A New Fuzzy Method for Assessing Six Sigma Measures

Six-Sigma has some measures which measure performance characteristics related to a process. In most of the traditional methods, exact estimation is used to assess these measures and to utilize them in practice. In this paper, to estimate some of these measures, including Defects per Million Opportunities (DPMO), Defects per Opportunity (DPO), Defects per unit (DPU) and Yield, a new algorithm based on Buckley's estimation approach is introduced. The algorithm uses a family of confidence intervals to estimate the mentioned measures. The final results of introduced algorithm for different measures are triangular shaped fuzzy numbers. Finally, since DPMO, as one of the most useful measures in Six-Sigma, should be consistent with costumer need, this paper introduces a new fuzzy method to check this consistency. The method compares estimated DPMO with fuzzy customer need. Numerical examples are given to show the performance of the method. All rights reserve

A fuzzy Bayesian network approach for risk analysis in process industries

YesFault tree analysis is a widely used method of risk assessment in process industries. However, the classical fault tree approach has its own limitations such as the inability to deal with uncertain failure data and to consider statistical dependence among the failure events. In this paper, we propose a comprehensive framework for the risk assessment in process industries under the conditions of uncertainty and statistical dependency of events. The proposed approach makes the use of expert knowledge and fuzzy set theory for handling the uncertainty in the failure data and employs the Bayesian network modeling for capturing dependency among the events and for a robust probabilistic reasoning in the conditions of uncertainty. The effectiveness of the approach was demonstrated by performing risk assessment in an ethylene transportation line unit in an ethylene oxide (EO) production plant

The estimation of normalized fuzzy weights

AbstractThe estimation of a normalized set of positive fuzzy weights constitutes the most important aspects in the fuzzy multiple attribute decision making process. A systematic treatment of this problem is carried out in this paper. The concept of fuzzy normalization is first defined and the meaning of consistency in a fuzzy environment is discussed. Based on these definitions and discussions, the various approaches in the literature are examined and several improvements or new approaches are proposed. Numerical examples are used to evaluate and to compare the various existing and the newly proposed approaches

AN EXTENSION OF THE FAILURE MODE EFFECTS AND CRITICALITY ANALYSIS WITH FUZZY ANALYTICAL HIERARCHY PROCESS METHOD TO ASSESS THE EMERGENCY SAFETY BARRIERS

The emergency safety barrier is one of the reactive technical safety barriers in industrial facilities. Degrade of emergency safety barriers can lead to a major accident with serious consequences for people, property and the environment. In this context, the purpose of this article is to present a proposed methodology to identify these deficiencies, thus ensuring the effectiveness of the emergency safety barriers. This paper presents an integrated approach that uses fuzzy set theory, extension of failure modes, effects and criticality analysis and the fuzzy analytic hierarchy process method to deal with uncertainty in decision-making related to the prioritization of risk factors. These risk factors are the prioritization of corrective actions associated with the most critical disturbance modes to improve the reliability of emergency safety barriers. In addition, a Liquefied Petroleum Gas production facility was selected as a case study to assess the emergency safety barriers. The results show that the proposed methodology provides the possibility to evaluate the fire-fighting systems. In addition, the fuzzy analytical approach method is the most reliable and accurate. Therefore, some corrective actions are suggested to reduce the failure criticality of the emergency safety barriers and help practitioners prioritize the improvement of the emergency safety barriers of the Liquefied Petroleum Gas storage facility. This paper has an important role in the dysfunctional analysis of the emergency safety barriers related to the others effects of the release of LPG, such as the effects of domino scenarios

The Improvement Model of Navigational Safety for Inland Waterway Transport

This paper aims at evaluating navigational safety for inland waterway transport (IWT). In doing so, the literature and operational features of IWT were initially reviewed to figure out risk elements (REs) influencing the navigational safety for IWT. After that, a fuzzy Analytic Hierarchical Process (AHP) approach was adopted to estimate the weight for the likelihood and consequence measures of REs. Then, continuous risk matrix (RM) was introduced to identify REs\u27 risk level. Lastly, to test the proposed research model\u27s applicability, IWT operators across Vietnam were empirically surveyed. The empirical findings could be useful for IWT operators in launching managerial policies to boost their navigational safety. Furthermore, the proposed risk evaluation framework may serve as a methodological reference in relevant literature

Modeling the Impact of Citizens' Social Responsibility on Sustainable Development Based on the Modifying Role of the COVID-19 Pandemic

In light of the coronavirus disease 2019 (COVID-19) pandemic, there is a strong correlation between citizens' social responsibility (SR) and sustainable development (SD). Accordingly, the present study aimed to model the impact of citizens' SR on SD concerning the modifying role of the COVID-19 pandemic. To this end, the data were collected from two target groups, namely, elites (viz. experts and professionals) (n=15) and the citizens of Tehran, Iran (n=384), through a questionnaire. The research model was also designed based on expert opinions, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), type-II fuzzy logic, and ELECTRE III, and then modified. The given model was subsequently examined by the partial least squares regression (PLS regression). Results showed that if citizens' SR is elevated by about one-unit, social justice, sustainable economy, and stable environment would be augmented by 0.693, 0.735, and 0.583 units, respectively. SD would also grow by 0.485, 0.948, and 0.743 units if social justice, sustainable economy, and sustainable environment increased by one unit. Consequently, the results of the present study confirm the mechanism of the impact of citizens' SR on SD

