A New Decision Support Tool for Dynamic Risks Analysis in Collaborative Networks

Collaborative networks are complex systems and consist of many factors with dependencies among them. Although the number of collaborative networks such as advanced supply chains or virtual organizations/laboratories/e-science is growing and their significance is increasing in the world, many of them are unsuccessful. In addition, very little attention has been paid to the risk analysis of collaborative networks by considering the dependencies among risk factors. So, the precise risks analysis associated with collaborative networks projects is crucial to attain a satisfactory performance. To address this, we are proposing an advanced decision support tool called “Fuzzy Cognitive Maps” (FCM) which can deal with risks of such complicated systems by considering the interrelationships between factors. FCM states the behaviour of complex systems accurately and illustrate any complex environment based on the experts’ perceptions and by graphical representations. It is able to consider uncertainties, imprecise information, the interactions between risk factors, Information scarcity, and several decision maker’s opinions. FCM is not only able to evaluate risks more precisely in collaborative networks, but also it could be applied in different decision makings problems related to collaborative networks such as partner selection and forecasting behaviors, policy analysis, modeling collaboration preparedness assessment, etc. Hence, the proposed tool would help practitioners to manage collaborative network risks and decision making problems effectively and proactively.

[1]  Somayeh Alizadeh,et al.  Learning FCM by chaotic simulated annealing , 2009 .

[2]  Xingwei Wang,et al.  A fuzzy synthetic evaluation embedded tabu search for risk programming of virtual enterprises , 2008 .

[3]  Michael Glykas,et al.  Fuzzy Cognitive Maps , 2010 .

[4]  Tak Kuen Siu,et al.  A distributed decision making model for risk management of virtual enterprise , 2011, Expert Syst. Appl..

[5]  V. Swaminathan,et al.  Factors influencing partner selection in strategic alliances: the moderating role of alliance context , 2008 .

[6]  Jose L. Salmeron,et al.  Dynamic risks modelling in ERP maintenance projects with FCM , 2014, Inf. Sci..

[7]  Min Huang,et al.  Genetic algorithm solution for a risk-based partner selection problem in a virtual enterprise , 2003, Comput. Oper. Res..

[8]  Yuan Li,et al.  Decision support for risk analysis on dynamic alliance , 2007, Decis. Support Syst..

[9]  Huang Hexin,et al.  A partner selection method based on risk evaluation in virtual enterprises , 2005, Proceedings of ICSSSM '05. 2005 International Conference on Services Systems and Services Management, 2005..

[10]  Elpiniki I. Papageorgiou,et al.  Learning Algorithms for Fuzzy Cognitive Maps—A Review Study , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Jose L. Salmeron,et al.  Fuzzy Cognitive Maps-Based IT Projects Risks Scenarios , 2010 .

[12]  Afshin Jamshidi,et al.  A new framework for risk assessment in ERP maintenance , 2014, 2014 Reliability and Maintainability Symposium.

[13]  Gwo-Hshiung Tzeng,et al.  A soft computing method for multi-criteria decision making with dependence and feedback , 2006, Appl. Math. Comput..

[14]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[15]  Afshin Jamshidi,et al.  A comprehensive fuzzy risk-based maintenance framework for prioritization of medical devices , 2015, Appl. Soft Comput..

[16]  Elpiniki I. Papageorgiou,et al.  Fuzzy Cognitive Maps for Applied Sciences and Engineering - From Fundamentals to Extensions and Learning Algorithms , 2013, Fuzzy Cognitive Maps for Applied Sciences and Engineering.

[17]  Chrysostomos D. Stylios,et al.  Active Hebbian learning algorithm to train fuzzy cognitive maps , 2004, Int. J. Approx. Reason..

[18]  Bart Kosko,et al.  Fuzzy Engineering , 1996 .

[19]  Michael N. Vrahatis,et al.  Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization , 2005, Journal of Intelligent Information Systems.

[20]  Gerald R. Ferris,et al.  Firm relationships: The dynamics of effective organization alliances , 2011 .

[21]  Afshin Jamshidi,et al.  Using Fuzzy Cost‐Based FMEA, GRA and Profitability Theory for Minimizing Failures at a Healthcare Diagnosis Service , 2015, Qual. Reliab. Eng. Int..

[22]  Luis M. Camarinha-Matos,et al.  Modeling collaboration preparedness assessment , 2008 .

[23]  Mi Lu,et al.  Risk Evaluation of Dynamic Alliance Based on Fuzzy Analytic Network Process and Fuzzy TOPSIS , 2012 .

[24]  Lingling Li,et al.  An integrated FCM and fuzzy soft set for supplier selection problem based on risk evaluation , 2012 .

[25]  T. Das,et al.  A risk perception model of alliance structuring , 2001 .

[26]  Dimitris E. Koulouriotis,et al.  Training Fuzzy Cognitive Maps by using Hebbian learning algorithms: A comparative study , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).