Combinatorial framework for reducing tardiness in multi-machine scheduling using EDD, NEH and genetic algorithm

[EN] No-idle flow shop scheduling is a critical challenge in manufacturing, where minimising overall tardiness directly impacts efficiency and customer satisfaction. This study introduces a novel hybrid algorithm, EDD-NEH-GA (ENG), which integrates Earliest Due Date (EDD) and Nawaz-Enscore-Ham (NEH)...

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Autores: Bari, Prasad, Deshmukh, Nilaj, Yadav, Pradeep, Karande, Prasad, Bari, Poonam
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/232249
Acceso en línea:https://riunet.upv.es/handle/10251/232249
Access Level:acceso abierto
Palabra clave:Genetic Algorithm
Tardiness
Scheduling
Combinatorial Approach
NEH
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spelling Combinatorial framework for reducing tardiness in multi-machine scheduling using EDD, NEH and genetic algorithmBari, PrasadDeshmukh, NilajYadav, PradeepKarande, PrasadBari, PoonamGenetic AlgorithmTardinessSchedulingCombinatorial ApproachNEH[EN] No-idle flow shop scheduling is a critical challenge in manufacturing, where minimising overall tardiness directly impacts efficiency and customer satisfaction. This study introduces a novel hybrid algorithm, EDD-NEH-GA (ENG), which integrates Earliest Due Date (EDD) and Nawaz-Enscore-Ham (NEH) heuristics with a Genetic Algorithm (GA) to balance global exploration and local optimisation. The objective is to overcome premature convergence and achieve superior tardiness reduction. Computational experiments on Taillard s benchmark instances demonstrate ENG s effectiveness compared to the Mixed Integer Linear Programming (MILP) based approach by Balogh. Across all tested cases, ENG updated 93% of previously best-known solutions, achieving an average tardiness reduction of 18 52%. These results confirm ENG as a robust and efficient solution for complex no-idle flow shop environments, offering significant gains in scheduling performance and operational productivity. After performing statistical analysis it is noted that ENG outperforms. ENG significantly improved scheduling performance, averaging 8430 units of reduction in overall tardiness. According to the standard error of the difference (SE = 1859), this improvement appears to be constant across cases.Universitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20262026-01-31journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/232249reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Compartir igual (by-nc-sa) http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2322492026-06-13T07:49:27Z
dc.title.none.fl_str_mv Combinatorial framework for reducing tardiness in multi-machine scheduling using EDD, NEH and genetic algorithm
title Combinatorial framework for reducing tardiness in multi-machine scheduling using EDD, NEH and genetic algorithm
spellingShingle Combinatorial framework for reducing tardiness in multi-machine scheduling using EDD, NEH and genetic algorithm
Bari, Prasad
Genetic Algorithm
Tardiness
Scheduling
Combinatorial Approach
NEH
title_short Combinatorial framework for reducing tardiness in multi-machine scheduling using EDD, NEH and genetic algorithm
title_full Combinatorial framework for reducing tardiness in multi-machine scheduling using EDD, NEH and genetic algorithm
title_fullStr Combinatorial framework for reducing tardiness in multi-machine scheduling using EDD, NEH and genetic algorithm
title_full_unstemmed Combinatorial framework for reducing tardiness in multi-machine scheduling using EDD, NEH and genetic algorithm
title_sort Combinatorial framework for reducing tardiness in multi-machine scheduling using EDD, NEH and genetic algorithm
dc.creator.none.fl_str_mv Bari, Prasad
Deshmukh, Nilaj
Yadav, Pradeep
Karande, Prasad
Bari, Poonam
author Bari, Prasad
author_facet Bari, Prasad
Deshmukh, Nilaj
Yadav, Pradeep
Karande, Prasad
Bari, Poonam
author_role author
author2 Deshmukh, Nilaj
Yadav, Pradeep
Karande, Prasad
Bari, Poonam
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Genetic Algorithm
Tardiness
Scheduling
Combinatorial Approach
NEH
topic Genetic Algorithm
Tardiness
Scheduling
Combinatorial Approach
NEH
description [EN] No-idle flow shop scheduling is a critical challenge in manufacturing, where minimising overall tardiness directly impacts efficiency and customer satisfaction. This study introduces a novel hybrid algorithm, EDD-NEH-GA (ENG), which integrates Earliest Due Date (EDD) and Nawaz-Enscore-Ham (NEH) heuristics with a Genetic Algorithm (GA) to balance global exploration and local optimisation. The objective is to overcome premature convergence and achieve superior tardiness reduction. Computational experiments on Taillard s benchmark instances demonstrate ENG s effectiveness compared to the Mixed Integer Linear Programming (MILP) based approach by Balogh. Across all tested cases, ENG updated 93% of previously best-known solutions, achieving an average tardiness reduction of 18 52%. These results confirm ENG as a robust and efficient solution for complex no-idle flow shop environments, offering significant gains in scheduling performance and operational productivity. After performing statistical analysis it is noted that ENG outperforms. ENG significantly improved scheduling performance, averaging 8430 units of reduction in overall tardiness. According to the standard error of the difference (SE = 1859), this improvement appears to be constant across cases.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-01-31
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/232249
url https://riunet.upv.es/handle/10251/232249
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de València
publisher.none.fl_str_mv Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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