Scheduling multi-component maintenance with a greedy heuristic local search algorithm
Abstract As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link b...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hosseini, Seyedmohsen [verfasserIn] Kalam, Sifat [verfasserIn] Barker, Kash [verfasserIn] Ramirez-Marquez, Jose E. [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 24(2019), 1 vom: 15. März, Seite 351-366 |
|---|---|
| Übergeordnetes Werk: |
volume:24 ; year:2019 ; number:1 ; day:15 ; month:03 ; pages:351-366 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-019-03914-7 |
|---|
| Katalog-ID: |
SPR006511880 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR006511880 | ||
| 003 | DE-627 | ||
| 005 | 20201124002925.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 201005s2019 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00500-019-03914-7 |2 doi | |
| 035 | |a (DE-627)SPR006511880 | ||
| 035 | |a (SPR)s00500-019-03914-7-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Hosseini, Seyedmohsen |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Scheduling multi-component maintenance with a greedy heuristic local search algorithm |
| 264 | 1 | |c 2019 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link between sustainment policies and acquisition, this work develops a greedy heuristic-based local search algorithm (GHLSA) to provide a system maintenance schedule for multi-component systems, coordinating recommended component maintenance times to reduce system downtime costs, thereby enabling effective acquisition. The proposed iterative algorithm aims to minimize the sum of downtime, earliness and tardiness costs of scheduling, which contains three phases: (1) the construction phase, which uses a heuristic to construct an initial partial solution, (2) an improvement phase, which aims to improve the partial solution generated in the construction phase, and finally, (3) a local search phase, which performs a local search technique to the partial solution found in the improvement phase. The proposed algorithm makes a trade-off between exploration and exploitation of solutions. The experimental results for small (10 jobs) and large size (50 jobs) problems indicate that GHLSA outperforms both genetic algorithm and simulated annealing approaches in terms of solution quality and is similar in terms of efficiency. | ||
| 650 | 4 | |a Maintenance scheduling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Downtime |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Repair |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Kalam, Sifat |e verfasserin |4 aut | |
| 700 | 1 | |a Barker, Kash |e verfasserin |4 aut | |
| 700 | 1 | |a Ramirez-Marquez, Jose E. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Soft Computing |d Springer-Verlag, 2003 |g 24(2019), 1 vom: 15. März, Seite 351-366 |w (DE-627)SPR006469531 |7 nnns |
| 773 | 1 | 8 | |g volume:24 |g year:2019 |g number:1 |g day:15 |g month:03 |g pages:351-366 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s00500-019-03914-7 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 951 | |a AR | ||
| 952 | |d 24 |j 2019 |e 1 |b 15 |c 03 |h 351-366 | ||
| author_variant |
s h sh s k sk k b kb j e r m jer jerm |
|---|---|
| matchkey_str |
hosseiniseyedmohsenkalamsifatbarkerkashr:2019----:ceuigutcmoetaneacwtareyersi |
| hierarchy_sort_str |
2019 |
| publishDate |
2019 |
| allfields |
10.1007/s00500-019-03914-7 doi (DE-627)SPR006511880 (SPR)s00500-019-03914-7-e DE-627 ger DE-627 rakwb eng Hosseini, Seyedmohsen verfasserin aut Scheduling multi-component maintenance with a greedy heuristic local search algorithm 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link between sustainment policies and acquisition, this work develops a greedy heuristic-based local search algorithm (GHLSA) to provide a system maintenance schedule for multi-component systems, coordinating recommended component maintenance times to reduce system downtime costs, thereby enabling effective acquisition. The proposed iterative algorithm aims to minimize the sum of downtime, earliness and tardiness costs of scheduling, which contains three phases: (1) the construction phase, which uses a heuristic to construct an initial partial solution, (2) an improvement phase, which aims to improve the partial solution generated in the construction phase, and finally, (3) a local search phase, which performs a local search technique to the partial solution found in the improvement phase. The proposed algorithm makes a trade-off between exploration and exploitation of solutions. The experimental results for small (10 jobs) and large size (50 jobs) problems indicate that GHLSA outperforms both genetic algorithm and simulated annealing approaches in terms of solution quality and is similar in terms of efficiency. Maintenance scheduling (dpeaa)DE-He213 Downtime (dpeaa)DE-He213 Repair (dpeaa)DE-He213 Kalam, Sifat verfasserin aut Barker, Kash verfasserin aut Ramirez-Marquez, Jose E. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 1 vom: 15. März, Seite 351-366 (DE-627)SPR006469531 nnns volume:24 year:2019 number:1 day:15 month:03 pages:351-366 https://dx.doi.org/10.1007/s00500-019-03914-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 1 15 03 351-366 |
| spelling |
