 Office of the Deputy Assistant Secretary of the Army for Cost & Economics (ODASA-CE) TABLE OF CONTENTS 12.1
More informationRandom Variables and Probability Distributions
Chapter 3 Random Variables and Probability Distributions Chapter Three Random Variables and Probability Distributions 3. Introduction An event is defined as the possible outcome of an experiment. In engineering
More informationChapter 2 Uncertainty Analysis and Sampling Techniques
Chapter 2 Uncertainty Analysis and Sampling Techniques The probabilistic or stochastic modeling (Fig. 2.) iterative loop in the stochastic optimization procedure (Fig..4 in Chap. ) involves:. Specifying
More informationA Framework for Risk Assessment in Egyptian Real Estate Projects using Fuzzy Approach
A Framework for Risk Assessment in Egyptian Real Estate Projects using Fuzzy Approach By Ahmed Magdi Ibrahim Aboshady A Thesis Submitted to the Faculty of Engineering at Cairo University In Partial Fulfillment
More informationUncertainty in Economic Analysis
Risk and Uncertainty Uncertainty in Economic Analysis CE 215 28, Richard J. Nielsen We ve already mentioned that interest rates reflect the risk involved in an investment. Risk and uncertainty can affect
More informationLONG INTERNATIONAL. Rod C. Carter, CCP, PSP and Richard J. Long, P.E.
Rod C. Carter, CCP, PSP and Richard J. Long, P.E. LONG INTERNATIONAL Long International, Inc. 5265 Skytrail Drive Littleton, Colorado 80123-1566 USA Telephone: (303) 972-2443 Fax: (303) 200-7180 www.long-intl.com
More informationPerformance risk evaluation of long term infrastructure projects (PPP-BOT projects) using probabilistic methods
EPPM, Singapore, 20-21 Sep 2011 Performance risk evaluation of long term infrastructure projects (PPP-BOT projects) using probabilistic Meghdad Attarzadeh 1 and David K H Chua 2 Abstract Estimation and
More informationGetting started with WinBUGS
1 Getting started with WinBUGS James B. Elsner and Thomas H. Jagger Department of Geography, Florida State University Some material for this tutorial was taken from http://www.unt.edu/rss/class/rich/5840/session1.doc
More informationA Model to Quantify the Return On Information Assurance
A Model to Quantify the Return On Information Assurance This article explains and demonstrates the structure of a model for forecasting, and subsequently measuring, the ROIA, or the ROIA model 2. This
More informationPreprint: Will be published in Perm Winter School Financial Econometrics and Empirical Market Microstructure, Springer
STRESS-TESTING MODEL FOR CORPORATE BORROWER PORTFOLIOS. Preprint: Will be published in Perm Winter School Financial Econometrics and Empirical Market Microstructure, Springer Seleznev Vladimir Denis Surzhko,
More informationOptimizing the Incremental Delivery of Software Features under Uncertainty
Optimizing the Incremental Delivery of Software Features under Uncertainty Olawole Oni, Emmanuel Letier Department of Computer Science, University College London, United Kingdom. {olawole.oni.14, e.letier}@ucl.ac.uk
More informationIntegrated Management System For Construction Projects
Integrated Management System For Construction Projects Abbas M. Abd 1, Amiruddin Ismail 2 and Zamri Bin Chik 3 1 Correspondence Authr: PhD Student, Dept. of Civil and structural Engineering Universiti
More informationExcavation and haulage of rocks
Use of Value at Risk to assess economic risk of open pit slope designs by Frank J Lai, SAusIMM; Associate Professor William E Bamford, MAusIMM; Dr Samuel T S Yuen; Dr Tao Li, MAusIMM Introduction Excavation
More informationSAQ KONTROLL AB Box 49306, STOCKHOLM, Sweden Tel: ; Fax:
