Risk Analysis for Offshore Wind Turbines Using Aggregation Operators and VIKOR

In various engineering actions, potential hazards are reduced, calculated, or controlled using a variety of risk analysis methodologies. The FMEA, or Failure Mode and Effects Analysis, is a very efficient strategy that may be used in this situation. When evaluating safety concerns, failure modes\u27 likely causes and consequences are considered. Serious failures in the FMEA are identified using the Risk Priority Number (RPN). The RPN considers the effect of the probability of occurrence, probability of detection and severity by multiplying these three parameters. However, because of the formula\u27s various flaws, it is frequently criticized. In the current work, a hybrid approach using ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and geometric averaging of ordered weights (OWGA) as an aggregation operator is used to assess risk for offshore wind turbines. While the OWGA technique is used to provide weight to risk indices, the VIKOR method is used to assess the relevance of failure modes of offshore wind turbine components. The method\u27s final findings show it solves the issues with the traditional RPN technique and produces more logical outcomes

VIKOR Technique:A Systematic Review of the State of the Art Literature on Methodologies and Applications

The main objective of this paper is to present a systematic review of the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method in several application areas such as sustainability and renewable energy. This study reviewed a total of 176 papers, published in 2004 to 2015, from 83 high-ranking journals; most of which were related to Operational Research, Management Sciences, decision making, sustainability and renewable energy and were extracted from the “Web of Science and Scopus” databases. Papers were classified into 15 main application areas. Furthermore, papers were categorized based on the nationalities of authors, dates of publications, techniques and methods, type of studies, the names of the journals and studies purposes. The results of this study indicated that more papers on VIKOR technique were published in 2013 than in any other year. In addition, 13 papers were published about sustainability and renewable energy fields. Furthermore, VIKOR and fuzzy VIKOR methods, had the first rank in use. Additionally, the Journal of Expert Systems with Applications was the most significant journal in this study, with 27 publications on the topic. Finally, Taiwan had the first rank from 22 nationalities which used VIKOR technique

Use of Selected Mathematical and Statistical Methods for Data Analysis Related to the Area of ​​Quality

Import 26/06/2013Cílem práce bylo systematizovat přístupy vybraných matematických a statistických metod a to konkrétně využití fuzzy logiky pro vybrané metody. Mezi tyto metody lze zařadit metodu FMEA a metodu QFD, jejich popis a principy využití v oblasti kvality. První část diplomové práce se zabývá popisem fuzzy logiky. Je zde vysvětlena její základní definice a procesy zpracování. Další část se zabývá metodou FMEA (Analýza druhů poruchových stavů a jejich důsledků). Zde je popsaná tradiční metoda FMEA a její další inovativní přístupy. Třetí část pojednává o uvedeném principu, přehledu aplikací a vývoji metody QFD (Quality Function Deployment) česky také „dům kvality“. V poslední části je aplikace metody QFD a metody FMEA pomocí přístupu fuzzy logiky na konkrétních příkladech.The object of this thesis was to systematize the approaches of the selected mathematical and statistical methods, namely the use of fuzzy logic for selected methods. These methods include QFD and FMEA, their description and applications in the field of quality. The first part of this thesis describes the fuzzy logic. This part explains its basic definition and processing. The next part deals with method FMEA (Failure Mode and Effects Analysis). We can find the description of traditional method FMEA and its other innovative approaches here. The third section provides a principle and overview of the application and development of QFD (Quality Function Deployment), "house of quality" in the Czech sense. The last part contains the application of the methods QFD and FMEA using fuzzy logic in specific example.639 - Katedra kontroly a řízení jakostivelmi dobř

Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA

[EN] Supply chains are complex networks that receive assiduous attention in the literature. Like any complex network, a supply chain is subject to a wide variety of risks that can result in significant economic losses and negative impacts in terms of image and prestige for companies. In circumstances of aggressive competition among companies, effective management of supply chain risks (SCRs) is crucial, and is currently a very active field of research. Failure Mode, Effects and Criticality Analysis (FMECA) has been recently extended to SCR identification and prioritization, aiming at reducing potential losses caused by lack of risk control. This article has a twofold objective. First, SCR assessment is investigated, and a comprehensive list of specific risks related to the automotive industry is compiled to extend the set of most commonly considered risks. Second, an alternative way of calculating the Risk Priority Number (RPN) is proposed within the FMECA framework by means of an integrated Multi-Criteria Decision-Making (MCDM) approach. We give a new calculation procedure by making use of the Analytic Hierarchy Process (AHP) to derive factors weights, and then the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) to evaluate the new factor of "dependence" among the risks. The developed joint analysis constitutes a risk analysis support tool for criticality in systems engineering. The approach also deals with uncertainty and vagueness associated with input data through the use of fuzzy numbers. The results obtained from a relevant case study in the automotive industry showcase the effectiveness of this approach, which brings important value to those companies: When planning interventions of prevention/mitigation, primary importance should be given to (1) supply chain disruptions due to natural disasters; (2) manufacturing facilities, human resources, policies and breakdown processes; and (3) inefficient transport.Mzougui, I.; Carpitella, S.; Certa, A.; El Felsoufi, Z.; Izquierdo Sebastián, J. (2020). Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA. Processes. 8(5):1-22. https://doi.org/10.3390/pr8050579S12285Tian, Q., & Guo, W. (2019). Reconfiguration of manufacturing supply chains considering outsourcing decisions and supply chain risks. Journal of Manufacturing Systems, 52, 217-226. doi:10.1016/j.jmsy.2019.04.005Wu, Y., Jia, W., Li, L., Song, Z., Xu, C., & Liu, F. (2019). Risk assessment of electric vehicle supply chain based on fuzzy synthetic evaluation. Energy, 182, 397-411. doi:10.1016/j.energy.2019.06.007Garvey, M. D., & Carnovale, S. (2020). The rippled newsvendor: A new inventory framework for modeling supply chain risk severity in the presence of risk propagation. International Journal of Production Economics, 228, 107752. doi:10.1016/j.ijpe.2020.107752Kern, D., Moser, R., Hartmann, E., & Moder, M. (2012). Supply risk management: model development and empirical analysis. International Journal of Physical Distribution & Logistics Management, 42(1), 60-82. doi:10.1108/09600031211202472Wang, H., Gu, T., Jin, M., Zhao, R., & Wang, G. (2018). The complexity measurement and evolution analysis of supply chain network under disruption risks. Chaos, Solitons & Fractals, 116, 72-78. doi:10.1016/j.chaos.2018.09.018Ghoshal, S. (1987). Global strategy: An organizing framework. Strategic Management Journal, 8(5), 425-440. doi:10.1002/smj.4250080503Schoenherr, T., Rao Tummala, V. M., & Harrison, T. P. (2008). Assessing supply chain risks with the analytic hierarchy process: Providing decision support for the offshoring decision by a US manufacturing company. Journal of Purchasing and Supply Management, 14(2), 100-111. doi:10.1016/j.pursup.2008.01.008Xu, M., Cui, Y., Hu, M., Xu, X., Zhang, Z., Liang, S., & Qu, S. (2019). Supply chain sustainability risk and assessment. Journal of Cleaner Production, 225, 857-867. doi:10.1016/j.jclepro.2019.03.307Tang, C., & Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks. International Journal of Production Economics, 116(1), 12-27. doi:10.1016/j.ijpe.2008.07.008Ghadge, A., Dani, S., & Kalawsky, R. (2012). Supply chain risk management: present and future scope. The