Loading…

A genetic algorithm and B&B algorithm for integrated production scheduling, preventiveand corrective maintenance to save energy

The rapid global economic development of the world economy depends on the availability of substantial energy and resources, which is why in recent years a large share of non-renewable energy resources has attracted interest in energy control. In addition, inappropriate use of energy resources raises...

Full description

Saved in:
Bibliographic Details
Published in:Management and Production Engineering Review 2020-10, Vol.11 (4)
Main Authors: Assia, Sadiqi, Ikram, El Abbassi, Abdellah, El Barkany, Moumen, Darcherif, Ahmed, El Biyaali
Format: Article
Language:English
Subjects:
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c1198-ef354c2cc0d0c4e3182432aacbcc40ac0b4a3681259330f604ae663d6545cee93
cites
container_end_page
container_issue 4
container_start_page
container_title Management and Production Engineering Review
container_volume 11
creator Assia, Sadiqi
Ikram, El Abbassi
Abdellah, El Barkany
Moumen, Darcherif
Ahmed, El Biyaali
description The rapid global economic development of the world economy depends on the availability of substantial energy and resources, which is why in recent years a large share of non-renewable energy resources has attracted interest in energy control. In addition, inappropriate use of energy resources raises the serious problem of inadequate emissions of greenhouse effect gases, with major impact on the environment and climate. On the other hand, it is important to ensure efficient energy consumption in order to stimulate economic development and preserve the environment. As scheduling conflicts in the different workshops are closely associated with energy consumption. However, we find in the literature only a brief work strictly focused on two directions of research: the scheduling with PM and the scheduling with energy. Moreover, our objective is to combine both aspects and directions of in-depth research in a single machine. In this context, this article addresses the problem of integrated scheduling of production, preventive maintenance (PM) and corrective maintenance (CM) jobs in a single machine. The objective of this article is to minimize total energy consumption under the constraints of system robustness and stability. A common model for the integration of preventive maintenance (PM) in production scheduling is proposed, where the sequence of production tasks, as well as the preventive maintenance (PM) periods and the expected times for completion of the tasks are established simultaneously; this makes the theory put into practice more efficient. On the basis of the exact Branch and Bound method integrated on the CPLEX solver and the genetic algorithm (GA) solved in the Python software, the performance of the proposed integer binary mixed programming model is tested and evaluated. Indeed, after numerically experimenting with various parameters of the problem, the B&B algorithm works relatively satisfactorily and provides accurate results compared to the GA algorithm. A comparative study of the results proved that the model developed was sufficiently efficient.
doi_str_mv 10.24425/mper.2020.136128
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2651861051</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2651861051</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1198-ef354c2cc0d0c4e3182432aacbcc40ac0b4a3681259330f604ae663d6545cee93</originalsourceid><addsrcrecordid>eNpNjs1LAzEQxYMoWLR_gLeA4Mmtk0_SY1v8goIXPZd0dnYbaZOazRY8-a-7VQ9eZt784L03jF0JmEitpbnb7SlPJEiYCGWFdCdsJMHJSiitT380VG6Y52zcdWENQmqhNagR-5rxliKVgNxv25RD2ey4jzWf38z_kSZlHmKhNvtCNd_nVPdYQoq8ww3V_TbE9nbAdKBYwoGOCZhyJjxefOeP5ugjEi-Jd35gQ2tuPy_ZWeO3HY3_9gV7e7h_XTxVy5fH58VsWaEQU1dRo4xGiQg1oCYlnNRKeo9rRA0eYa29sk5IM1UKGgvak7WqtkYbJJqqC3b9mzu8_tFTV1bvqc9xqFxJa4SzAoxQ3_FmZIM</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2651861051</pqid></control><display><type>article</type><title>A genetic algorithm and B&amp;B algorithm for integrated production scheduling, preventiveand corrective maintenance to save energy</title><source>Publicly Available Content (ProQuest)</source><creator>Assia, Sadiqi ; Ikram, El Abbassi ; Abdellah, El Barkany ; Moumen, Darcherif ; Ahmed, El