ProSINTAP - A Probabilistic Program for Safety Evaluation Peter Dillström SAQ / SINTAP / 09 SAQ KONTROLL AB Box 49306, 100 29 STOCKHOLM, Sweden Tel: +46 8 617 40 00; Fax: +46 8 651 70 43 June 1999 Page
More informationBounding the Composite Value at Risk for Energy Service Company Operation with DEnv, an Interval-Based Algorithm
Bounding the Composite Value at Risk for Energy Service Company Operation with DEnv, an Interval-Based Algorithm Gerald B. Sheblé and Daniel Berleant Department of Electrical and Computer Engineering Iowa
More informationA_A0008: FUZZY MODELLING APPROACH FOR PREDICTING GOLD PRICE BASED ON RATE OF RETURN
Section A - Mathematics / Statistics / Computer Science 13 A_A0008: FUZZY MODELLING APPROACH FOR PREDICTING GOLD PRICE BASED ON RATE OF RETURN Piyathida Towwun,* Watcharin Klongdee Risk and Insurance Research
More informationA New Method of Cost Contingency Management
A New Method of Cost Contingency Management Mohammed Wajdi Hammad, Alireza Abbasi, Michael J. Ryan School of Engineering and Information Technology, University of New South Wales (UNSW Australia), Canberra
More informationBudgeting and Costing Control Workshop
CORPORATE FINANCIAL PLANNING, Budgeting and Costing Control Workshop H.H. Sheik Sultan Tower (0) Floor Corniche Street Abu Dhabi U.A.E www.ictd.ae ictd@ictd.ae Course Introduction: All business decision-making
More informationSTATISTICAL FLOOD STANDARDS
STATISTICAL FLOOD STANDARDS SF-1 Flood Modeled Results and Goodness-of-Fit A. The use of historical data in developing the flood model shall be supported by rigorous methods published in currently accepted
More informationUPDATED IAA EDUCATION SYLLABUS
II. UPDATED IAA EDUCATION SYLLABUS A. Supporting Learning Areas 1. STATISTICS Aim: To enable students to apply core statistical techniques to actuarial applications in insurance, pensions and emerging
More informationStochastic Risk Analysis and Cost Contingency Allocation Approach for Construction Projects Applying Monte Carlo Simulation
, July 5-7, 2017, London, U.K. Stochastic Risk Analysis and Cost Contingency Allocation Approach for Construction Projects Applying Monte Carlo Simulation Fahimeh Allahi, Lucia Cassettari, Marco Mosca
More informationFinancial Risk Analysis for Engineering Management: A Framework Development and Testing
, 23-25 October, 2013, San Francisco, USA Financial Risk Analysis for Engineering Management: A Framework Development and Testing J. Lai, L. Zhang, C.F. Duffield, and L. Aye Abstract An important facet
More informationA Study on the Risk Regulation of Financial Investment Market Based on Quantitative
80 Journal of Advanced Statistics, Vol. 3, No. 4, December 2018 https://dx.doi.org/10.22606/jas.2018.34004 A Study on the Risk Regulation of Financial Investment Market Based on Quantitative Xinfeng Li
More informationKING FAHAD UNIVERSITY OF PETROLEUM & MINERALS COLLEGE OF ENVIROMENTAL DESGIN CONSTRUCTION ENGINEERING & MANAGEMENT DEPARTMENT
KING FAHAD UNIVERSITY OF PETROLEUM & MINERALS COLLEGE OF ENVIROMENTAL DESGIN CONSTRUCTION ENGINEERING & MANAGEMENT DEPARTMENT Report on: Associated Problems with Life Cycle Costing As partial fulfillment
More informationMaking sense of Schedule Risk Analysis
Making sense of Schedule Risk Analysis John Owen Barbecana Inc. Version 2 December 19, 2014 John Owen - jowen@barbecana.com 2 5 Years managing project controls software in the Oil and Gas industry 28 years
More informationMULTI-PARTY RISK MANAGEMENT PROCESS (MRMP) FOR A CONSTRUCTION PROJECT FINANCED BY AN INTERNATIONAL LENDER
MULTI-PRTY RISK MNGEMENT PROCESS (MRMP) FOR CONSTRUCTION PROJECT FINNCED BY N INTERNTIONL LENDER Jirapong Pipattanapiwong and Tsunemi Watanabe School of Civil Engineering, sian Institute of Technology,
More informationCA. Sonali Jagath Prasad ACA, ACMA, CGMA, B.Com.
