Optimum assembly line balancing: A stochastic programming approach

Assembly line balancing problem is an approach of assigning a set of tasks to an ordered sequence of workstations. This assignment needs to be made in such a way that the underlying precedence constraints are not violated and efficiency measures are optimized subject to the restriction of the cycle...

Full description

Bibliographic Details
Main Authors: Dilip Roy, Debdip khan
Format: Article
Language:English
Published: Growing Science 2011-04-01
Series:International Journal of Industrial Engineering Computations
Subjects:
Online Access:http://www.growingscience.com/ijiec/Vol2/IJIEC_2010_24.pdf
id doaj-4fdbe96fc74e413ca10df23ae5c1360f
record_format Article
spelling doaj-4fdbe96fc74e413ca10df23ae5c1360f2020-11-24T20:54:38ZengGrowing ScienceInternational Journal of Industrial Engineering Computations1923-29261923-29342011-04-0122329336Optimum assembly line balancing: A stochastic programming approachDilip RoyDebdip khanAssembly line balancing problem is an approach of assigning a set of tasks to an ordered sequence of workstations. This assignment needs to be made in such a way that the underlying precedence constraints are not violated and efficiency measures are optimized subject to the restriction of the cycle time constraint. Research works, reported so far, mainly deal with the minimization of balancing loss, subject to precedence constraints. Lack of uniqueness in those optimum solutions and pressing demand to include system loss in the objective function have led to the present work of minimization of both balancing and system loss. As there is no standard measure for system loss, the current work proposes a measure for system loss and offers solution to this bi-objective problem. http://www.growingscience.com/ijiec/Vol2/IJIEC_2010_24.pdfSlacknessAssembly lineSystem lossBalancing lossInteger ProgrammingStochastic Line balancing
collection DOAJ
language English
format Article
sources DOAJ
author Dilip Roy
Debdip khan
spellingShingle Dilip Roy
Debdip khan
Optimum assembly line balancing: A stochastic programming approach
International Journal of Industrial Engineering Computations
Slackness
Assembly line
System loss
Balancing loss
Integer Programming
Stochastic Line balancing
author_facet Dilip Roy
Debdip khan
author_sort Dilip Roy
title Optimum assembly line balancing: A stochastic programming approach
title_short Optimum assembly line balancing: A stochastic programming approach
title_full Optimum assembly line balancing: A stochastic programming approach
title_fullStr Optimum assembly line balancing: A stochastic programming approach
title_full_unstemmed Optimum assembly line balancing: A stochastic programming approach
title_sort optimum assembly line balancing: a stochastic programming approach
publisher Growing Science
series International Journal of Industrial Engineering Computations
issn 1923-2926
1923-2934
publishDate 2011-04-01
description Assembly line balancing problem is an approach of assigning a set of tasks to an ordered sequence of workstations. This assignment needs to be made in such a way that the underlying precedence constraints are not violated and efficiency measures are optimized subject to the restriction of the cycle time constraint. Research works, reported so far, mainly deal with the minimization of balancing loss, subject to precedence constraints. Lack of uniqueness in those optimum solutions and pressing demand to include system loss in the objective function have led to the present work of minimization of both balancing and system loss. As there is no standard measure for system loss, the current work proposes a measure for system loss and offers solution to this bi-objective problem.
topic Slackness
Assembly line
System loss
Balancing loss
Integer Programming
Stochastic Line balancing
url http://www.growingscience.com/ijiec/Vol2/IJIEC_2010_24.pdf
work_keys_str_mv AT diliproy optimumassemblylinebalancingastochasticprogrammingapproach
AT debdipkhan optimumassemblylinebalancingastochasticprogrammingapproach
_version_ 1716793845488812032