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

Optimal placement of pressure sensors using fuzzy DEMATEL-based sensor influence

[EN] Nowadays, optimal sensor placement (OSP) for leakage detection in water distribution networks is a lively field of research, and a challenge for water utilities in terms of network control, management, and maintenance. How many sensors to install and where to install them are crucial decisions to make for those utilities to reach a trade-off between efficiency and economy. In this paper, we address the where-to-install-them part of the OSP through the following elements: nodes' sensitivity to leakage, uncertainty of information, and redundancy through conditional entropy maximisation. We evaluate relationships among candidate sensors in a network to get a picture of the mutual influence among the nodes. This analysis is performed within a multi-criteria decision-making approach: specifically, a herein proposed variant of DEMATEL, which uses fuzzy logic and builds comparison matrices derived from information obtained through leakage simulations of the network. We apply the proposal first to a toy example to show how the approach works, and then to a real-world case study.This research has been partially supported by the CNPq grant with number 156213/2018-4.Frances-Chust, J.; Brentan, BM.; Carpitella, S.; Izquierdo Sebastián, J.; Montalvo, I. (2020). Optimal placement of pressure sensors using fuzzy DEMATEL-based sensor influence. Water. 12(2):1-18. https://doi.org/10.3390/w12020493S118122Li, J., Wang, C., Qian, Z., & Lu, C. (2019). Optimal sensor placement for leak localization in water distribution networks based on a novel semi-supervised strategy. Journal of Process Control, 82, 13-21. doi:10.1016/j.jprocont.2019.08.001Pérez, R., Puig, V., Pascual, J., Quevedo, J., Landeros, E., & Peralta, A. (2011). Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Engineering Practice, 19(10), 1157-1167. doi:10.1016/j.conengprac.2011.06.004Boatwright, S., Romano, M., Mounce, S., Woodward, K., & Boxall, J. (s. f.). Optimal Sensor Placement and Leak/Burst Localisation in a Water Distribution System Using Spatially-Constrained Inverse-Distance Weighted Interpolation. doi:10.29007/37cpBlesa, J., Nejjari, F., & Sarrate, R. (2015). Robust sensor placement for leak location: analysis and design. Journal of Hydroinformatics, 18(1), 136-148. doi:10.2166/hydro.2015.021Steffelbauer, D. B., & Fuchs-Hanusch, D. (2016). Efficient Sensor Placement for Leak Localization Considering Uncertainties. Water Resources Management, 30(14), 5517-5533. doi:10.1007/s11269-016-1504-6Yoo, D., Chang, D., Song, Y., & Lee, J. (2018). Optimal Placement of Pressure Gauges for Water Distribution Networks Using Entropy Theory Based on Pressure Dependent Hydraulic Simulation. Entropy, 20(8), 576. doi:10.3390/e20080576De Schaetzen, W. B. ., Walters, G. ., & Savic, D. . (2000). Optimal sampling design for model calibration using shortest path, genetic and entropy algorithms. Urban Water, 2(2), 141-152. doi:10.1016/s1462-0758(00)00052-2Cugueró-Escofet, M. À., Puig, V., & Quevedo, J. (2017). Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network. Control Engineering Practice, 63, 1-12. doi:10.1016/j.conengprac.2017.03.003Sela Perelman, L., Abbas, W., Koutsoukos, X., & Amin, S. (2016). Sensor placement for fault location identification in water networks: A minimum test cover approach. Automatica, 72, 166-176. doi:10.1016/j.automatica.2016.06.005Carpitella, S., Carpitella, F., Certa, A., Benítez, J., & Izquierdo, J. (2018). Managing Human Factors to Reduce Organisational Risk in Industry. Mathematical and Computational Applications, 23(4), 67. doi:10.3390/mca23040067Addae, B. A., Zhang, L., Zhou, P., & Wang, F. (2019). Analyzing barriers of Smart Energy City in Accra with two-step fuzzy DEMATEL. Cities, 89, 218-227. doi:10.1016/j.cities.2019.01.043Dalvi-Esfahani, M., Niknafs, A., Kuss, D. J., Nilashi, M., & Afrough, S. (2019). Social media addiction: Applying the DEMATEL approach. Telematics and Informatics, 43, 101250. doi:10.1016/j.tele.2019.101250Quezada, L. E., López-Ospina, H. A., Palominos, P. I., & Oddershede, A. M. (2018). Identifying causal relationships in strategy maps using ANP and DEMATEL. Computers & Industrial Engineering, 118, 170-179. doi:10.1016/j.cie.2018.02.020Nilashi, M., Samad, S., Manaf, A. A., Ahmadi, H., Rashid, T. A., Munshi, A., … Hassan Ahmed, O. (2019). Factors influencing medical tourism adoption in Malaysia: A DEMATEL-Fuzzy TOPSIS approach. Computers & Industrial Engineering, 137, 106005. doi:10.1016/j.cie.2019.106005Zhang, L., Sun, X., & Xue, H. (2019). Identifying critical risks in Sponge City PPP projects using DEMATEL method: A case study of China. Journal of Cleaner Production, 226, 949-958. doi:10.1016/j.jclepro.2019.04.067Du, Y.