MANAGEMENT OF FINANCIAL RESOURCES AND PERFORMANCE SESSIONS 3& 4 INVESTMENT APPRAISAL METHODS June 10 to 24, 2013 CA. Sonali Jagath Prasad ACA, ACMA, CGMA, B.Com. WESTFORD 2008 Thomson SCHOOL South-Western
More informationIntegrating Contract Risk with Schedule and Cost Estimates
Integrating Contract Risk with Schedule and Cost Estimates Breakout Session # B01 Donald E. Shannon, Owner, The Contract Coach December 14, 2015 2:15pm 3:30pm 1 1 The Importance of Estimates Estimates
More informationHomeowners Ratemaking Revisited
Why Modeling? For lines of business with catastrophe potential, we don t know how much past insurance experience is needed to represent possible future outcomes and how much weight should be assigned to
More informationCalifornia Department of Transportation(Caltrans)
California Department of Transportation(Caltrans) Probabilistic Cost Estimating using Crystal Ball Software "You cannot exactly predict an uncertain future" Presented By: Jack Young California Department
More informationCASE 6: INTEGRATED RISK ANALYSIS MODEL HOW TO COMBINE SIMULATION, FORECASTING, OPTIMIZATION, AND REAL OPTIONS ANALYSIS INTO A SEAMLESS RISK MODEL
ch11_4559.qxd 9/12/05 4:06 PM Page 527 Real Options Case Studies 527 being applicable only for European options without dividends. In addition, American option approximation models are very complex and
More informationINVESTMENT RISK ANALYSIS: THEORETICAL ASPECTS
INVESTMENT RISK ANALYSIS: THEORETICAL ASPECTS Agnė Keršytė Kaunas University of Technology, Lithuania, agne.kersyte@ktu.lt http://dx.doi.org/10.5755/j01.em.17.3.2099 Abstract Strategic investment decisions
More informationDescribing Uncertain Variables
Describing Uncertain Variables L7 Uncertainty in Variables Uncertainty in concepts and models Uncertainty in variables Lack of precision Lack of knowledge Variability in space/time Describing Uncertainty
More informationA Cash Flow-Based Approach to Estimate Default Probabilities
A Cash Flow-Based Approach to Estimate Default Probabilities Francisco Hawas Faculty of Physical Sciences and Mathematics Mathematical Modeling Center University of Chile Santiago, CHILE fhawas@dim.uchile.cl
More informationEquitable Financial Evaluation Method for Public-Private Partnership Projects *
TSINGHUA SCIENCE AND TECHNOLOGY ISSN 1007-0214 20/25 pp702-707 Volume 13, Number 5, October 2008 Equitable Financial Evaluation Method for Public-Private Partnership Projects * KE Yongjian ( ), LIU Xinping
More informationDecommissioning Basis of Estimate Template
Decommissioning Basis of Estimate Template Cost certainty and cost reduction June 2017, Rev 1.0 2 Contents Introduction... 4 Cost Basis of Estimate... 5 What is a Basis of Estimate?... 5 When to prepare
More informationTIE2140 / IE2140e Engineering Economy Tutorial 6 (Lab 2) Engineering-Economic Decision Making Process using EXCEL
TIE2140 / IE2140e Engineering Economy Tutorial 6 (Lab 2) Engineering-Economic Decision Making Process using EXCEL Solutions Guide by Wang Xin, Hong Lanqing & Mei Wenjie 1. Learning Objectives In this lab-based
More informationXSG. Economic Scenario Generator. Risk-neutral and real-world Monte Carlo modelling solutions for insurers
XSG Economic Scenario Generator Risk-neutral and real-world Monte Carlo modelling solutions for insurers 2 Introduction to XSG What is XSG? XSG is Deloitte s economic scenario generation software solution,
More informationPractical example of an Economic Scenario Generator
Practical example of an Economic Scenario Generator Martin Schenk Actuarial & Insurance Solutions SAV 7 March 2014 Agenda Introduction Deterministic vs. stochastic approach Mathematical model Application