International Journal of Logistics Management, 23(3), 313-339. doi:10.1108/09574091211289200Ho, W. (2008). Integrated analytic hierarchy process and its applications – A literature review. European Journal of Operational Research, 186(1), 211-228. doi:10.1016/j.ejor.2007.01.004Lolli, F., Ishizaka, A., Gamberini, R., & Rimini, B. (2017). A multicriteria framework for inventory classification and control with application to intermittent demand. Journal of Multi-Criteria Decision Analysis, 24(5-6), 275-285. doi:10.1002/mcda.1620Żak, J., & Kruszyński, M. (2015). Application of AHP and ELECTRE III/IV Methods to Multiple Level, Multiple Criteria Evaluation of Urban Transportation Projects. Transportation Research Procedia, 10, 820-830. doi:10.1016/j.trpro.2015.09.035Zaidan, A. A., Zaidan, B. B., Al-Haiqi, A., Kiah, M. L. M., Hussain, M., & Abdulnabi, M. (2015). Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. Journal of Biomedical Informatics, 53, 390-404. doi:10.1016/j.jbi.2014.11.012Chang, K.-H., Chang, Y.-C., & Lee, Y.-T. (2014). Integrating TOPSIS and DEMATEL Methods to Rank the Risk of Failure of FMEA. International Journal of Information Technology & Decision Making, 13(06), 1229-1257. doi:10.1142/s0219622014500758Nazeri, A., & Naderikia, R. (2017). A new fuzzy approach to identify the critical risk factors in maintenance management. The International Journal of Advanced Manufacturing Technology, 92(9-12), 3749-3783. doi:10.1007/s00170-017-0222-4Liu, H.-C., You, J.-X., Lin, Q.-L., & Li, H. (2014). Risk assessment in system FMEA combining fuzzy weighted average with fuzzy decision-making trial and evaluation laboratory. International Journal of Computer Integrated Manufacturing, 28(7), 701-714. doi:10.1080/0951192x.2014.900865Muhammad, M. N., & Cavus, N. (2017). Fuzzy DEMATEL method for identifying LMS evaluation criteria. Procedia Computer Science, 120, 742-749. doi:10.1016/j.procs.2017.11.304Chang, K.-H., & Cheng, C.-H. (2009). Evaluating the risk of failure using the fuzzy OWA and DEMATEL method. Journal of Intelligent Manufacturing, 22(2), 113-129. doi:10.1007/s10845-009-0266-xMarch, J. G., & Shapira, Z. (1987). Managerial Perspectives on Risk and Risk Taking. Management Science, 33(11), 1404-1418. doi:10.1287/mnsc.33.11.1404Blos, M. F., Quaddus, M., Wee, H. M., & Watanabe, K. (2009). Supply chain risk management (SCRM): a case study on the automotive and electronic industries in Brazil. Supply Chain Management: An International Journal, 14(4), 247-252. doi:10.1108/13598540910970072Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V.-M., & Tuominen, M. (2004). Risk management processes in supplier networks. International Journal of Production Economics, 90(1), 47-58. doi:10.1016/j.ijpe.2004.02.007Jüttner, U. (2005). Supply chain risk management. The International Journal of Logistics Management, 16(1), 120-141. doi:10.1108/09574090510617385Gary Teng, S., Ho, S. M., Shumar, D., & Liu, P. C. (2006). Implementing FMEA in a collaborative supply chain environment. International Journal of Quality & Reliability Management, 23(2), 179-196. doi:10.1108/02656710610640943Jüttner, U., Peck, H., & Christopher, M. (2003). Supply chain risk management: outlining an agenda for future research. International Journal of Logistics Research and Applications, 6(4), 197-210. doi:10.1080/13675560310001627016Sodhi, M. S., Son, B.-G., & Tang, C. S. (2011). Researchers’ Perspectives on Supply Chain Risk Management. Production and Operations Management, 21(1), 1-13. doi:10.1111/j.1937-5956.2011.01251.xWagner, S. M., & Bode, C. (2006). An empirical investigation into supply chain vulnerability. Journal of Purchasing and Supply Management, 12(6), 301-312. doi:10.1016/j.pursup.2007.01.004Manuj, I., & Mentzer, J. T. (2008). Global supply chain risk management strategies. International Journal of Physical Distribution & Logistics Management, 38(3), 192-223. doi:10.1108/09600030810866986Bevilacqua, M., Ciarapica, F. E., Marcucci, G., & Mazzuto, G. (2019). Fuzzy cognitive maps approach for analysing the domino effect of factors affecting supply chain resilience: a fashion industry case study. International Journal of Production Research, 58(20), 