@book{scholars14252,
            note = {cited By 1},
             doi = {10.4018/978-1-7998-7176-7.ch010},
            year = {2021},
           title = {Bilevel optimization of taxing strategies for carbon emissions using fuzzy random matrix generators},
       publisher = {IGI Global},
         journal = {Smart Cities and Machine Learning in Urban Health},
           pages = {210--234},
        abstract = {Bilevel (BL) optimization of taxing strategies in consideration of carbon emissions was carried out in this work. The BL optimization problem was considered with two primary targets: (1) designing an optimal taxing strategy (imposed on power generation companies) and (2) developing optimal economic dispatch (ED) schema (by power generation companies) in response to tax rates. The resulting interaction was represented using Stackelberg game theory - where the novel fuzzy random matrix generators were used in tandem with the cuckoo search (CS) technique. Fuzzy random matrices were developed by modifying certain aspects of the original random matrix theory. The novel methodology was tailored for tackling complex optimization systems with intermediate complexity such as the application problem tackled in this work. Detailed performance and comparative analysis are also presented in this chapter. {\^A}{\copyright} 2022, IGI Global.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136375268&doi=10.4018\%2f978-1-7998-7176-7.ch010&partnerID=40&md5=caadb2916d04a92661d0785958c8b374},
            isbn = {9781799871781; 9781799871767},
          author = {Ganesan, T. and Elamvazuthi, I.}
}