10.1007/s00500-019-03914-7 doi (DE-627)SPR006511880 (SPR)s00500-019-03914-7-e DE-627 ger DE-627 rakwb eng Hosseini, Seyedmohsen verfasserin aut Scheduling multi-component maintenance with a greedy heuristic local search algorithm 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link between sustainment policies and acquisition, this work develops a greedy heuristic-based local search algorithm (GHLSA) to provide a system maintenance schedule for multi-component systems, coordinating recommended component maintenance times to reduce system downtime costs, thereby enabling effective acquisition. The proposed iterative algorithm aims to minimize the sum of downtime, earliness and tardiness costs of scheduling, which contains three phases: (1) the construction phase, which uses a heuristic to construct an initial partial solution, (2) an improvement phase, which aims to improve the partial solution generated in the construction phase, and finally, (3) a local search phase, which performs a local search technique to the partial solution found in the improvement phase. The proposed algorithm makes a trade-off between exploration and exploitation of solutions. The experimental results for small (10 jobs) and large size (50 jobs) problems indicate that GHLSA outperforms both genetic algorithm and simulated annealing approaches in terms of solution quality and is similar in terms of efficiency. Maintenance scheduling (dpeaa)DE-He213 Downtime (dpeaa)DE-He213 Repair (dpeaa)DE-He213 Kalam, Sifat verfasserin aut Barker, Kash verfasserin aut Ramirez-Marquez, Jose E. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 1 vom: 15. März, Seite 351-366 (DE-627)SPR006469531 nnns volume:24 year:2019 number:1 day:15 month:03 pages:351-366 https://dx.doi.org/10.1007/s00500-019-03914-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 1 15 03 351-366 |
| allfields_unstemmed |
10.1007/s00500-019-03914-7 doi (DE-627)SPR006511880 (SPR)s00500-019-03914-7-e DE-627 ger DE-627 rakwb eng Hosseini, Seyedmohsen verfasserin aut Scheduling multi-component maintenance with a greedy heuristic local search algorithm 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link between sustainment policies and acquisition, this work develops a greedy heuristic-based local search algorithm (GHLSA) to provide a system maintenance schedule for multi-component systems, coordinating recommended component maintenance times to reduce system downtime costs, thereby enabling effective acquisition. The proposed iterative algorithm aims to minimize the sum of downtime, earliness and tardiness costs of scheduling, which contains three phases: (1) the construction phase, which uses a heuristic to construct an initial partial solution, (2) an improvement phase, which aims to improve the partial solution generated in the construction phase, and finally, (3) a local search phase, which performs a local search technique to the partial solution found in the improvement phase. The proposed algorithm makes a trade-off between exploration and exploitation of solutions. The experimental results for small (10 jobs) and large size (50 jobs) problems indicate that GHLSA outperforms both genetic algorithm and simulated annealing approaches in terms of solution quality and is similar in terms of efficiency. Maintenance scheduling (dpeaa)DE-He213 Downtime (dpeaa)DE-He213 Repair (dpeaa)DE-He213 Kalam, Sifat verfasserin aut Barker, Kash verfasserin aut Ramirez-Marquez, Jose E. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 1 vom: 15. März, Seite 351-366 (DE-627)SPR006469531 nnns volume:24 year:2019 number:1 day:15 month:03 pages:351-366 https://dx.doi.org/10.1007/s00500-019-03914-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 1 15 03 351-366 |
| allfieldsGer |
10.1007/s00500-019-03914-7 doi (DE-627)SPR006511880 (SPR)s00500-019-03914-7-e DE-627 ger DE-627 rakwb eng Hosseini, Seyedmohsen verfasserin aut Scheduling multi-component maintenance with a greedy heuristic local search algorithm 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link between sustainment policies and acquisition, this work develops a greedy heuristic-based local search algorithm (GHLSA) to provide a system maintenance schedule for multi-component systems, coordinating recommended component maintenance times to reduce system downtime costs, thereby enabling effective acquisition. The proposed iterative algorithm aims to minimize the sum of downtime, earliness and tardiness costs of scheduling, which contains three phases: (1) the construction phase, which uses a heuristic to construct an initial partial solution, (2) an improvement phase, which aims to improve the partial solution generated in the construction phase, and finally, (3) a local search phase, which performs a local search technique to the partial solution found in the improvement phase. The proposed algorithm makes a trade-off between exploration and exploitation of solutions. The experimental results for small (10 jobs) and large size (50 jobs) problems indicate that GHLSA outperforms both genetic algorithm and simulated annealing approaches in terms of solution quality and is similar in terms of efficiency. Maintenance scheduling (dpeaa)DE-He213 Downtime (dpeaa)DE-He213 Repair (dpeaa)DE-He213 Kalam, Sifat verfasserin aut Barker, Kash verfasserin aut Ramirez-Marquez, Jose E. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 1 vom: 15. März, Seite 351-366 (DE-627)SPR006469531 nnns volume:24 year:2019 number:1 day:15 month:03 pages:351-366 https://dx.doi.org/10.1007/s00500-019-03914-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 1 15 03 351-366 |
