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This paper presents a new method to enhance simulation-based analysis of complex investments that contain multi-variable uncertainty. The method is called "simulation decomposition". Typically the result of simulation-based investment analysis is in the form of histogram distributions - here we propose a method for first classifying the possible outcomes of selected uncertain variables into states and then using combinations of the created states in the decomposition of the simulated distribution into a number of sub-distributions. The sub-distributions that can be matched to state-combinations of the variables contain relevant actionable information that helps managers in decision-making with regards to the studied investments. A numerical illustration of a renewable energy investment is used to demonstrate the usability, the enhanced analytical power, and the intuitively understandable benefits that can be reached by using the simulation decomposition method. The proposed method is generally usable and can be utilized independent of the investment context.
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... In this vein, in this paper we select two modern analysis techniques used in the analysis and the valuation of flexibility, the pay-off method [21] and (Monte Carlo) simulation based analysis, called "simulation decomposition" [22] and use them to study incentivepolicies in the context of biofuels. The reason for selecting these two methods is the fit of these methods to the type of uncertainty that surrounds the context of biofuel-policies [23]. ...
... The procedure is based on (i) identifying the relevant variables that can be affected by the project owner, their relevant "states", and boundaries for each state; (ii) forming "groups" or scenarios by combining the states; (iii) running the simulation, while keeping track on the input-output "inference"; (iv) visualizing the results such that the outcome resulting from each input group (scenario) is separately visualized and allows better understanding of "what leads to what". The procedure is depicted in Figure 2. The detailed description of the procedure, how the results from it are visualized, and available implementation tools can be found in [22,47]. Simulation decomposition has demonstrated its value in renewable energy policy analysis [22,48], in other environmental policy issues [25,49], and can be generally applied to any problem modeled with Monte Carlo simulation independent of the context [47]. ...
... The procedure is depicted in Figure 2. The detailed description of the procedure, how the results from it are visualized, and available implementation tools can be found in [22,47]. Simulation decomposition has demonstrated its value in renewable energy policy analysis [22,48], in other environmental policy issues [25,49], and can be generally applied to any problem modeled with Monte Carlo simulation independent of the context [47]. ...
Article
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A variety of policy types are available to foster the transition to a low-carbon economy. In every sector, including transportation, heat and power production, policymakers face the choice of what type of policy to adopt. For this choice, it is crucial to understand how different mechanisms incentivize investments in terms of improving their profitability, shaping the flexibility available for investors, and how they are affected by the surrounding uncertainty. This paper focuses on transportation-biofuel policies, particularly on the financial incentives put on the bio-component of fuel and the combination of using penalties and tax-relief. Delivery of vital policymaking insights by using two modern simple-to-use profitability analysis methods, the pay-off method and the simulation decomposition method, is illustrated. Both methods enable the incorporation of uncertainty into the profitability analyses, and thus generate insight about the flexibilities involved, and the factors affecting the results. The results show that the combination of penalties and tax-relief is a way to steer fuel-production towards sustainability. The two methods used for analysis complement each other and provide important insights for analysis and decision-making beyond what the commonly used profitability analysis methods typically provide.
... SimDec is generalizable to any simulation method without the need for any significant additional computing resources. SimDec's innovative visual analytics capabilities have already been considered in a diverse range of environmental decision-making problems (Kozlova et al., 2016;Kozlova and Yeomans, 2019;Deviatkin et al., 2020;Hietanen, 2020;Sadyhova, 2020). Although not commercially available, downloadable versions of SimDec code are readily accessible in Matlab (Kozlova et al., 2018a(Kozlova et al., , 2018b, VBA for Excel (Kozlova and Yeomans, 2020), Python, and R (Sadyhova, 2020). ...
... Recently, the visual analytics capabilities of SimDec have been used to produce novel insights into such diverse environmental decision-making problems as renewable energy policy analysis (Kozlova et al., 2016;Hietanen, 2020), environmental policy planning (Kozlova and Yeomans, 2019), carbon footprint analysis (Deviatkin et al., 2020), and green construction (Sadyhova, 2020). Hietanen (2020) demonstrated how performing a decomposition of renewable energy policy with different sets of factors can uncover additional insights and provide a comprehensive understanding of the problem's underlying complexities. ...
