eprintid: 3420
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/34/20
datestamp: 2023-11-09 15:51:40
lastmod: 2023-11-09 15:51:40
status_changed: 2023-11-09 15:46:47
type: article
metadata_visibility: show
creators_name: Vasant, P.
title: Hybrid Mesh Adaptive Direct Search Genetic Algorithms and Line Search Approaches for Fuzzy Optimization Problems in Production Planning
ispublished: pub
note: cited By 15
abstract: In this chapter, the main significant contributions are formulation of a new non-linear membership function using fuzzy approach to capture and describe vagueness in the technological coefficients of constraints in the industrial production planning problems. This non-linear membership function is flexible and convenience to the decision makers in their decision making process. Secondly, a nonlinear objective function in the form of cubic function for fuzzy optimization problems is successfully solved by two hybrid optimization techniques from the area of soft computing and classical approaches. Among the two techniques, one outstanding technique is selected based on the quality of the solution. An intelligent performance analysis is adapted to the convenience of decision makers and implementers to select the niche optimization techniques to apply in real word problem solving approach particularly related to industrial engineering problems. © Springer-Verlag Berlin Heidelberg 2013.
date: 2013
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885449933&doi=10.1007%2f978-3-642-30504-7_30&partnerID=40&md5=483f248e1ae69f0073caf81a14871e96
id_number: 10.1007/978-3-642-30504-7₃₀
full_text_status: none
publication: Intelligent Systems Reference Library
volume: 38
pagerange: 779-799
refereed: TRUE
isbn: 9783642305030
issn: 18684394
citation:   Vasant, P.  (2013) Hybrid Mesh Adaptive Direct Search Genetic Algorithms and Line Search Approaches for Fuzzy Optimization Problems in Production Planning.  Intelligent Systems Reference Library, 38.  pp. 779-799.  ISSN 18684394