eprintid: 833
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/08/33
datestamp: 2023-11-09 15:48:58
lastmod: 2023-11-09 15:48:58
status_changed: 2023-11-09 15:38:34
type: book
metadata_visibility: show
creators_name: Vasant, P.
title: Innovative hybrid genetic algorithms and line search method for industrial production management
ispublished: pub
note: cited By 9
abstract: Many engineering, science, information technology and management optimization problems can be considered as non linear programming real world problems where the all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research chapter is to solve non linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers which was represented by logistic membership functions by using hybrid evolutionary optimization approach. To explore the applicability of the present study a numerical example is considered to determine the production planning for the decision variables and profit of the company. © 2010, IGI Global.
date: 2010
publisher: IGI Global
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84866178365&doi=10.4018%2f978-1-61520-809-8.ch008&partnerID=40&md5=7b71aac4a48e7961ef41ce986337c78b
id_number: 10.4018/978-1-61520-809-8.ch008
full_text_status: none
publication: Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques
pagerange: 142-160
refereed: TRUE
isbn: 9781615208098
citation:   Vasant, P.  (2010) Innovative hybrid genetic algorithms and line search method for industrial production management.     IGI Global, pp. 142-160.  ISBN 9781615208098