... Hietanen (2020) demonstrated how performing a decomposition of renewable energy policy with different sets of factors can uncover additional insights and provide a comprehensive understanding of the problem's underlying complexities. Figure 4 provides a summary of SimDec's visualization output from these various complex, multifaceted environmental decision-making problems (Kozlova et al., 2016;Kozlova and Yeomans, 2019;Deviatkin et al., 2020;Hietanen, 2020). ...
... Simulation decomposition (SimDec) has recently provided a simple, but powerful, advancement to the standard Monte Carlo approach (Kozlova et al. 2016). The basic concept behind SimDec is to decompose (and color-code) the final output distribution into groups of outcomes resulting from multivariable combinations of different states of the input variables. ...
... Because the contribution of the state combinations to the overall output is easy to portray visually, SimDec can reveal previously unidentified connections between the multivariable combinations of inputs on the outputs. The decomposition has been shown to provide deeper insights into the uncertainty surrounding the problem, to assist decision making when actionable variables are chosen for decomposition, and to better understand the interplay of different sources of uncertainty on the distribution of outcomes (Kozlova et al. 2016, Kozlova andYeomans 2019). The SimDec approach is completely generalizable to any Monte Carlo model with negligible additional computational overhead. ...
... Although stacked histograms and bar charts have been widely used across all domains of academia and industry, the idea of decomposing Monte Carlo simulation results has remained largely overlooked. To the best of our knowledge, multivariable simulation decomposition has only been applied to a couple of investment cases (Kozlova et al. 2016, Kozlova andYeomans 2019); single-factor decomposition has been used in memristor performance analysis (García-Redondo et al. 2012) and has been alluded to sparingly in some very narrowly focused commercial software products. ...
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Monte Carlo (MC) simulation is widely used in many different disciplines in order to analyze problems that involve uncertainty. Simulation decomposition has recently provided a simple, but powerful, advancement to the standard Monte Carlo approach. Its value for better informing decision making has been previously shown in the investment-analysis field. In this paper, we demonstrate that simulation decomposition can enhance problem analysis in a wide array of domains by applying it to three very different disciplines: geology, business, and environmental science. Further extensions to such disciplines as engineering, natural sciences, and social sciences are discussed. We propose that by incorporating simulation decomposition into pedagogical practices, we expect students to significantly advance their problem-understanding and problem-solving skills.
... Recently, Kozlova et al. [29] created a supplementary technique referred to as simulation decomposition (SD) that enhances the analytical capabilities of simulation by significantly extending the explanatory power of the cause-effect relationships between the input variables and the simulation results. SD is a practical concept that provides a straightforward, powerful tool for visually assessing the impacts of variables. ...
... As an "add-on" to Monte Carlo simulation, the SD approach is capable of working with any kind of probability distribution (discrete, continuous, or custom-made). In SD, the possible outcomes of selected uncertain variables are classified into discrete states and then combinations of these states are used to "decompose" the chart of simulated outputs into a number of state-influenced sub-distributions [29]. Of great practical significance, these sub-distributions can be superimposed onto the overall output distribution figure to permit a direct visualization of the specific decomposed cause-effect impacts of the multi-variable groups of input combinations (see Figure 1(b)). ...
... Generally speaking, however, its analytical approaches for evaluating the resulting outputs have remained largely unchanged over time [26]. SD was recently introduced to expand the explanatory capabilities of Monte Carlo by exploring inherent cause-effect links between combinations of input variables and the resulting outputs [29]. While this section briefly outlines the SD approach, more extensive explanations appear in Kozlova et al. [29]. ...
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Environmental sustainability problems frequently require the need for decision-making in situations containing considerable uncertainty. Monte Carlo simulation methods have been used in a wide array of environmental planning settings to incorporate these uncertain features. Simulation-generated outputs are commonly displayed as probability distributions. Recently simulation decomposition (SD) has enhanced the visualization of the cause-effect relationships of multi-variable combinations of inputs on the corresponding simulated outputs. SD partitions sub-distributions of the Monte Carlo outputs by pre-classifying selected input variables into states, grouping combinations of these states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since it is a straightforward task to visually project the contribution of the subdivided scenarios onto the overall output, SD can illuminate previously unidentified connections between the multi-variable combinations of inputs on the outputs. SD is generalizable to any Monte Carlo method with negligible additional computational overhead and, therefore, can be readily extended into most environmental analyses that use simulation models. This study demonstrates the efficacy of SD for environmental sustainability decision-making on a carbon footprint analysis case for wooden pallets.