-W., & Zhou, W. (2019). New improved DEMATEL method based on both subjective experience and objective data. Engineering Applications of Artificial Intelligence, 83, 57-71. doi:10.1016/j.engappai.2019.05.001Yazdi, M., Nedjati, A., Zarei, E., & Abbassi, R. (2020). A novel extension of DEMATEL approach for probabilistic safety analysis in process systems. Safety Science, 121, 119-136. doi:10.1016/j.ssci.2019.09.006Chen, Z., Ming, X., Zhang, X., Yin, D., & Sun, Z. (2019). A rough-fuzzy DEMATEL-ANP method for evaluating sustainable value requirement of product service system. Journal of Cleaner Production, 228, 485-508. doi:10.1016/j.jclepro.2019.04.145Wu, W.-W., & Lee, Y.-T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499-507. doi:10.1016/j.eswa.2005.12.005Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xMahmoudi, 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.2019.105729Lin, K.-P., Tseng, M.-L., & Pai, P.-F. (2018). Sustainable supply chain management using approximate fuzzy DEMATEL method. Resources, Conservation and Recycling, 128, 134-142. doi:10.1016/j.resconrec.2016.11.017Vardopoulos, I. (2019). Critical sustainable development factors in the adaptive reuse of urban industrial buildings. A fuzzy DEMATEL approach. Sustainable Cities and Society, 50, 101684. doi:10.1016/j.scs.2019.101684Mirmousa, S., & Dehnavi, H. D. (2016). Development of Criteria of Selecting the Supplier by Using the Fuzzy DEMATEL Method. Procedia - Social and Behavioral Sciences, 230, 281-289. doi:10.1016/j.sbspro.2016.09.036Acuña-Carvajal, F., Pinto-Tarazona, L., López-Ospina, H., Barros-Castro, R., Quezada, L., & Palacio, K. (2019). An integrated method to plan, structure and validate a business strategy using fuzzy DEMATEL and the balanced scorecard. Expert Systems with Applications, 122, 351-368. doi:10.1016/j.eswa.2019.01.030Chou, J.-S., & Ongkowijoyo, C. S. (2019). Hybrid decision-making method for assessing interdependency and priority of critical infrastructure. International Journal of Disaster Risk Reduction, 39, 101134. doi:10.1016/j.ijdrr.2019.101134Winter, C. de, Palleti, V. R., Worm, D., & Kooij, R. (2019). Optimal placement of imperfect water quality sensors in water distribution networks. Computers & Chemical Engineering, 121, 200-211. doi:10.1016/j.compchemeng.2018.10.021Schwaller, J., & van Zyl, J. E. (2015). Modeling the Pressure-Leakage Response of Water Distribution Systems Based on Individual Leak Behavior. Journal of Hydraulic Engineering, 141(5), 04014089. doi:10.1061/(asce)hy.1943-7900.0000984Giustolisi, O., Savic, D., & Kapelan, Z. (2008). Pressure-Driven Demand and Leakage Simulation for Water Distribution Networks. Journal of Hydraulic Engineering, 134(5), 626-635. doi:10.1061/(asce)0733-9429(2008)134:5(626)EPANET 2: Users Manualhttps://epanet.es/wp-content/uploads/2012/10/EPANET_User_Guide.pdfChristodoulou, S. E., Gagatsis, A., Xanthos, S., Kranioti, S., Agathokleous, A., & Fragiadakis, M. (2013). Entropy-Based Sensor Placement Optimization for Waterloss Detection in Water Distribution Networks. Water Resources Management, 27(13), 4443-4468. doi:10.1007/s11269-013-0419-8Falatoonitoosi, E., Leman, Z., Sorooshian, S., & Salimi, M. (2013). Decision-Making Trial and Evaluation Laboratory. Research Journal of Applied Sciences, Engineering and Technology, 5(13), 3476-3480. doi:10.19026/rjaset.5.4475OPRICOVIC, S., & TZENG, G.-H. (2003). DEFUZZIFICATION WITHIN A MULTICRITERIA DECISION MODEL. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(05), 635-652. doi:10.1142/s0218488503002387Sara, J., Stikkelman, R. M., & Herder, P. M. (2015). Assessing relative importance and mutual influence of barriers for CCS deployment of the ROAD project using AHP and DEMATEL methods. International Journal of Greenhouse Gas Control, 41, 336-357. doi:10.1016/j.ijggc.2015.07.008Alperovits, E., & Shamir, U. (1977). Design of optimal water distribution systems. Water Resources Research, 13(6), 885-900. doi:10.1029/wr013i006p00885Walski, T., Bezts, W., Posluszny, E. T., Weir, M., & Whitman, B. E. (2006). Modeling leakage reduction through pressure control. Journal - American Water Works Association, 98(4), 147-155. doi:10.1002/j.1551-8833.2006.tb07642.xZheng, F., Du, J., Diao, K., Zhang, T., Yu, T., & Shao, Y. (2018). Investigating Effectiveness of Sensor Placement Strategies in Contamination Detection within Water Distribution Systems. Journal of Water Resources Planning and Management, 144(4), 06018003. doi:10.1061/(asce)wr.1943-5452.0000919Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2014). Water Distribution System Computer-Aided Design by Agent Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering, 29(6), 433-448. doi:10.1111/mice.1206