6370-6398. doi:10.1080/00207543.2019.1680893Bevilacqua, M., Ciarapica, F. E., Marcucci, G., & Mazzuto, G. (2018). Conceptual model for analysing domino effect among concepts affecting supply chain resilience. Supply Chain Forum: An International Journal, 19(4), 282-299. doi:10.1080/16258312.2018.1537504Hsieh, C. Y., Wee, H. M., & Chen, A. (2016). Resilient logistics to mitigate supply chain uncertainty: A case study of an automotive company. Scientia Iranica, 23(5), 2287-2296. doi:10.24200/sci.2016.3957Lotfi, M., & Saghiri, S. (2018). Disentangling resilience, agility and leanness. Journal of Manufacturing Technology Management, 29(1), 168-197. doi:10.1108/jmtm-01-2017-0014Marasova, D., Andrejiova, M., & Grincova, A. (2017). Applying the Heuristic to the Risk Assessment within the Automotive Industry Supply Chain. Open Engineering, 7(1), 43-49. doi:10.1515/eng-2017-0007Pandey, A. K., & Sharma, R. K. (2017). FMEA-based interpretive structural modelling approach to model automotive supply chain risk. International Journal of Logistics Systems and Management, 27(4), 395. doi:10.1504/ijlsm.2017.085221Vujović, A., Đorđević, A., Gojković, R., & Borota, M. (2017). ABC Classification of Risk Factors in Production Supply Chains with Uncertain Data. Mathematical Problems in Engineering, 2017, 1-11. doi:10.1155/2017/4931797Aven, T. (2012). The risk concept—historical and recent development trends. Reliability Engineering & System Safety, 99, 33-44. doi:10.1016/j.ress.2011.11.006Chang, D., & Paul Sun, K. (2009). Applying DEA to enhance assessment capability of FMEA. International Journal of Quality & Reliability Management, 26(6), 629-643. doi:10.1108/02656710910966165Chin, K.-S., Wang, Y.-M., Ka Kwai Poon, G., & Yang, J.-B. (2009). Failure mode and effects analysis using a group-based evidential reasoning approach. Computers & Operations Research, 36(6), 1768-1779. doi:10.1016/j.cor.2008.05.002Chang, B., Chang, C.-W., & Wu, C.-H. (2011). Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systems with Applications, 38(3), 1850-1858. doi:10.1016/j.eswa.2010.07.114Zhang, Z., & Chu, X. (2011). Risk prioritization in failure mode and effects analysis under uncertainty. Expert Systems with Applications, 38(1), 206-214. doi:10.1016/j.eswa.2010.06.046Zhang, Y. F., Zhou, R. B., Yang, J. M., & Zhang, Z. (2014). Application of FTA-FMEA Method in Fault Diagnosis of Tracked Vehicle. Advanced Materials Research, 940, 112-115. doi:10.4028/www.scientific.net/amr.940.112Liu, H.-C., Liu, L., Bian, Q.-H., Lin, Q.-L., Dong, N., & Xu, P.-C. (2011). Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory. Expert Systems with Applications, 38(4), 4403-4415. doi:10.1016/j.eswa.2010.09.110Liu, H.-C., Liu, L., Liu, N., & Mao, L.-X. (2012). Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment. Expert Systems with Applications, 39(17), 12926-12934. doi:10.1016/j.eswa.2012.05.031Liu, Y., Fan, Z.-P., Yuan, Y., & Li, H. (2014). A FTA-based method for risk decision-making in emergency response. Computers & Operations Research, 42, 49-57. doi:10.1016/j.cor.2012.08.015Kutlu, A. C., & Ekmekçioğlu, M. (2012). Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications, 39(1), 61-67. doi:10.1016/j.eswa.2011.06.044Chang, C., Liu, P., & Wei, C. (2001). Failure mode and effects analysis using grey theory. Integrated Manufacturing Systems, 12(3), 211-216. doi:10.1108/09576060110391174Bevilacqua, M., Braglia, M., & Gabbrielli, R. (2000). Monte Carlo simulation approach for a modified FMECA in a power plant. Quality and Reliability Engineering International, 16(4), 313-324. doi:10.1002/1099-1638(200007/08)16:43.0.co;2-uLai, G., Debo, L. G., & Sycara, K. (2009). Sharing inventory risk in supply chain: The implication of financial constraint. Omega, 37(4), 811-825. doi:10.1016/j.omega.2008.06.003Carpitella, S., Certa, A., Izquierdo, J., & La Fata, C. M. (2018). A combined multi-criteria approach to support FMECA analyses: A real-world case. Reliability Engineering & System Safety, 169, 394-402. doi:10.1016/j.ress.2017.09.017Mahmoudi, S., Jalali, A., Ahmadi, M., Abasi, P., & Salari, N. (2019). Identifying critical success factors in Heart Failure Self-Care using fuzzy DEMATEL method. Applied Soft Computing, 84, 105729. doi:10.1016/j.asoc.2019.10572