... Recently, Kozlova et al. (2016) introduced an ancillary approach referred to as simulation decomposition (SD) that extends the analysis of simulation results by enhancing the explanatory power of the cause-effect relationships between the input variables and the simulation results in multi-variable investment projects. Typically, these simulation-based investment outputs are displayed in the form of histogram distributions. ...
... Typically, these simulation-based investment outputs are displayed in the form of histogram distributions. In SD, Kozlova et al. (2016) classified the possible outcomes of selected uncertain variables into states and then used combinations of these states to decompose the simulated output histogram into a number of sub-distributions. The decomposed sub-distributions could then be matched to state-combinations of the variables containing relevant actionable information. ...
... Although Monte Carlo techniques enjoy an extensive history of application to a wide spectrum of different problems, the approach-and the way its results have been analyzed-has remained relatively unchanged (Kleijnen, 2018). Kozlova et al. (2016) proposed SD as an enhancement to the explanatory power of simulation by further exploiting the cause-effect relationships inherent between the input variables and the corresponding output. While this section briefly outlines the SD approach, more extensive details and descriptions can be found in Kozlova et al. (2016). ...
Article
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Environmental decision-making commonly involves multifaceted problems that demonstrate considerable uncertainty. Monte Carlo simulation approaches have been employed in a variety of environmental planning venues to address these uncertain aspects. Simulation-based outputs are frequently presented in the form of probability distributions. Recently an approach referred to as simulation decomposition (SD) has been introduced that extends the analysis of Monte Carlo results by enhancing the explanatory power of the cause-effect relationships between the multi-variable combinations of inputs and the simulated outputs. SD constructs sub-distributions of the simulation output by pre-classifying some of the uncertain input variables into states, clustering the various combinations of these different states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since the contribution of subdivided scenarios to the overall output is easily portrayed visually, SD can highlight and disclose previously unidentified connections between the multi-variable combinations of inputs on the outputs. An SD approach is generalizable to any Monte Carlo model with negligible additional computational overhead and, hence, can be readily used for environmental analyses that employ simulation models. This study illustrates the efficacy of SD in environmental analysis using a carbon capture and storage project from China.
... On the other hand, for complex risk management and investment decisions, scholars have proposed some countermeasures, such as FMEA and simulation analysis [29]. The essence of FMEA can also be regarded as a multiple criteria decision making (MCDM) problem. ...
... Step 3: Determine the Final Risk Ranking Order of Potential Failure Modes. The comprehensive utility value y i , the Tchebycheff Metric distance d max , and the multiplicative utility value U i of potential failure modes are obtained using Equations (29), (32), and (33) in Table 7. Then, the risk ranking of potential failure mode is obtained, as expressed in Table 8, by the IVIF-ratio system, the IVIF-reference point, and the IVIF-full multiplicative form method. Finally, the final risk ranking of potential failure modes is determined based on dominance theory as seen in the last column of Table 8. ...
Article
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Failure Mode and Effect Analysis (FMEA) is a useful risk assessment tool used to identify, evaluate, and eliminate potential failure modes in numerous fields to improve security and reliability. Risk evaluation is a crucial step in FMEA and the Risk Priority Number (RPN) is a classical method for risk evaluation. However, the traditional RPN method has deficiencies in evaluation information, risk factor weights, robustness of results, etc. To overcome these shortcomings, this paper aims to develop a new risk evaluation in FMEA method. First, this paper converts linguistic evaluation information into corresponding interval-valued intuitionistic fuzzy numbers (IVIFNs) to effectively address the uncertainty and vagueness of the information. Next, different priorities are assigned to experts using the interval-valued intuitionistic fuzzy priority weight average (IVIFPWA) operator to solve the problem of expert weight. Then, the weights of risk factors are subjectively and objectively determined using the expert evaluation method and the deviation maximization model method. Finally, the paper innovatively introduces the interval-valued intuitionistic fuzzy weighted averaging (IVIFWA) operator, Tchebycheff Metric distance, and the interval-valued intuitionistic fuzzy weighted geometric (IVIFWG) operator into the ratio system, the reference point method, and the full multiplication form of MULTIMOORA sub-methods to optimize the information aggregation process of FMEA. The extended IVIF-MULTIMOORA method is proposed to obtain the risk ranking order of failure modes, which will help in obtaining more reasonable and practical results and in improving the robustness of results. The case of the Middle Route of the South-to-North Water Diversion Project’s operation risk is used to demonstrate the application and effectiveness of the proposed FMEA framework.
