eprintid: 14252
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/42/52
datestamp: 2023-11-10 03:28:49
lastmod: 2023-11-10 03:28:49
status_changed: 2023-11-10 01:56:25
type: book
metadata_visibility: show
creators_name: Ganesan, T.
creators_name: Elamvazuthi, I.
title: Bilevel optimization of taxing strategies for carbon emissions using fuzzy random matrix generators
ispublished: pub
note: cited By 1
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. © 2022, IGI Global.
date: 2021
publisher: IGI Global
official_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
id_number: 10.4018/978-1-7998-7176-7.ch010
full_text_status: none
publication: Smart Cities and Machine Learning in Urban Health
pagerange: 210-234
refereed: TRUE
isbn: 9781799871781; 9781799871767
citation:   Ganesan, T. and Elamvazuthi, I.  (2021) Bilevel optimization of taxing strategies for carbon emissions using fuzzy random matrix generators.     IGI Global, pp. 210-234.  ISBN 9781799871781; 9781799871767