Biyaali</creator><creatorcontrib>Assia, Sadiqi ; Ikram, El Abbassi ; Abdellah, El Barkany ; Moumen, Darcherif ; Ahmed, El Biyaali</creatorcontrib><description>The rapid global economic development of the world economy depends on the availability of substantial energy and resources, which is why in recent years a large share of non-renewable energy resources has attracted interest in energy control. In addition, inappropriate use of energy resources raises the serious problem of inadequate emissions of greenhouse effect gases, with major impact on the environment and climate. On the other hand, it is important to ensure efficient energy consumption in order to stimulate economic development and preserve the environment. As scheduling conflicts in the different workshops are closely associated with energy consumption. However, we find in the literature only a brief work strictly focused on two directions of research: the scheduling with PM and the scheduling with energy. Moreover, our objective is to combine both aspects and directions of in-depth research in a single machine. In this context, this article addresses the problem of integrated scheduling of production, preventive maintenance (PM) and corrective maintenance (CM) jobs in a single machine. The objective of this article is to minimize total energy consumption under the constraints of system robustness and stability. A common model for the integration of preventive maintenance (PM) in production scheduling is proposed, where the sequence of production tasks, as well as the preventive maintenance (PM) periods and the expected times for completion of the tasks are established simultaneously; this makes the theory put into practice more efficient. On the basis of the exact Branch and Bound method integrated on the CPLEX solver and the genetic algorithm (GA) solved in the Python software, the performance of the proposed integer binary mixed programming model is tested and evaluated. Indeed, after numerically experimenting with various parameters of the problem, the B&amp;B algorithm works relatively satisfactorily and provides accurate results compared to the GA algorithm. A comparative study of the results proved that the model developed was sufficiently efficient.</description><identifier>ISSN: 2080-8208</identifier><identifier>EISSN: 2082-1344</identifier><identifier>DOI: 10.24425/mper.2020.136128</identifier><language>eng</language><publisher>Warsaw: Polish Academy of Sciences</publisher><subject>Branch and bound methods ; Comparative studies ; Economic development ; Economics ; Energy consumption ; Energy resources ; Energy sources ; Environmental impact ; Genetic algorithms ; Global economy ; Greenhouse effect ; Greenhouse gases ; Preventive maintenance ; Production scheduling ; Robustness (mathematics) ; Scheduling ; Task scheduling</subject><ispartof>Management and Production Engineering Review, 2020-10, Vol.11 (4)</ispartof><rights>2020. This work is licensed under https://creativecommons.org/licenses/by-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1198-ef354c2cc0d0c4e3182432aacbcc40ac0b4a3681259330f604ae663d6545cee93</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2651861051?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590</link.rule.ids></links><search><creatorcontrib>Assia, Sadiqi</creatorcontrib><creatorcontrib>Ikram, El Abbassi</creatorcontrib><creatorcontrib>Abdellah, El Barkany</creatorcontrib><creatorcontrib>Moumen, Darcherif</creatorcontrib><creatorcontrib>Ahmed, El Biyaali</creatorcontrib><title>A genetic algorithm and B&amp;B algorithm for integrated production scheduling, preventiveand corrective maintenance to save energy</title><title>Management and Production Engineering Review</title><description>The rapid global economic development of the world economy depends on the availability of substantial energy and resources, which is why in recent years a large share of non-renewable energy resources has attracted interest in energy control. In addition, inappropriate use of energy resources raises the serious problem of inadequate emissions of greenhouse effect gases, with major impact on the environment and climate. On the other hand, it is important to ensure efficient energy consumption in order to stimulate economic development and preserve the