More informationA Better Way to Assess Benefits, Costs, and Risks in a Product Support Business Case Analysis
A Better Way to Assess Benefits, Costs, and Risks in a Product Support Business Case Analysis Frank Camm, John Matsumura, Lauren A. Mayer, and Kyle Siler-Evans March 2018 Traditional DoD Approach to BCA
More informationMONTE CARLO SIMULATION AND PARETO TECHNIQUES FOR CALCULATION OF MULTI- PROJECT OUTTURN-VARIANCE
MONTE CARLO SIMULATION AND PARETO TECHNIQUES FOR CALCULATION OF MULTI- PROJECT OUTTURN-VARIANCE Keith Futcher 1 and Anthony Thorpe 2 1 Colliers Jardine (Asia Pacific) Ltd., Hong Kong 2 Department of Civil
More informationWorking Paper #1. Optimizing New York s Reforming the Energy Vision
Center for Energy, Economic & Environmental Policy Rutgers, The State University of New Jersey 33 Livingston Avenue, First Floor New Brunswick, NJ 08901 http://ceeep.rutgers.edu/ 732-789-2750 Fax: 732-932-0394
More informationComparing Datar-Mathews and fuzzy pay-off approaches to real option valuation
Comparing Datar-Mathews and fuzzy pay-off approaches to real option valuation Mariia Kozlova, Mikael Collan, and Pasi Luukka School of Business and Management Lappeenranta University of Technology Lappeenranta,
More informationRISK MANAGEMENT ON USACE CIVIL WORKS PROJECTS
RISK MANAGEMENT ON USACE CIVIL WORKS PROJECTS Identify, Quantify, and 237 217 200 237 217 200 Manage 237 217 200 255 255 255 0 0 0 163 163 163 131 132 122 239 65 53 80 119 27 252 174.59 110 135 120 112
More informationProbabilistic Sensitivity Analysis Prof. Tony O Hagan
Bayesian Methods in Health Economics Part : Probabilistic Sensitivity Analysis Course outline Part : Bayesian principles Part : Prior distributions Part 3: Uncertainty in health economic evaluation Part
More informationProgrammatic Risk Management in Space Projects
r bulletin 103 august 2000 Programmatic Risk Management in Space Projects M. Belingheri, D. von Eckardstein & R. Tosellini ESA Directorate of Manned Space and Microgravity, ESTEC, Noordwijk, The Netherlands
More informationGuidance on satisfactory expected commercial return (SECR)
Guidance on satisfactory expected commercial return (SECR) Date of publication: August 2018 2 Guidance on satisfactory expected commercial return (SECR) Contents Scope and purpose of this guidance 3 Introduction
More informationA Literature Review Fuzzy Pay-Off-Method A Modern Approach in Valuation
Journal of Economics and Business Research, ISSN: 2068-3537, E ISSN (online) 2069 9476, ISSN L = 2068 3537 Year XXI, No. 1, 2015, pp. 98-107 A Literature Review Fuzzy Pay-Off-Method A Modern Approach in
More informationRisk Analysis in Investment Appraisal
Risk Analysis in Investment Appraisal by Savvakis C. Savvides Published in Project Appraisal, Volume 9 Number 1, pages 3-18, March 1994 Beech Tree Publishing 1994 Reprinted with permission ABSTRACT * This
More informationLikelihood-based Optimization of Threat Operation Timeline Estimation
12th International Conference on Information Fusion Seattle, WA, USA, July 6-9, 2009 Likelihood-based Optimization of Threat Operation Timeline Estimation Gregory A. Godfrey Advanced Mathematics Applications
More informationRISKS AND RISK TREATMENTS IN PUBLIC PRIVATE PARTNERSHIP PROJECTS
RISKS AND RISK TREATMENTS IN PUBLIC PRIVATE PARTNERSHIP PROJECTS Bing Li, A. Akintoye and C.Hardcastle School of Built and Natural Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK Public
More informationRisk Measuring of Chosen Stocks of the Prague Stock Exchange
Risk Measuring of Chosen Stocks of the Prague Stock Exchange Ing. Mgr. Radim Gottwald, Department of Finance, Faculty of Business and Economics, Mendelu University in Brno, radim.gottwald@mendelu.cz Abstract
More informationGN47: Stochastic Modelling of Economic Risks in Life Insurance
