Sensitivity analysis for system design improvement

Contini, S.; Stefan, Scheer; Wilikens, Marc

doi:10.1109/icdsn.2000.857545

Cited by 6 publications

(3 citation statements)

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“…In this paper, a sensitivity analysis method is introduced for studying and predicting the disturbance degree of the model output value (risk magnitude) caused by the change of the input value of each index. Sensitivity analysis is a systematic analysis method, which identifies weak points or areas in the system with the insight of managers in quantitative evaluation and continuously improves the design of the system and improves the stability of the system [39]. If the validated model is reliable and its reasoning process is logically feasible, then the sensitivity analysis of the model at least satisfies the following three theorems:…”

Section: Verification Of the Model Using Sensitivity Analysismentioning

confidence: 99%

An Integrated Method for Fire Risk Assessment in Residential Buildings

Liu

Wang

et al. 2020

Mathematical Problems in Engineering

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Building fires are characterized by high uncertainty, so their fire risk assessment is a very challenging task. Many indexes and parameters related to building fires are ambiguous and uncertain; as a result, a flexible and robust method is needed to process quantitative or qualitative data and update existing information when new data are available. This paper presents a novel model to deal with the uncertainty of the residential building fire risk and systematically optimize its performance effectiveness. The model includes fuzzy theory, evidence reasoning theory, and expected utility methods. Fuzzy analysis hierarchy process is applied to analyze the residential building fire risk index system and determine the weights of the risk indexes, while the evidence reasoning operator is used to synthesize them. Three buildings were selected as a case study to illustrate the proposed fire risk model. The results show that the fire risk level of three buildings corresponds to “moderate” or below which is consistent with the previous study. These results also truly reflect the actual situation of fire safety in these residential buildings. The application of this model provides a powerful mathematical framework for cooperative modeling of the fire risk assessment system and allows data to be analyzed step by step in a systematic manner. It is expected that the proposed model could provide managers and researchers with flexible and transparent tools to effectively reduce the fire risk in the system.

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Section: Verification Of the Model Using Sensitivity Analysismentioning

confidence: 99%

An Integrated Method for Fire Risk Assessment in Residential Buildings

Liu

Wang

et al. 2020

Mathematical Problems in Engineering

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“…Uncertainties are inherently present in different influencing factors. Since the proposed methodology provides a numerical estimation of disruption risk without identifying the most important input event, sensitivity analysis (SA) is a systematic approach that can provide managerial insight in evaluating quantitative information in order to identify the weakest points or areas of a system in order to improve its designs [39].…”

Section: Validation Of the Model Using Sensitivity Analysis (Step 6)mentioning

confidence: 99%

An integrated fuzzy risk assessment for seaport operations

et al. 2014

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An integrated fuzzy risk assessment for seaport operations http://researchonline.ljmu.ac.uk/id/eprint/1787/ Article LJMU has developed LJMU Research Online for users to access the research output of the University more effectively.Abstract Seaport operations are characterised by high levels of uncertainty, as a result their risk evaluation is a very challenging task. Much of the available data associated with the system's operations is uncertain and ambiguous, requiring a flexible yet robust approach of handling both quantitative and qualitative data as well as a means of updating existing information as new data becomes available. Conventional risk modelling approaches are considered to be inadequate due to the lack of flexibility and inappropriate structure for addressing the system's risks. This paper proposes a novel fuzzy risk assessment approach to facilitating the treatment of uncertainties in seaport operations and to optimize its performance effectiveness in a systematic manner. The methodology consists of a fuzzy analytical hierarchy process, an evidential reasoning (ER) approach, fuzzy set theory and expected utility. The fuzzy analytical hierarchy process is used to analyse the complex structure of seaport operations and determine the weights of risk factors while ER is used to synthesise them. The methodology provides a robust mathematical framework for collaborative modelling of the system and allows for a step by step analysis of the system in a systematic manner. It is envisaged that the proposed approach could provide managers and infrastructure analysts a flexible tool to enhance the resilience of the system in a systematic manner. Keywords: Seaport operations; evidential reasoning approach; fuzzy set theory; fuzzy analytical hierarchy process 2. Literature Review CMI systems are faced with high operational constraints due to the dynamic interactions among their interrelated components. The level of interdependences and complexity of the system's operations can be acknowledged through its description by the US Department of Homeland Security "as all areas and things of, on, under, relating to, adjacent to, or bordering on a sea, ocean, or other navigable waterway, including all maritime related activities, infrastructure, people, cargo, vessels and other conveyances" [8]. Analysing the systems in terms of their interdependences which include infrastructure characteristics, operational relationships, environmental impacts, technical efficiency, failure types and state of operation provides insight into their complexity, enabling collaborative modelling to be undertaken. Modern seaports, which are an integral component of CMI systems, focus their operations on continuous handling of flows and efficient transport. Meersman [9], as shown in Figure 1, revealed that these systems progressed from performing cargo handling, stacking and distribution functions to being a complex transportation hub in logistic chains. A vessel operator controls a fleet of vessels with a set of characteristics; the land si...

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“…Sensitivity analysis helps to evaluate the percentage contribution of each basic event that leads to a system failure (top event) and also estimates unavailability and failure frequencies of the basic event, which have to be changed in order to maintain a predefined system's top event probability. In addition, sensitivity analysis also helps to identify three important decisions for the design modifications of a system: the weakest link of the system; a better design alternative; and how to evaluate the effect of the adopted solution on system safety (Contini et al, 2000). In this study the sensitivity analysis for a system is evaluated on the basis of investigating the fuzzy weighted index (FWI) of each basic event, and the cutsets importance of a fault tree.…”

Section: Sensitivity Analysismentioning

confidence: 99%