A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/74343 |
Resumo: | Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach. |
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A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systemsText miningNetwork-based distributed manufacturing systemsMoth flame optimization algorithmSupport vector machinesNaive BayesRandom forestDecision treesSupplier classificationScience & TechnologyRising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach.The project is funded by Department of Science and Technology, Science and Engineering Research Board (DST-SERB), Statutory Body Established through an Act of Parliament: SERB Act 2008, Government of India with Sanction Order No ECR/2016/001808, and also by FCT–Portuguese Foundation for Science and Technology within the R&D Units Projects Scopes: UIDB/00319/2020, UIDP/04077/2020, and UIDB/04077/2020.Multidisciplinary Digital Publishing Institute (MDPI)Universidade do MinhoRamakurthi, Veera BabuManupati, V. K.Machado, JoséVarela, M.L.R.2021-07-082021-07-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/74343engRamakurthi, V.B.; Manupati, V.K.; Machado, J.; Varela, L. A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems. Appl. Sci. 2021, 11, 6314. https://doi.org/10.3390/app111463142076-341710.3390/app11146314https://www.mdpi.com/2076-3417/11/14/6314info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:35:33Zoai:repositorium.sdum.uminho.pt:1822/74343Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:31:25.490635Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems |
title |
A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems |
spellingShingle |
A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems Ramakurthi, Veera Babu Text mining Network-based distributed manufacturing systems Moth flame optimization algorithm Support vector machines Naive Bayes Random forest Decision trees Supplier classification Science & Technology |
title_short |
A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems |
title_full |
A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems |
title_fullStr |
A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems |
title_full_unstemmed |
A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems |
title_sort |
A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems |
author |
Ramakurthi, Veera Babu |
author_facet |
Ramakurthi, Veera Babu Manupati, V. K. Machado, José Varela, M.L.R. |
author_role |
author |
author2 |
Manupati, V. K. Machado, José Varela, M.L.R. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Ramakurthi, Veera Babu Manupati, V. K. Machado, José Varela, M.L.R. |
dc.subject.por.fl_str_mv |
Text mining Network-based distributed manufacturing systems Moth flame optimization algorithm Support vector machines Naive Bayes Random forest Decision trees Supplier classification Science & Technology |
topic |
Text mining Network-based distributed manufacturing systems Moth flame optimization algorithm Support vector machines Naive Bayes Random forest Decision trees Supplier classification Science & Technology |
description |
Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-07-08 2021-07-08T00:00:00Z |
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://hdl.handle.net/1822/74343 |
url |
http://hdl.handle.net/1822/74343 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ramakurthi, V.B.; Manupati, V.K.; Machado, J.; Varela, L. A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems. Appl. Sci. 2021, 11, 6314. https://doi.org/10.3390/app11146314 2076-3417 10.3390/app11146314 https://www.mdpi.com/2076-3417/11/14/6314 |
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 |
Multidisciplinary Digital Publishing Institute (MDPI) |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799132822918660096 |