GN47: Stochastic Modelling of Economic Risks in Life Insurance Classification Recommended Practice MEMBERS ARE REMINDED THAT THEY MUST ALWAYS COMPLY WITH THE PROFESSIONAL CONDUCT STANDARDS (PCS) AND THAT
More informationRISK MANAGEMENT. Budgeting, d) Timing, e) Risk Categories,(RBS) f) 4. EEF. Definitions of risk probability and impact, g) 5. OPA
RISK MANAGEMENT 11.1 Plan Risk Management: The process of DEFINING HOW to conduct risk management activities for a project. In Plan Risk Management, the remaining FIVE risk management processes are PLANNED
More informationRISK MANAGEMENT IN CONSTRUCTION PROJECTS
International Journal of Advances in Applied Science and Engineering (IJAEAS) ISSN (P): 2348-1811; ISSN (E): 2348-182X Vol-1, Iss.-3, JUNE 2014, 162-166 IIST RISK MANAGEMENT IN CONSTRUCTION PROJECTS SUDARSHAN
More informationFuzzy sets and real options approaches for innovation-based investment projects effectiveness evaluation
Fuzzy sets and real options approaches for innovation-based investment projects effectiveness evaluation Olga A. Kalchenko 1,* 1 Peter the Great St.Petersburg Polytechnic University, Institute of Industrial
More informationDo Not Sum Earned-Value-Based WBS-Element Estimates-at-Completion
Do Not Sum Earned-Value-Based WBS-Element Estimates-at-Completion Stephen A. Book The Aerospace Corporation P.O. Box 92957 Los Angeles, CA 90009-2957 (310) 336-8655 stephen.a.book@aero.org Society of Cost
More informationRISK MANAGEMENT IN THE DIFFERENT PHASES OF A CONSTRUCTION PROJECT A STUDY OF ACTORS INVOLVEMENT
RISK MANAGEMENT IN THE DIFFERENT PHASES OF A CONSTRUCTION PROJECT A STUDY OF ACTORS INVOLVEMENT Ekaterina Osipova 1 Department of Civil, Mining and Environmental Engineering Luleå University of Technology,
More informationAn Enhancement of Earthquake Vulnerability Models for Australian Residential Buildings Using Historical Building Damage
An Enhancement of Earthquake Vulnerability Models for Australian Residential Buildings Using Historical Building Damage Hyeuk Ryu 1, Martin Wehner 2, Tariq Maqsood 3 and Mark Edwards 4 1. Corresponding
More informationPoint Estimation. Stat 4570/5570 Material from Devore s book (Ed 8), and Cengage
6 Point Estimation Stat 4570/5570 Material from Devore s book (Ed 8), and Cengage Point Estimation Statistical inference: directed toward conclusions about one or more parameters. We will use the generic
More informationMonte Carlo analysis and its application within the valuation of technologies
431 Monte Carlo analysis and its application within the valuation of technologies S. Č. Aguilar, M. Dubová, J. Chudoba & A. Šarman Institute of Novel Technologies and Applied Informatics, Technical University
More informationRISK MANAGEMENT IN PUBLIC-PRIVATE PARTNERSHIP ROAD PROJECTS USING THE REAL OPTIONS THEORY
I International Symposium Engineering Management And Competitiveness 20 (EMC20) June 24-25, 20, Zrenjanin, Serbia RISK MANAGEMENT IN PUBLIC-PRIVATE PARTNERSHIP ROAD PROJECTS USING THE REAL OPTIONS THEORY
More informationSIMULATION CHAPTER 15. Basic Concepts
CHAPTER 15 SIMULATION Basic Concepts Monte Carlo Simulation The Monte Carlo method employs random numbers and is used to solve problems that depend upon probability, where physical experimentation is impracticable
More informationManagement Accounting Research: Trends, Perspectives, and Future
Management Accounting Research: Trends, Perspectives, and Future University Hohenheim 1 Management accounting Disclose inventories and cost of goods sold Unit manufacturing costs Planning and control functions
More informationResearch Article Portfolio Optimization of Equity Mutual Funds Malaysian Case Study