environment. As scheduling conflicts in the different workshops are closely associated with energy consumption. However, we find in the literature only a brief work strictly focused on two directions of research: the scheduling with PM and the scheduling with energy. Moreover, our objective is to combine both aspects and directions of in-depth research in a single machine. In this context, this article addresses the problem of integrated scheduling of production, preventive maintenance (PM) and corrective maintenance (CM) jobs in a single machine. The objective of this article is to minimize total energy consumption under the constraints of system robustness and stability. A common model for the integration of preventive maintenance (PM) in production scheduling is proposed, where the sequence of production tasks, as well as the preventive maintenance (PM) periods and the expected times for completion of the tasks are established simultaneously; this makes the theory put into practice more efficient. On the basis of the exact Branch and Bound method integrated on the CPLEX solver and the genetic algorithm (GA) solved in the Python software, the performance of the proposed integer binary mixed programming model is tested and evaluated. Indeed, after numerically experimenting with various parameters of the problem, the B&amp;B algorithm works relatively satisfactorily and provides accurate results compared to the GA algorithm. A comparative study of the results proved that the model developed was sufficiently efficient.</description><subject>Branch and bound methods</subject><subject>Comparative studies</subject><subject>Economic development</subject><subject>Economics</subject><subject>Energy consumption</subject><subject>Energy resources</subject><subject>Energy sources</subject><subject>Environmental impact</subject><subject>Genetic algorithms</subject><subject>Global economy</subject><subject>Greenhouse effect</subject><subject>Greenhouse gases</subject><subject>Preventive maintenance</subject><subject>Production scheduling</subject><subject>Robustness (mathematics)</subject><subject>Scheduling</subject><subject>Task scheduling</subject><issn>2080-8208</issn><issn>2082-1344</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNpNjs1LAzEQxYMoWLR_gLeA4Mmtk0_SY1v8goIXPZd0dnYbaZOazRY8-a-7VQ9eZt784L03jF0JmEitpbnb7SlPJEiYCGWFdCdsJMHJSiitT380VG6Y52zcdWENQmqhNagR-5rxliKVgNxv25RD2ey4jzWf38z_kSZlHmKhNvtCNd_nVPdYQoq8ww3V_TbE9nbAdKBYwoGOCZhyJjxefOeP5ugjEi-Jd35gQ2tuPy_ZWeO3HY3_9gV7e7h_XTxVy5fH58VsWaEQU1dRo4xGiQg1oCYlnNRKeo9rRA0eYa29sk5IM1UKGgvak7WqtkYbJJqqC3b9mzu8_tFTV1bvqc9xqFxJa4SzAoxQ3_FmZIM</recordid><startdate>20201001</startdate><enddate>20201001</enddate><creator>Assia, Sadiqi</creator><creator>Ikram, El Abbassi</creator><creator>Abdellah, El Barkany</creator><creator>Moumen, Darcherif</creator><creator>Ahmed, El Biyaali</creator><general>Polish Academy of Sciences</general><scope>7TA</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20201001</creationdate><title>A genetic algorithm and B&amp;B algorithm for integrated production scheduling, preventiveand corrective maintenance to save energy</title><author>Assia, Sadiqi ; Ikram, El Abbassi ; Abdellah, El Barkany ; Moumen, Darcherif ; Ahmed, El Biyaali</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1198-ef354c2cc0d0c4e3182432aacbcc40ac0b4a3681259330f604ae663d6545cee93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Branch and bound methods</topic><topic>Comparative studies</topic><topic>Economic development</topic><topic>Economics</topic><topic>Energy consumption</topic><topic>Energy resources</topic><topic>Energy sources</topic><topic>Environmental impact</topic><topic>Genetic algorithms</topic><topic>Global economy</topic><topic>Greenhouse effect</topic><topic>Greenhouse gases</topic><topic>Preventive maintenance</topic><topic>Production scheduling</topic><topic>Robustness (mathematics)</topic><topic>Scheduling</topic><topic>Task scheduling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Assia, Sadiqi</creatorcontrib><creatorcontrib>Ikram, El Abbassi</creatorcontrib><creatorcontrib>Abdellah, El Barkany</creatorcontrib><creatorcontrib>Moumen, Darcherif</creatorcontrib><creatorcontrib>Ahmed, El Biyaali</creatorcontrib><collection>Materials Business