DEMATEL Technique: A Systematic Review of the State-of-the-Art Literature on Methodologies and Applications

Decision making trial and evaluation laboratory (DEMATEL) is considered as an effective method for the identification of cause-effect chain components of a complex system. It deals with evaluating interdependent relationships among factors and finding the critical ones through a visual structural model. Over the recent decade, a large number of studies have been done on the application of DEMATEL and many different variants have been put forward in the literature. The objective of this study is to review systematically the methodologies and applications of the DEMATEL technique. We reviewed a total of 346 papers published from 2006 to 2016 in the international journals. According to the approaches used, these publications are grouped into five categories: classical DEMATEL, fuzzy DEMATEL, grey DEMATEL, analytical network process- (ANP-) DEMATEL, and other DEMATEL. All papers with respect to each category are summarized and analyzed, pointing out their implementing procedures, real applications, and crucial findings. This systematic and comprehensive review holds valuable insights for researchers and practitioners into using the DEMATEL in terms of indicating current research trends and potential directions for further research.Peer Reviewe

Proposing a new methodology for prioritising the investment strategies in the private sector of Iran

This article proposes a systematic and organised approach for group decision-making in the presence of the uncertainty involved in expert judgments as used in multi-criteria decision-making (MCDM) issues. This procedure comprises the selection of the optimum alternative with respect to the evaluation criteria under consideration, in particular to select the strategy of investing. However, the selection of the investment strategy is difficult on account of considering the numerous quantitative and qualitative parameters like benefits, opportunities, costs, and risks. However, it is possible that these parameters have a significant influence on each other. A decision-making trial and evaluation laboratory (DEMATEL), used to define the influential network of elements, can be employed to construct a network relationship map (NRM). On the other hand, according to whether the information is incomplete or unavailable, uncertainty is an inseparable part of making decision for solving the MCDM problems. Therefore, this article proposes a new hybrid model based on analytic hierarchical process (AHP), DEMATEL, and echnique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques under fuzzy environment to evaluate the problem of the selection of the investment strategy. To achieve the aim, a three-step process is presented to solve a sophisticated problem. First, the AHP method is employed to break down the investment problem into simple structure and calculate the importance weights of criteria by using a pairwise comparison process. Second, the DEMATEL technique is applied for considering interdependence and dependencies and computing the global weights of benefit, opportunities, cost, and risk (BOCR) factors. Finally, the fuzzy TOPSIS methodology is used for prioritising the possible alternatives. To demonstrate the potential application of the proposed model, a numerical example is illustrated and investigated. The results show that the proposed model has a high ability to prioritise the strategies of investing

FlowSort-GDSS:a novel group multi-criteria decision support system for sorting problems with application to FMEA

