Simulation Decomposition: New Approach for Better Simulation Analysis of Multi-Variable Investment Projects

Kozlova, Mariia; Collan, Mikael; Luukka, Pasi

doi:10.25102/fer.2016.02.01

Cited by 11 publications

(27 citation statements)

References 24 publications

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“…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).…”

Section: Simulation Decomposition Approachmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Variable Simulation Decomposition in Environmental Planning: An Application to Carbon Capture and Storage

Kozlova¹,

Yeomans²

2019

J ENVIRON INFORM LET

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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.

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Section: Simulation Decomposition Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Variable Simulation Decomposition in Environmental Planning: An Application to Carbon Capture and Storage

Kozlova¹,

Yeomans²

2019

J ENVIRON INFORM LET

Self Cite

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“…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.…”

Section: Case Study On Middle Route Of the South-to-north Water Divermentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

Failure Mode and Effect Analysis (FMEA) with Extended MULTIMOORA Method Based on Interval-Valued Intuitionistic Fuzzy Set: Application in Operational Risk Evaluation for Infrastructure

Wang

et al. 2019

Information

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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.

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Risk Assessment of Investment Projects Using the Simulation Decomposition Method

Afanaseva,

Neyrus,

Navatskaya

et al. 2023

Lecture Notes in Networks and Systems

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Simulation Decomposition: New Approach for Better Simulation Analysis of Multi-Variable Investment Projects

Cited by 11 publications

References 24 publications

Multi-Variable Simulation Decomposition in Environmental Planning: An Application to Carbon Capture and Storage

Multi-Variable Simulation Decomposition in Environmental Planning: An Application to Carbon Capture and Storage

Failure Mode and Effect Analysis (FMEA) with Extended MULTIMOORA Method Based on Interval-Valued Intuitionistic Fuzzy Set: Application in Operational Risk Evaluation for Infrastructure

Risk Assessment of Investment Projects Using the Simulation Decomposition Method

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