File</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Management and Production Engineering Review</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Assia, Sadiqi</au><au>Ikram, El Abbassi</au><au>Abdellah, El Barkany</au><au>Moumen, Darcherif</au><au>Ahmed, El Biyaali</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A genetic algorithm and B&amp;B algorithm for integrated production scheduling, preventiveand corrective maintenance to save energy</atitle><jtitle>Management and Production Engineering Review</jtitle><date>2020-10-01</date><risdate>2020</risdate><volume>11</volume><issue>4</issue><issn>2080-8208</issn><eissn>2082-1344</eissn><abstract>The rapid global economic development of the world economy depends on the availability of substantial energy and resources, which is why in recent years a large share of non-renewable energy resources has attracted interest in energy control. In addition, inappropriate use of energy resources raises the serious problem of inadequate emissions of greenhouse effect gases, with major impact on the environment and climate. On the other hand, it is important to ensure efficient energy consumption in order to stimulate economic development and preserve the environment. As scheduling conflicts in the different workshops are closely associated with energy consumption. However, we find in the literature only a brief work strictly focused on two directions of research: the scheduling with PM and the scheduling with energy. Moreover, our objective is to combine both aspects and directions of in-depth research in a single machine. In this context, this article addresses the problem of integrated scheduling of production, preventive maintenance (PM) and corrective maintenance (CM) jobs in a single machine. The objective of this article is to minimize total energy consumption under the constraints of system robustness and stability. A common model for the integration of preventive maintenance (PM) in production scheduling is proposed, where the sequence of production tasks, as well as the preventive maintenance (PM) periods and the expected times for completion of the tasks are established simultaneously; this makes the theory put into practice more efficient. On the basis of the exact Branch and Bound method integrated on the CPLEX solver and the genetic algorithm (GA) solved in the Python software, the performance of the proposed integer binary mixed programming model is tested and evaluated. Indeed, after numerically experimenting with various parameters of the problem, the B&amp;B algorithm works relatively satisfactorily and provides accurate results compared to the GA algorithm. A comparative study of the results proved that the model developed was sufficiently efficient.</abstract><cop>Warsaw</cop><pub>Polish Academy of Sciences</pub><doi>10.24425/mper.2020.136128</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2080-8208
ispartof Management and Production Engineering Review, 2020-10, Vol.11 (4)
issn 2080-8208
2082-1344
language eng
recordid cdi_proquest_journals_2651861051
source Publicly Available Content (ProQuest)
subjects Branch and bound methods
Comparative studies
Economic development
Economics
Energy consumption
Energy resources
Energy sources
Environmental impact
Genetic algorithms
Global economy
Greenhouse effect
Greenhouse gases
Preventive maintenance
Production scheduling
Robustness (mathematics)
Scheduling
Task scheduling
title A genetic algorithm and B&B algorithm for integrated production scheduling, preventiveand corrective maintenance to save energy
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T08%3A09%3A43IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20genetic%20algorithm%20and%20B&B%20algorithm%20for%20integrated%20production%20scheduling,%20preventiveand%20corrective%20maintenance%20to%20save%20energy&rft.jtitle=Management%20and%20Production%20Engineering%20Review&rft.au=Assia,%20Sadiqi&rft.date=2020-10-01&rft.volume=11&rft.issue=4&rft.issn=2080-8208&rft.eissn=2082-1344&rft_id=info:doi/10.24425/mper.2020.136128&rft_dat=%3Cproquest%3E2651861051%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c1198-ef354c2cc0d0c4e3182432aacbcc40ac0b4a3681259330f604ae663d6545cee93%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2651861051&rft_id=info:pmid/&rfr_iscdi=true