Dynamics under Uncertainty: Modeling Simulation and Complexity

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc

An Integrated Decision-Making Method Based on Neutrosophic Numbers for Investigating Factors of Coastal Erosion

The recent boom of various integrated decision-making methods has attracted many researchers to the field. The recent integrated Analytic Network Process and Decision Making Trial and Evaluation Laboratory (ANP–DEMATEL) methods were developed based on crisp numbers and fuzzy numbers. However, these numbers are incapable of dealing with the indeterminant and inconsistent information that exists in real-life problems

A new approach to analyze strategy map using an integrated BSC and FUZZY DEMATEL

Today, with ever-increasing competition in global economic conditions, the necessity of effective implementation of strategy map has become an inevitable and necessary. The strategy map represents a general and structured framework for strategic objectives and plays an important role in forming competitive advantages for organizations. It is important to find important factors influencing strategy map and prioritize them based on suitable factors. In this paper, we propose an integration of BSC and Fuzzy DEMATEL technique to rank different items influencing strategy of a production plan. The proposed technique is implemented for real-world case study of glass production

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

Supplier Selection Model Based on D Numbers and Transformation Function

Selecting reasonable suppliers can effectively improve the efficiency of enterprise supply chain management. Among them, expert evaluation is an important part of supplier selection problem, but the uncertainty, fuzziness and incompleteness of expert opinions make supplier selection problem difficult to solve. In order to systematically and effectively solve the uncertainty, ambiguity and incompleteness in supplier selection problem, this paper presents a new supplier selection method based on D numbers and transformation function. First, fuzzy preference relation is generated based on the decision matrix of pairwise comparisons given by experts. D numbers which can effectively deal with uncertain information extend fuzzy preference relation (D matrix). Second, the D matrix is converted into a crisp matrix form based on the integration representation of D numbers according to different situations whether or not the information in D matrix is complete. Third, the crisp matrix is converted into judgement matrix by using the transformation functions. Finally, analytic hierarchy process (AHP) method is applied based on the judgment matrix to give a priority weights for decision making. Three numerical examples and application of the supplier selection are used to show the feasibility and effectiveness of the proposed method

Bibliometric analysis of scientific production on methods to aid decision making in the last 40 years

Purpose: Multicriteria methods have gained traction in both academia and industry practices for effective decision-making over the years. This bibliometric study aims to explore and provide an overview of research carried out on multicriteria methods, in its various aspects, over the past forty-four years. Design/Methodology/Approach: The Web of Science (WoS) and Scopus databases were searched for publications from January 1945 to April 29, 2021, on multicriteria methods in titles, abstracts, and keywords. The bibliographic data were analyzed using the R bibliometrix package. Findings: This bibliometric study asserts that 29,050 authors have produced 20,861 documents on the theme of multicriteria methods in 131 countries in the last forty-four years. Scientific production in this area grows at a rate of 13.88 per year. China is the leading country in publications with 14.14%; India with 10.76%; and Iran with 8.09%. Islamic Azad University leads others with 504 publications, followed by the Vilnius Gediminas Technical University with 456 and the National Institute of Technology with 336. As for journals, Expert Systems With Applications; Sustainability; and Journal of Cleaner Production are the leading journals, which account for more than 4.67% of all indexed literature. Furthermore, Zavadskas E. and Wang J have the highest publications in the multicriteria methods domain regarding the authors. Regarding the most commonly used multicriteria decision-making methods, AHP is the most favored approach among the ten countries with the most publications in this research area, followed by TOPSIS, VIKOR, PROMETHEE, and ANP. Practical implications: The bibliometric literature review method allows the researchers to explore the multicriteria research area more extensively than the traditional literature review method. It enables a large dataset of bibliographic records to be systematically analyzed through statistical measures, yielding informative insights. Originality/value: The usefulness of this bibliometric study is summed in presenting an overview of the topic of the multicriteria methods during the previous forty-four years, allowing other academics to use this research as a starting point for their research

Generalized Hamacher aggregation operators for intuitionistic uncertain linguistic sets: Multiple attribute group decision making methods

© 2019 by the authors. In this paper, we consider multiple attribute group decision making (MAGDM) problems in which the attribute values take the form of intuitionistic uncertain linguistic variables. Based on Hamacher operations, we developed several Hamacher aggregation operators, which generalize the arithmetic aggregation operators and geometric aggregation operators, and extend the algebraic aggregation operators and Einstein aggregation operators. A number of special cases for the two operators with respect to the parameters are discussed in detail. Also, we developed an intuitionistic uncertain linguistic generalized Hamacher hybrid weighted average operator to reflect the importance degrees of both the given intuitionistic uncertain linguistic variables and their ordered positions. Based on the generalized Hamacher aggregation operator, we propose a method for MAGDM for intuitionistic uncertain linguistic sets. Finally, a numerical example and comparative analysis with related decision making methods are provided to illustrate the practicality and feasibility of the proposed method