Improving document representation by accumulating relevance feedback : the relevance feedback accumulation (RFA) algorithm

Document representation (indexing) techniques are dominated by variants of the term-frequency analysis approach, based on the assumption that the more occurrences a term has throughout a document the more important the term is in that document. Inherent drawbacks associated with this approach include: poor index quality, high document representation size and the word mismatch problem. To tackle these drawbacks, a document representation improvement method called the Relevance Feedback Accumulation (RFA) algorithm is presented. The algorithm provides a mechanism to continuously accumulate relevance assessments over time and across users. It also provides a document representation modification function, or document representation learning function that gradually improves the quality of the document representations. To improve document representations, the learning function uses a data mining measure called support for analyzing the accumulated relevance feedback. Evaluation is done by comparing the RFA algorithm to other four algorithms. The four measures used for evaluation are (a) average number of index terms per document; (b) the quality of the document representations assessed by human judges; (c) retrieval effectiveness; and (d) the quality of the document representation learning function. The evaluation results show that (1) the algorithm is able to substantially reduce the document representations size while maintaining retrieval effectiveness parameters; (2) the algorithm provides a smooth and steady document representation learning function; and (3) the algorithm improves the quality of the document representations. The RFA algorithm\u27s approach is consistent with efficiency considerations that hold in real information retrieval systems. The major contribution made by this research is the design and implementation of a novel, simple, efficient, and scalable technique for document representation improvement

A failure probability assessment method for train derailments in railway yards based on IFFTA and NGBN

Derailment is one of the main hazards during train passes through railway turnouts (RTs) in classification yards. The complexity of the train-turnout system (TTS) and unfavorable operating conditions frequently cause freight wagons to derail at RTs. Secondary damages such as hazardous material spillage and train collisions can result in loss of life and property. Therefore, the primary goal is to assess the derailment risk and identify the root causes when trains pass through RTs in classification yards. To address this problem, this paper proposes a failure probability assessment approach that integrates intuitionistic fuzzy fault tree analysis (IFFTA) and Noisy or gate Bayesian network (NGBN) for quantifying the derailment risk at RTs. This method can handle the fact that the available information on the components of the TTS is imprecise, incomplete, and vague. The proposed methodology was tested through data analysis at Taiyuan North classification yard in China. The results demonstrate that the method can efficiently evaluate the derailment risk and identify key risk factors. To reduce the derailment risk at RTs and prevent secondary damage and injuries, measures such as optimizing turnout alignment, controlling impact between wagons, lubricating the rails, and regularly inspecting the turnout geometries can be implemented. By developing a risk-based model, this study connects theory with practice and provides insights that can help railway authorities better understand the impact of poor TTS conditions on train safety in classification yards

A RISK-BASED VERIFICATION FRAMEWORK FOR OFFSHORE WIND FARM DEVELOPMENT: DESIGN, INSTALLATION, OPERATIONS AND MAINTENANCE OF OFFSHORE WIND TURBINES

This thesis encompasses a holistic review of the development trends in wind turbine technology (onshore and offshore) and the challenges perceived at the stages of design, construction and operations of modern-day wind energy technology (Friedrich and Lukas, 2017). The main focus of this study is to evaluate the risks associated with offshore wind farm development (OWFD). This is achieved by first estimating those perceived risks, understanding the relative importance of each individual risk, and carrying out an assessment using a specialist analytical tool known as AHiP-Evi. AHiP-Evi was developed through a combination of application of Analytic Hierarchy Process (AHP) and Evidential Reasoning (ER) techniques. The AHP was used to ascertain the weighting of the respective risk variables according to their relative importance, while the ER was used to evaluate the aggregated influence of the collective risk variables associated with the OWFD. Finally, a specific modelling tool known as BN-SAT (Bayesian Network Sensitivity Analysis Technique) was developed to evaluate the probabilities of occurrence of the variable nodes and their overall impacts on the decision node (OWFD). The BN-SAT is comprised of a combination of Bayesian networks (BNs) concepts and a sensitivity analysis (SA) approach. The AHiP-Evi model initially developed in this study is transformed into the BN structure in order to compute the conditional and unconditional prior probability for each starting node using the NETICA analytical software to determine the aggregated impact of the specific risk variables on the OWFD. The outcome from this modelling analysis is then compared to the initial assessment carried out by the application of the AHiP-Evi modelling tool in order to validate the robustness of both modelling tools. In the case study of this research, the percentage difference of the outcomes of the two models is insignificant, which demonstrates the fact that both systems are effective. The Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were integrated to develop a specific model for the selection of best-case risk management technique (RMT). Based on the decision makers’ (DMs) aggregated judgements, it was possible to compute the values and determine the best-case RMT dependent on the decision variables driving the decision - for example, costs and benefits, through the developed integrated model known as FAHP-FTOPSIS. The outcome of this selection model has been seen to be reasonably practical and a successful conclusion of the research contribution. Awareness of the aggregated impact of the risk variables is important in making the decision about appropriate investments in a strategic improvement of risk management and efficient resource allocations to the offshore wind industry