... Optimization tools are required to quickly generate alternatives and evaluate corresponding outcomes. Other research has adopted decomposition approaches; for example, the simulation decomposition proposed by Kozlova et al. (2016) enables decision-makers to check the influence of any multivariable combination of inputs on a system's outcome, providing an insightful simulation. Babic et al. (2020) Humans are responsible for ensuring a problem's suitability (Linkov et al. 2020). ...
... Optimization tools are required to quickly generate alternatives and evaluate corresponding outcomes. Other research has adopted decomposition approaches; for example, the simulation decomposition proposed by Kozlova et al. (2016) enables decision-makers to check the influence of any multivariable combination of inputs on a system's outcome, providing an insightful simulation. Babic et al. (2020) Humans are responsible for ensuring a problem's suitability (Linkov et al. 2020). ...
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The concept of a Digital Twin (DT) has stood out among the emerging digitization technologies and been embraced by U.S. and EU governments and companies. Practitioners and scholars recognize the closeness between DT and Operations Research (OR) and seek cooperation between the two fields. Driven by the question of how OR can help implement DTs, we aim to (i) identify key properties and functions of a DT from the lens of OR, (ii) evaluate the importance and urgency of OR methods in DT applications, (iii) suggest specific OR research opportunities to realize key functions of a DT, and (iv) summarizing non-OR factors that may become OR-related and influence future DT implementations. We survey the literature and show that OR and DT can contribute to each other in the areas of real-time decisions, digital models, and data integration.
... Understanding what causes the positive and/or negative NPV outcomes is actionable information and allows managers to act, in order to steer the situation towards a good, positive NPV. A similar procedure for simulationbased profitability analysis has been introduced in Kozlova et al. (2016a). We call this new procedure "fuzzy pay-off distribution decomposition". ...
Article
This paper proposes a new procedure for enriching investment and real option analysis performed with the fuzzy pay-off method by decomposing the pay-off distribution into multiple sub-distributions that correspond to different investment scenarios. This creates more information about the importance of the effect of selected factors to investment profitability. Furthermore, based on the proposed procedure, we show how a fuzzy inference system to support investment decision-making can be constructed. The proposed new procedure and the application of a fuzzy inference system are illustrated with a numerical case analysis of a power generation investment. The results show that the proposed new procedure reveals actionable information about the analyzed investment that may otherwise remain uncovered and enhances the decision-making ability of investment managers. The application of a fuzzy inference system to investment decision-support and real option analysis is a rather new approach. The obtained results highlight how the construct of a fuzzy inference system must be adapted to the perspective of the application for which it is used. © 2018 Int. Association for Fuzzy-Set Management and Economy. All rights reserved.
Chapter
This paper focuses on the analysis of agricultural and engineering processes using simulation decomposition (SD). SD is a technique that utilizes Monte Carlo simulations and distribution decomposition to visually evaluate the source and the outcome of different portions of data. Here, SD is applied to three distinct processes: a model problem, a nondestructive evaluation testing system, and an agricultural food-water-energy system. The results demonstrate successful implementations of SD for the different systems, and the illustrate the potential of SD to support new understanding of cause and effect relationships in complex systems.