Fuzzy Systems Volume 2010, Article ID 879453, 7 pages doi:10.1155/2010/879453 Research Article Portfolio Optimization of Equity Mutual Funds Malaysian Case Study Adem Kılıçman 1 and Jaisree Sivalingam
More informationAGENERATION company s (Genco s) objective, in a competitive
1512 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 21, NO. 4, NOVEMBER 2006 Managing Price Risk in a Multimarket Environment Min Liu and Felix F. Wu, Fellow, IEEE Abstract In a competitive electricity market,
More informationRobust Critical Values for the Jarque-bera Test for Normality
Robust Critical Values for the Jarque-bera Test for Normality PANAGIOTIS MANTALOS Jönköping International Business School Jönköping University JIBS Working Papers No. 00-8 ROBUST CRITICAL VALUES FOR THE
More informationAmerican Option Pricing: A Simulated Approach
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 5-2013 American Option Pricing: A Simulated Approach Garrett G. Smith Utah State University Follow this and
More informationCharacterization of the Optimum
ECO 317 Economics of Uncertainty Fall Term 2009 Notes for lectures 5. Portfolio Allocation with One Riskless, One Risky Asset Characterization of the Optimum Consider a risk-averse, expected-utility-maximizing
More informationBetter decision making under uncertain conditions using Monte Carlo Simulation
IBM Software Business Analytics IBM SPSS Statistics Better decision making under uncertain conditions using Monte Carlo Simulation Monte Carlo simulation and risk analysis techniques in IBM SPSS Statistics
More informationThoughts about Selected Models for the Valuation of Real Options
Acta Univ. Palacki. Olomuc., Fac. rer. nat., Mathematica 50, 2 (2011) 5 12 Thoughts about Selected Models for the Valuation of Real Options Mikael COLLAN University of Turku, Turku School of Economics
More informationDynamic Strategic Planning. Evaluation of Real Options
Evaluation of Real Options Evaluation of Real Options Slide 1 of 40 Previously Established The concept of options Rights, not obligations A Way to Represent Flexibility Both Financial and REAL Issues in
More informationSimulation and Calculation of Reliability Performance and Maintenance Costs
Simulation and Calculation of Reliability Performance and Maintenance Costs Per-Erik Hagmark, PhD, Tampere University of Technology Seppo Virtanen, PhD, Tampere University of Technology Key Words: simulation,
More informationProject Theft Management,
Project Theft Management, by applying best practises of Project Risk Management Philip Rosslee, BEng. PrEng. MBA PMP PMO Projects South Africa PMO Projects Group www.pmo-projects.co.za philip.rosslee@pmo-projects.com
More informationSIMULATION OF ELECTRICITY MARKETS
SIMULATION OF ELECTRICITY MARKETS MONTE CARLO METHODS Lectures 15-18 in EG2050 System Planning Mikael Amelin 1 COURSE OBJECTIVES To pass the course, the students should show that they are able to - apply
More informationInvesting in a Robotic Milking System: A Monte Carlo Simulation Analysis
J. Dairy Sci. 85:2207 2214 American Dairy Science Association, 2002. Investing in a Robotic Milking System: A Monte Carlo Simulation Analysis J. Hyde and P. Engel Department of Agricultural Economics and
More informationMonte Carlo Introduction
Monte Carlo Introduction Probability Based Modeling Concepts moneytree.com Toll free 1.877.421.9815 1 What is Monte Carlo? Monte Carlo Simulation is the currently accepted term for a technique used by
More informationDevelopment of Debt Management IT Systems in Peru
R E P U B L I C O F P E R U Development of Debt Management IT Systems in Peru Presented to: Sovereign Debt Management Forum World Bank Washington DC, October 2012 Agenda The first step Developing the system
More information