Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1111/exsy.12840 http://hdl.handle.net/11449/222507 |
Resumo: | This work has been developed in a large steel industry in Brazil, which produces railway and industrial components, and whose aim was to reduce casting defects. Usually, in industrial processes, identifying the causes of defects and their control are relatively complex activities, due to the many variables involved. In this context, the production processes of seven products, involving 38 process variables (inputs and outputs), have been evaluated adopting a new and innovative procedure. Initially, using a Weighted Goal Programming - Multiple Criteria Data Envelopment Analysis (WGP-MCDEA) model, we identified the most relevant input and output variables, and the studied company validated the results. Next, using the multiple regression technique, empirical functions were constructed for two response variables chosen by the company – number of external cracks and number of internal cracks. Then, to model the real processes adequately, we introduced the occurrence of uncertainty on the coefficients of these functions, considering them as random variables, according to triangular probability functions. Finally, applying the optimizer Optquest, optimization via Monte Carlo simulation (OvMCS) was performed, and with the Ordinary Least Square technique, we obtained the best fit for the two response variables. Specialists from the company validated the proposed procedure. They found that the values of input and output variables obtained by OvMSC, as well as the values of the response variables, belonged to the database available in the ERP system of the company. These results showed that the procedure proposed herein provided feasible and useful solutions to improve the industrial processes under study. |
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Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway componentsThis work has been developed in a large steel industry in Brazil, which produces railway and industrial components, and whose aim was to reduce casting defects. Usually, in industrial processes, identifying the causes of defects and their control are relatively complex activities, due to the many variables involved. In this context, the production processes of seven products, involving 38 process variables (inputs and outputs), have been evaluated adopting a new and innovative procedure. Initially, using a Weighted Goal Programming - Multiple Criteria Data Envelopment Analysis (WGP-MCDEA) model, we identified the most relevant input and output variables, and the studied company validated the results. Next, using the multiple regression technique, empirical functions were constructed for two response variables chosen by the company – number of external cracks and number of internal cracks. Then, to model the real processes adequately, we introduced the occurrence of uncertainty on the coefficients of these functions, considering them as random variables, according to triangular probability functions. Finally, applying the optimizer Optquest, optimization via Monte Carlo simulation (OvMCS) was performed, and with the Ordinary Least Square technique, we obtained the best fit for the two response variables. Specialists from the company validated the proposed procedure. They found that the values of input and output variables obtained by OvMSC, as well as the values of the response variables, belonged to the database available in the ERP system of the company. These results showed that the procedure proposed herein provided feasible and useful solutions to improve the industrial processes under study.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Department of Production São Paulo State UniversityIndustrial Engineering and Management Institute Federal University of Itajubá (UNIFEI)Department of Production São Paulo State UniversityCNPq: 302730/2018CNPq: 303350/2018-0CNPq: 431758/2016-6Universidade Estadual Paulista (UNESP)Federal University of Itajubá (UNIFEI)da Silva, Aneirson Francisco [UNESP]Silva Marins, Fernando Augusto [UNESP]Dias, Erica Ximenes [UNESP]de Carvalho Miranda, Rafael2022-04-28T19:45:11Z2022-04-28T19:45:11Z2022-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1111/exsy.12840Expert Systems, v. 39, n. 2, 2022.1468-03940266-4720http://hdl.handle.net/11449/22250710.1111/exsy.128402-s2.0-85115872992Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengExpert Systemsinfo:eu-repo/semantics/openAccess2022-04-28T19:45:11Zoai:repositorio.unesp.br:11449/222507Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:47:54.029868Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components |
title |
Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components |
spellingShingle |
Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components da Silva, Aneirson Francisco [UNESP] |
title_short |
Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components |
title_full |
Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components |
title_fullStr |
Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components |
title_full_unstemmed |
Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components |
title_sort |
Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components |
author |
da Silva, Aneirson Francisco [UNESP] |
author_facet |
da Silva, Aneirson Francisco [UNESP] Silva Marins, Fernando Augusto [UNESP] Dias, Erica Ximenes [UNESP] de Carvalho Miranda, Rafael |
author_role |
author |
author2 |
Silva Marins, Fernando Augusto [UNESP] Dias, Erica Ximenes [UNESP] de Carvalho Miranda, Rafael |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Federal University of Itajubá (UNIFEI) |
dc.contributor.author.fl_str_mv |
da Silva, Aneirson Francisco [UNESP] Silva Marins, Fernando Augusto [UNESP] Dias, Erica Ximenes [UNESP] de Carvalho Miranda, Rafael |
description |
This work has been developed in a large steel industry in Brazil, which produces railway and industrial components, and whose aim was to reduce casting defects. Usually, in industrial processes, identifying the causes of defects and their control are relatively complex activities, due to the many variables involved. In this context, the production processes of seven products, involving 38 process variables (inputs and outputs), have been evaluated adopting a new and innovative procedure. Initially, using a Weighted Goal Programming - Multiple Criteria Data Envelopment Analysis (WGP-MCDEA) model, we identified the most relevant input and output variables, and the studied company validated the results. Next, using the multiple regression technique, empirical functions were constructed for two response variables chosen by the company – number of external cracks and number of internal cracks. Then, to model the real processes adequately, we introduced the occurrence of uncertainty on the coefficients of these functions, considering them as random variables, according to triangular probability functions. Finally, applying the optimizer Optquest, optimization via Monte Carlo simulation (OvMCS) was performed, and with the Ordinary Least Square technique, we obtained the best fit for the two response variables. Specialists from the company validated the proposed procedure. They found that the values of input and output variables obtained by OvMSC, as well as the values of the response variables, belonged to the database available in the ERP system of the company. These results showed that the procedure proposed herein provided feasible and useful solutions to improve the industrial processes under study. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-28T19:45:11Z 2022-04-28T19:45:11Z 2022-02-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1111/exsy.12840 Expert Systems, v. 39, n. 2, 2022. 1468-0394 0266-4720 http://hdl.handle.net/11449/222507 10.1111/exsy.12840 2-s2.0-85115872992 |
url |
http://dx.doi.org/10.1111/exsy.12840 http://hdl.handle.net/11449/222507 |
identifier_str_mv |
Expert Systems, v. 39, n. 2, 2022. 1468-0394 0266-4720 10.1111/exsy.12840 2-s2.0-85115872992 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Expert Systems |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129119152504832 |