Thesis
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This thesis presents an analysis of recently enacted Russian renewable energy policy based on capacity mechanism. Considering its novelty and poor coverage by academic literature, the aim of the thesis is to analyze capacity mechanism influence on investors’ decision-making process. The current research introduces a number of approaches to investment analysis. Firstly, classical financial model was built with Microsoft Excel® and crisp efficiency indicators such as net present value were determined. Secondly, sensitivity analysis was performed to understand different factors influence on project profitability. Thirdly, Datar-Mathews method was applied that by means of Monte Carlo simulation realized with Matlab Simulink®, disclosed all possible outcomes of investment project and enabled real option thinking. Fourthly, previous analysis was duplicated by fuzzy pay-off method with Microsoft Excel®. Finally, decision-making process under capacity mechanism was illustrated with decision tree. Capacity remuneration paid within 15 years is calculated individually for each RE project as variable annuity that guarantees a particular return on investment adjusted on changes in national interest rates. Analysis results indicate that capacity mechanism creates a real option to invest in renewable energy project by ensuring project profitability regardless of market conditions if project-internal factors are managed properly. The latter includes keeping capital expenditures within set limits, production performance higher than 75% of target indicators, and fulfilling localization requirement, implying producing equipment and services within the country. Occurrence of real option shapes decision-making process in the following way. Initially, investor should define appropriate location for a planned power plant where high production performance can be achieved, and lock in this location in case of competition. After, investor should wait until capital cost limit and localization requirement can be met, after that decision to invest can be made without any risk to project profitability. With respect to technology kind, investment into solar PV power plant is more attractive than into wind or small hydro power, since it has higher weighted net present value and lower standard deviation. However, it does not change decision-making strategy that remains the same for each technology type. Fuzzy pay-method proved its ability to disclose the same patterns of information as Monte Carlo simulation. Being effective in investment analysis under uncertainty and easy in use, it can be recommended as sufficient analytical tool to investors and researchers. Apart from described results, this thesis contributes to the academic literature by detailed description of capacity price calculation for renewable energy that was not available in English before. With respect to methodology novelty, such advanced approaches as Datar-Mathews method and fuzzy pay-off method are applied on the top of investment profitability model that incorporates capacity remuneration calculation as well. Comparison of effects of two different RE supporting schemes, namely Russian capacity mechanism and feed-in premium, contributes to policy comparative studies and exhibits useful inferences for researchers and policymakers. Limitations of this research are simplification of assumptions to country-average level that restricts our ability to analyze renewable energy investment region wise and existing limitation of the studying policy to the wholesale power market that leaves retail markets and remote areas without our attention, taking away medium and small investment into renewable energy from the research focus. Elimination of these limitations would allow creating the full picture of Russian renewable energy investment profile.
Conference Paper
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The paper is designed to compare two real option valuation techniques, Datar-Mathews method based on the probabilistic approach and a fuzzy pay-off method based on the possibilistic theory. These approaches comprise similar logic, recognizing the whole investment project as a real option, if investment can be terminated in case of loss forecast. Real option value is defined as a risk adjusted expected mean of the positive side of the resulting outcome distribution. Simple intuition makes these methods attractive for investment analysis. However, being relatively young they have not spread deeply to business practice and academic research. Possessing identic logic but utilizing different theoretical foundations these techniques are especially interesting to compare. In general, results obtained from applying these methods to real option analysis are consistent. Simple triangular possibilistic distribution appears to overly simplify an investment case with complex interaction of uncertain factors. However, possibilistic theory provides grounds for further method extension. Fuzzy inference rules applied to outcomes resulting from different combinations of uncertain factors create an aggregate possibilistic distribution that joins features of real option and sensitivity analyses. This enables to trace interconnections of uncertain factors to particular ranges of investment pay-off, facilitating and deepening investment analysis.
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The inability of classic NPV analysis to capture the future value of options in a capital budgeting analysis is now well documented by Trigeorgis (199312. Trigeorgis , L. 1993. Topics in real options and applications. Financial Management, 22(3): 202–223. View all references, 200514. Trigeorgis , L. 2005. Making use of real options simple: An overview and applications in flexible/modular decision making. The Engineering Economist, 50(1): 25–53. View all references), Copeland and Antikarov (2001)4. Copeland , T. and Antikarov , V. 2001. Real options: A practitioner's guide, New York: TEXERE LLC. View all references, and others. In spite of this, traditional NPV analysis continues to be described as a normative approach. The author surveys Fortune 1,000 companies to see if they have picked up on the use of real options to complement traditional analysis. Out of 279 respondents, 40 were currently using real options (14.3%). While the percentage is small, the number is higher than in previous studies. The author goes on to describe in what manner real options are being used and, of equal importance, why they are resisted by many. Somewhat encouraging is the intent of well over half the nonusers to consider the use of real options in the future.
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