Failure mode and effects analysis (FMEA) is a well-known approach for correlating the failure modes of a system to their effects, with the objective of assessing their criticality. The criticality of a failure mode is traditionally established by its risk priority number (RPN), which is the product of the scores assigned to the three risk factors, which are likeness of occurrence, the chance of being undetected and the severity of the effects. Taking a simple "unweighted" product has major shortcomings. One of them is to provide just a number, which does not sort failures modes into priority classes. Moreover, to make the decision more robust, the FMEA is better tackled by multiple decision-makers. Unfortunately, the literature lacks group decision support systems (GDSS) for sorting failures in the field of the FMEA. In this paper, a novel multi-criteria decision making (MCDM) method named FlowSort-GDSS is proposed to sort the failure modes into priority classes by involving multiple decision-makers. The essence of this method lies in the pair-wise comparison between the failure modes and the reference profiles established by the decision-makers on the risk factors. Finally a case study is presented to illustrate the advantages of this new robust method in sorting failures

A NOVEL TYPE OF FLEXIBLE SOFT ANALYTIC NETWORK PROCESS TO SOLVE THE MULTIPLE-ATTRIBUTE DECISION-MAKING PROBLEM

  Research and development of scientific and technological products have been changing with each passing day in this new millennium. Decisions related to the production of technical products are the key to affecting the sustainable development and market share of enterprises. However, the decision-making related to the production of technology products contains many different evaluation criteria as well as qualitative and quantitative evaluation attributes. Moreover, the correlation between criteria must be considered so it can be treated as a complex multiple-attribute decision-making (MADM) problem. Moreover, performing a multi-attribute decision evaluation often encounters incomplete or missing information provided by experts, which will lead to difficulties in the solution process. In view of the incomplete or missing information of the assessment data, the traditional analytic network process (ANP) method and decision-making trial and evaluation laboratory ANP (DANP) method will delete the incomplete information during the process of assessment and decision-making, and this will bring about non-objective assessment results. In order to solve the above problems, this study proposes a novel type of flexible soft ANP (SANP) method to solve the MADM problems and uses a practical example of smartphone text entry to prove the effectiveness and suitability of the proposed SANP method

Assessing supply chain risks in the automotive industry through a modified MCDM-based FMECA

Supply chains are complex networks that receive assiduous attention in the literature. Like any complex network, a supply chain is subject to a wide variety of risks that can result in significant economic losses and negative impacts in terms of image and prestige for companies. In circumstances of aggressive competition among companies, effective management of supply chain risks (SCR) is crucial, and is currently a very active field of research. Failure Mode Effects and Criticality Analysis (FMECA) has been recently extended to SCR identification and prioritization, aiming at reducing potential losses caused by lack of risk control. This article has a twofold objective. First, SCR assessment is investigated, and a comprehensive list of specific risks related to the automotive industry is compiled to extend the set of most commonly considered risks. Second, an alternative way of calculating the risk priority number (RPN) is proposed within the FMECA framework by means of an integrated multi-criteria decision-making (MCDM) approach. We give a new calculation procedure by making use of the Analytic Hierarchy Process (AHP) to derive factors weights, and then the fuzzy DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) to evaluate the new factor of “dependence” among risks. The developed joint analysis constitutes a risk analysis support tool for criticality in systems engineering. The approach also deals with uncertainty and vagueness associated to input data through the use of fuzzy numbers. The results obtained from a relevant case study in the automotive industry showcase the effectiveness of this approach, which brings important value to those companies: when planning interventions of prevention/mitigation, primary importance should be given to 1) supply chain disruptions due to natural disasters, 2) manufacturing facilities, human resources, policies and breakdown processes, and 3) inefficient transport

Generalized DEMATEL technique with centrality measurements

DEMATEL technique is a useful tool for understanding the influential relationship be­tween criteria of a systematic problematique in structural modeling, and has received much atten­tion in the field of decision analysis recently. However, the past papers focused on the applications of DEMATEL technique and ignored the convergence problem of the approach. In addition, two simple indicators, i.e., in-degree and out-degree centralities, used in DEMATEL technique cannot fully represent the insight of the network relationship. In this paper, we propose a general DE­MATEL technique which incorporated the concept based on interaction diminishing effect. The traditional DEMATEL technique can be considered as a special case of the proposed method when we ignore the effect. Later, we give six important indicators which can be used in DEMATEL tech­nique to conclude the relative importance of criteria. In addition, a numerical example is used to demonstrate the proposed method and the applications of the indicators. First published online: 08 May 201