NDLI: Evaluation of waste management systems using fuzzy cognitive maps and optimization

Content Provider	IEEE Xplore Digital Library
Author	Buruzs, A. Hatwagner, M.F. Foldesi, P. Koczy, L.T.
Copyright Year	2015
Description	Author affiliation: Dept. of Logistics, Szechenyi Istvan Univ., Györ, Hungary (Foldesi, P.) \|\| Dept. of Environ. Eng., Szechenyi Istvan Univ., Györ, Hungary (Buruzs, A.) \|\| Dept. of Inf. Technol., Szechenyi Istvan Univ., Györ, Hungary (Hatwagner, M.F.; Koczy, L.T.)
Abstract	Integrated Waste Management Systems (IWMS) are very complex systems with a lot of uncertainty. These can be defined as the selection and application of suitable techniques, technologies and management programs to achieve waste management objectives and goals. In order to support the decision making process in waste management we propose the use of Fuzzy Cognitive Map (FCM) and Bacterial Evolutionary Algorithm (BEA) methods since the combination of the FCM and BEA seem to be suitable to model complex mechanisms such as IWMS. While the FCM is formed for a chosen system by determining the concepts and their relationships, it is possible to quantitatively simulate the system considering its parameters. However, if the time series of the factors of the system are known, then the connection matrix of FCM, thus the causal relations among the parameters can be determined by optimization. This way a more objective description of IWMS can be given.
Starting Page	1
Ending Page	6
File Size	301391
Page Count	6
File Format	PDF
ISBN	9781479978625
DOI	10.1109/ASCC.2015.7244894
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-31
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Waste management Algorithm design and analysis Microorganisms Time series analysis optimization Evolutionary computation Fuzzy Cognitive Maps Integrated Waste Management System Bacterial Evolutionary Algorithm Optimization
Content Type	Text
Resource Type	Article

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