Model for integrating the electricity cost consumption and power demand into aggregate production planning

Descripción del Articulo

The constant increases in electricity tax costs and the mandatory contracting of power demand in advance by companies connected to the high-voltage electrical system drive organizations to improve energy planning in their production processes. In addition, market uncertainties make only stochastic m...

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Detalles Bibliográficos
Autor: Lermen Henrique, Fernando
Formato: artículo
Fecha de Publicación:2022
Institución:Universidad Tecnológica del Perú
Repositorio:UTP-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.utp.edu.pe:20.500.12867/5996
Enlace del recurso:https://hdl.handle.net/20.500.12867/5996
https://doi.org/10.3390/app12157577
Nivel de acceso:acceso abierto
Materia:Production planning
Production control
Electric power
https://purl.org/pe-repo/ocde/ford#2.02.01
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dc.title.es_PE.fl_str_mv Model for integrating the electricity cost consumption and power demand into aggregate production planning
title Model for integrating the electricity cost consumption and power demand into aggregate production planning
spellingShingle Model for integrating the electricity cost consumption and power demand into aggregate production planning
Lermen Henrique, Fernando
Production planning
Production control
Electric power
https://purl.org/pe-repo/ocde/ford#2.02.01
title_short Model for integrating the electricity cost consumption and power demand into aggregate production planning
title_full Model for integrating the electricity cost consumption and power demand into aggregate production planning
title_fullStr Model for integrating the electricity cost consumption and power demand into aggregate production planning
title_full_unstemmed Model for integrating the electricity cost consumption and power demand into aggregate production planning
title_sort Model for integrating the electricity cost consumption and power demand into aggregate production planning
author Lermen Henrique, Fernando
author_facet Lermen Henrique, Fernando
author_role author
dc.contributor.author.fl_str_mv Lermen Henrique, Fernando
dc.subject.es_PE.fl_str_mv Production planning
Production control
Electric power
topic Production planning
Production control
Electric power
https://purl.org/pe-repo/ocde/ford#2.02.01
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.01
description The constant increases in electricity tax costs and the mandatory contracting of power demand in advance by companies connected to the high-voltage electrical system drive organizations to improve energy planning in their production processes. In addition, market uncertainties make only stochastic methods insufficient for forecasting future production demand. To fill this gap, this study proposes a model that integrates the cost with electricity consumption and power demand into the aggregate production planning, considering the market uncertainties. The model was empirically applied in the food industry, considering a family of potato chips products. From the collected data, a demand forecast was carried out for a later realization of the aggregate planning, using the Holt–Winters forecast model. Before modeling, the new energy demand was calculated, and finally, the model solution verification was performed. In the case study, after application, it was possible to reduce two workers and a cost reduction of R$ 14,288.00. Moreover, the proposal managed to define a power demand that minimized the costs of electric energy and the total costs of the aggregate production planning.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-09-28T14:46:42Z
dc.date.available.none.fl_str_mv 2022-09-28T14:46:42Z
dc.date.issued.fl_str_mv 2022
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
dc.type.version.es_PE.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.issn.none.fl_str_mv 2076-3417
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12867/5996
dc.identifier.journal.es_PE.fl_str_mv Applied Sciences
dc.identifier.doi.none.fl_str_mv https://doi.org/10.3390/app12157577
identifier_str_mv 2076-3417
Applied Sciences
url https://hdl.handle.net/20.500.12867/5996
https://doi.org/10.3390/app12157577
dc.language.iso.es_PE.fl_str_mv eng
language eng
dc.relation.ispartofseries.none.fl_str_mv Applied Sciences;vol. 12, n°5
dc.rights.es_PE.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
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dc.format.es_PE.fl_str_mv application/pdf
dc.publisher.es_PE.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.publisher.country.es_PE.fl_str_mv CH
dc.source.es_PE.fl_str_mv Repositorio Institucional - UTP
Universidad Tecnológica del Perú
dc.source.none.fl_str_mv reponame:UTP-Institucional
instname:Universidad Tecnológica del Perú
instacron:UTP
instname_str Universidad Tecnológica del Perú
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spelling Lermen Henrique, Fernando2022-09-28T14:46:42Z2022-09-28T14:46:42Z20222076-3417https://hdl.handle.net/20.500.12867/5996Applied Scienceshttps://doi.org/10.3390/app12157577The constant increases in electricity tax costs and the mandatory contracting of power demand in advance by companies connected to the high-voltage electrical system drive organizations to improve energy planning in their production processes. In addition, market uncertainties make only stochastic methods insufficient for forecasting future production demand. To fill this gap, this study proposes a model that integrates the cost with electricity consumption and power demand into the aggregate production planning, considering the market uncertainties. The model was empirically applied in the food industry, considering a family of potato chips products. From the collected data, a demand forecast was carried out for a later realization of the aggregate planning, using the Holt–Winters forecast model. Before modeling, the new energy demand was calculated, and finally, the model solution verification was performed. In the case study, after application, it was possible to reduce two workers and a cost reduction of R$ 14,288.00. 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