| allfieldsSound |
10.1007/s00500-019-03914-7 doi (DE-627)SPR006511880 (SPR)s00500-019-03914-7-e DE-627 ger DE-627 rakwb eng Hosseini, Seyedmohsen verfasserin aut Scheduling multi-component maintenance with a greedy heuristic local search algorithm 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link between sustainment policies and acquisition, this work develops a greedy heuristic-based local search algorithm (GHLSA) to provide a system maintenance schedule for multi-component systems, coordinating recommended component maintenance times to reduce system downtime costs, thereby enabling effective acquisition. The proposed iterative algorithm aims to minimize the sum of downtime, earliness and tardiness costs of scheduling, which contains three phases: (1) the construction phase, which uses a heuristic to construct an initial partial solution, (2) an improvement phase, which aims to improve the partial solution generated in the construction phase, and finally, (3) a local search phase, which performs a local search technique to the partial solution found in the improvement phase. The proposed algorithm makes a trade-off between exploration and exploitation of solutions. The experimental results for small (10 jobs) and large size (50 jobs) problems indicate that GHLSA outperforms both genetic algorithm and simulated annealing approaches in terms of solution quality and is similar in terms of efficiency. Maintenance scheduling (dpeaa)DE-He213 Downtime (dpeaa)DE-He213 Repair (dpeaa)DE-He213 Kalam, Sifat verfasserin aut Barker, Kash verfasserin aut Ramirez-Marquez, Jose E. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 1 vom: 15. März, Seite 351-366 (DE-627)SPR006469531 nnns volume:24 year:2019 number:1 day:15 month:03 pages:351-366 https://dx.doi.org/10.1007/s00500-019-03914-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 1 15 03 351-366 |
| language |
English |
| source |
Enthalten in Soft Computing 24(2019), 1 vom: 15. März, Seite 351-366 volume:24 year:2019 number:1 day:15 month:03 pages:351-366 |
| sourceStr |
Enthalten in Soft Computing 24(2019), 1 vom: 15. März, Seite 351-366 volume:24 year:2019 number:1 day:15 month:03 pages:351-366 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Maintenance scheduling Downtime Repair |
| isfreeaccess_bool |
false |
| container_title |
Soft Computing |
| authorswithroles_txt_mv |
Hosseini, Seyedmohsen @@aut@@ Kalam, Sifat @@aut@@ Barker, Kash @@aut@@ Ramirez-Marquez, Jose E. @@aut@@ |
| publishDateDaySort_date |
2019-03-15T00:00:00Z |
| hierarchy_top_id |
SPR006469531 |
| id |
SPR006511880 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006511880</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002925.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-019-03914-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006511880</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-019-03914-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hosseini, Seyedmohsen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Scheduling multi-component maintenance with a greedy heuristic local search algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link between sustainment policies and acquisition, this work develops a greedy heuristic-based local search algorithm (GHLSA) to provide a system maintenance schedule for multi-component systems, coordinating recommended component maintenance times to reduce system downtime costs, thereby enabling effective acquisition. The proposed iterative algorithm aims to minimize the sum of downtime, earliness and tardiness costs of scheduling, which contains three phases: (1) the construction phase, which uses a heuristic to construct an initial partial solution, (2) an improvement phase, which aims to improve the partial solution generated in the construction phase, and finally, (3) a local search phase, which performs a local search technique to the partial solution found in the improvement phase. The proposed algorithm makes a trade-off between exploration and exploitation of solutions. The experimental results for small (10 jobs) and large size (50 jobs) problems indicate that GHLSA outperforms both genetic algorithm and simulated annealing approaches in terms of solution quality and is similar in terms of efficiency.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Maintenance scheduling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Downtime</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Repair</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kalam, Sifat</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Barker, Kash</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ramirez-Marquez, Jose E.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">24(2019), 1 vom: 15. März, Seite 351-366</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:24</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:1</subfield><subfield code="g">day:15</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:351-366</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-019-03914-7</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">24</subfield><subfield code="j">2019</subfield><subfield code="e">1</subfield><subfield code="b">15</subfield><subfield code="c">03</subfield><subfield code="h">351-366</subfield></datafield></record></collection>
