Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Operations & Production Management (Online) |
Texto Completo: | https://bjopm.org.br/bjopm/article/view/BJV6N1_2009_2 |
Resumo: | Latest manufacturing technologies enhance cross-functional interaction between manufacturing and marketing. In spite of increasingly emphasizing on the aspect of end user’s demand, many production decision-making processes do not take into account only the dynamic nature of the marketer. Here, anattempt has been made to bridge the gap between marketing and partially integrated production problem, with the objective of developing mathematical model that can act as an optimizer in an add-on advanced planning system within an enterprise. Basic idea of this research is the integration of work on determining production and raw material batch sizes under different ordering and delivery assumptions for heuristically evaluating the two-stage batch production problem. Production rate is considered to be a decision variable. Integrated unit production cost function is formulated by considering the various pertinent factors. Proposed model is developed simultaneouslyby formulating constrained maximization problem for marketing division and minimization problem for production division. Considering the complexities for highly non-linear optimization problem, a Computational Intelligence approach is successfully developed and implemented. The model is practical in natureand may be used as an add-on optimizer that co-ordinates distinct function with an aim of maximizing the profit function in any firm. |
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oai:ojs.bjopm.org.br:article/46 |
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Brazilian Journal of Operations & Production Management (Online) |
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Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence ApproachLatest manufacturing technologies enhance cross-functional interaction between manufacturing and marketing. In spite of increasingly emphasizing on the aspect of end user’s demand, many production decision-making processes do not take into account only the dynamic nature of the marketer. Here, anattempt has been made to bridge the gap between marketing and partially integrated production problem, with the objective of developing mathematical model that can act as an optimizer in an add-on advanced planning system within an enterprise. Basic idea of this research is the integration of work on determining production and raw material batch sizes under different ordering and delivery assumptions for heuristically evaluating the two-stage batch production problem. Production rate is considered to be a decision variable. Integrated unit production cost function is formulated by considering the various pertinent factors. Proposed model is developed simultaneouslyby formulating constrained maximization problem for marketing division and minimization problem for production division. Considering the complexities for highly non-linear optimization problem, a Computational Intelligence approach is successfully developed and implemented. The model is practical in natureand may be used as an add-on optimizer that co-ordinates distinct function with an aim of maximizing the profit function in any firm.Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)2010-02-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articleapplication/pdfhttps://bjopm.org.br/bjopm/article/view/BJV6N1_2009_2Brazilian Journal of Operations & Production Management; Vol. 6 No. 1 (2009): July, 2009; 37-622237-8960reponame:Brazilian Journal of Operations & Production Management (Online)instname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROenghttps://bjopm.org.br/bjopm/article/view/BJV6N1_2009_2/pdf_7Roy, Supryioinfo:eu-repo/semantics/openAccess2019-04-04T07:29:26Zoai:ojs.bjopm.org.br:article/46Revistahttps://bjopm.org.br/bjopmONGhttps://bjopm.org.br/bjopm/oaibjopm.journal@gmail.com2237-89601679-8171opendoar:2023-03-13T09:45:01.735648Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach |
title |
Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach |
spellingShingle |
Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach Roy, Supryio |
title_short |
Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach |
title_full |
Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach |
title_fullStr |
Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach |
title_full_unstemmed |
Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach |
title_sort |
Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach |
author |
Roy, Supryio |
author_facet |
Roy, Supryio |
author_role |
author |
dc.contributor.author.fl_str_mv |
Roy, Supryio |
description |
Latest manufacturing technologies enhance cross-functional interaction between manufacturing and marketing. In spite of increasingly emphasizing on the aspect of end user’s demand, many production decision-making processes do not take into account only the dynamic nature of the marketer. Here, anattempt has been made to bridge the gap between marketing and partially integrated production problem, with the objective of developing mathematical model that can act as an optimizer in an add-on advanced planning system within an enterprise. Basic idea of this research is the integration of work on determining production and raw material batch sizes under different ordering and delivery assumptions for heuristically evaluating the two-stage batch production problem. Production rate is considered to be a decision variable. Integrated unit production cost function is formulated by considering the various pertinent factors. Proposed model is developed simultaneouslyby formulating constrained maximization problem for marketing division and minimization problem for production division. Considering the complexities for highly non-linear optimization problem, a Computational Intelligence approach is successfully developed and implemented. The model is practical in natureand may be used as an add-on optimizer that co-ordinates distinct function with an aim of maximizing the profit function in any firm. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-02-07 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/BJV6N1_2009_2 |
url |
https://bjopm.org.br/bjopm/article/view/BJV6N1_2009_2 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/BJV6N1_2009_2/pdf_7 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
dc.source.none.fl_str_mv |
Brazilian Journal of Operations & Production Management; Vol. 6 No. 1 (2009): July, 2009; 37-62 2237-8960 reponame:Brazilian Journal of Operations & Production Management (Online) instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
instname_str |
Associação Brasileira de Engenharia de Produção (ABEPRO) |
instacron_str |
ABEPRO |
institution |
ABEPRO |
reponame_str |
Brazilian Journal of Operations & Production Management (Online) |
collection |
Brazilian Journal of Operations & Production Management (Online) |
repository.name.fl_str_mv |
Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
bjopm.journal@gmail.com |
_version_ |
1797051459589636096 |