Intelligent Decision Support Systems for Sustainable Computing

In sustainable computing, Intelligent Decision Support Systems (IDSS) has been adopted for prediction, optimization and decision making challenges under variable number constraints based on un-structured data. The traditional systems are lack of efficiency, limited computational ability, inadequate and impreciseness nature of handling sustainable problems. Despite, Computational Intelligence (CI) paradigms have used for high computational power of intelligence system to integrate, analyze and share large volume of un-structured data in a real time, using diverse analytical techniques to discover sustainable information suitable for better decision making . In addition, CI has the ability to handle complex data using sophisticated mathematical models, analytical techniques. This chapter provides a brief overview of computational intelligence (CI) paradigms and its noteworthy character in intelligent decision support and analytics of sustainable computing problems. The objective of this chapter is to study and analyze the effect of CI for overall advancement of emerging sustainable computing technologies.

[1]  Ru-Jen Lin Using fuzzy DEMATEL to evaluate the green supply chain management practices , 2013 .

[2]  Xin-She Yang,et al.  Engineering Optimization: An Introduction with Metaheuristic Applications , 2010 .

[3]  Paul J. Werbos,et al.  Computational Intelligence for the Smart Grid-History, Challenges, and Opportunities , 2011, IEEE Computational Intelligence Magazine.

[4]  Vipul Jain,et al.  Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry , 2018, Neural Computing and Applications.

[5]  Wanliang Wang,et al.  Bio-Inspired Optimization of Sustainable Energy Systems: A Review , 2013 .

[6]  Oluwarotimi Williams Samuel,et al.  An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction , 2017, Expert Syst. Appl..

[7]  Mazidah Puteh,et al.  An overview of Gravitational Search Algorithm utilization in optimization problems , 2013, 2013 IEEE 3rd International Conference on System Engineering and Technology.

[8]  Zbigniew Michalewicz,et al.  Parameter control in evolutionary algorithms , 1999, IEEE Trans. Evol. Comput..

[9]  N. D. Md Sin,et al.  Application of metaheuristic algorithms in nano-process parameter optimization , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[10]  Lakshman S. Thakur,et al.  Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain , 2012, Expert Syst. Appl..

[11]  Arun Kumar Sangaiah,et al.  An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome , 2015, Neural Computing and Applications.