eprintid: 2396
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/23/96
datestamp: 2023-11-09 15:50:37
lastmod: 2023-11-09 15:50:37
status_changed: 2023-11-09 15:43:26
type: book
metadata_visibility: show
creators_name: Vasant, P.
title: Hybrid linear search, genetic algorithms, and simulated annealing for fuzzy nonlinear industrial production planning problems
ispublished: pub
note: cited By 11
abstract: This chapter outlines an introduction to real-world industrial problem for product-mix selection involving eight variables and twenty one constraints with fuzzy technological coefficients, and thereafter, a formulation for an optimization approach to solve the problem. This problem occurs in production planning in which a decision maker plays a pivotal role in making decision under fuzzy environment. Decision-maker should be aware of his/her level of satisfaction as well as degree of fuzziness while making the product-mix decision. Thus, a thorough analysis is performed on a modified S-curve membership function for the fuzziness patterns and fuzzy sensitivity solution is found from the various optimization methodologies. An evolutionary algorithm is proposed to capture the optimal solutions respect to the vagueness factor and level of satisfaction. The near global optimal solution for objective function is obtained by hybrid meta-heuristics optimization algorithms such as line search, genetic algorithms, and simulated annealing. © 2013, IGI Global.
date: 2012
publisher: IGI Global
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900610080&doi=10.4018%2f978-1-4666-2086-5.ch003&partnerID=40&md5=07f0bbf9814adb3f5b8f421056c20694
id_number: 10.4018/978-1-4666-2086-5.ch003
full_text_status: none
publication: Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance
pagerange: 87-109
refereed: TRUE
isbn: 9781466620865
citation:   Vasant, P.  (2012) Hybrid linear search, genetic algorithms, and simulated annealing for fuzzy nonlinear industrial production planning problems.     IGI Global, pp. 87-109.  ISBN 9781466620865