|
| author |
Hosseini, Seyedmohsen |
| spellingShingle |
Hosseini, Seyedmohsen misc Maintenance scheduling misc Downtime misc Repair Scheduling multi-component maintenance with a greedy heuristic local search algorithm |
| authorStr |
Hosseini, Seyedmohsen |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)SPR006469531 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
Scheduling multi-component maintenance with a greedy heuristic local search algorithm Maintenance scheduling (dpeaa)DE-He213 Downtime (dpeaa)DE-He213 Repair (dpeaa)DE-He213 |
| topic |
misc Maintenance scheduling misc Downtime misc Repair |
| topic_unstemmed |
misc Maintenance scheduling misc Downtime misc Repair |
| topic_browse |
misc Maintenance scheduling misc Downtime misc Repair |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Soft Computing |
| hierarchy_parent_id |
SPR006469531 |
| hierarchy_top_title |
Soft Computing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)SPR006469531 |
| title |
Scheduling multi-component maintenance with a greedy heuristic local search algorithm |
| ctrlnum |
(DE-627)SPR006511880 (SPR)s00500-019-03914-7-e |
| title_full |
Scheduling multi-component maintenance with a greedy heuristic local search algorithm |
| author_sort |
Hosseini, Seyedmohsen |
| journal |
Soft Computing |
| journalStr |
Soft Computing |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2019 |
| contenttype_str_mv |
txt |
| container_start_page |
351 |
| author_browse |
Hosseini, Seyedmohsen Kalam, Sifat Barker, Kash Ramirez-Marquez, Jose E. |
| container_volume |
24 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Hosseini, Seyedmohsen |
| doi_str_mv |
10.1007/s00500-019-03914-7 |
| author2-role |
verfasserin |
| title_sort |
scheduling multi-component maintenance with a greedy heuristic local search algorithm |
| title_auth |
Scheduling multi-component maintenance with a greedy heuristic local search algorithm |
| abstract |
Abstract As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link between sustainment policies and acquisition, this work develops a greedy heuristic-based local search algorithm (GHLSA) to provide a system maintenance schedule for multi-component systems, coordinating recommended component maintenance times to reduce system downtime costs, thereby enabling effective acquisition. The proposed iterative algorithm aims to minimize the sum of downtime, earliness and tardiness costs of scheduling, which contains three phases: (1) the construction phase, which uses a heuristic to construct an initial partial solution, (2) an improvement phase, which aims to improve the partial solution generated in the construction phase, and finally, (3) a local search phase, which performs a local search technique to the partial solution found in the improvement phase. The proposed algorithm makes a trade-off between exploration and exploitation of solutions. The experimental results for small (10 jobs) and large size (50 jobs) problems indicate that GHLSA outperforms both genetic algorithm and simulated annealing approaches in terms of solution quality and is similar in terms of efficiency. |
| abstractGer |
Abstract As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link between sustainment policies and acquisition, this work develops a greedy heuristic-based local search algorithm (GHLSA) to provide a system maintenance schedule for multi-component systems, coordinating recommended component maintenance times to reduce system downtime costs, thereby enabling effective acquisition. The proposed iterative algorithm aims to minimize the sum of downtime, earliness and tardiness costs of scheduling, which contains three phases: (1) the construction phase, which uses a heuristic to construct an initial partial solution, (2) an improvement phase, which aims to improve the partial solution generated in the construction phase, and finally, (3) a local search phase, which performs a local search technique to the partial solution found in the improvement phase. The proposed algorithm makes a trade-off between exploration and exploitation of solutions. The experimental results for small (10 jobs) and large size (50 jobs) problems indicate that GHLSA outperforms both genetic algorithm and simulated annealing approaches in terms of solution quality and is similar in terms of efficiency. |
| abstract_unstemmed |
Abstract As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link between sustainment policies and acquisition, this work develops a greedy heuristic-based local search algorithm (GHLSA) to provide a system maintenance schedule for multi-component systems, coordinating recommended component maintenance times to reduce system downtime costs, thereby enabling effective acquisition. The proposed iterative algorithm aims to minimize the sum of downtime, earliness and tardiness costs of scheduling, which contains three phases: (1) the construction phase, which uses a heuristic to construct an initial partial solution, (2) an improvement phase, which aims to improve the partial solution generated in the construction phase, and finally, (3) a local search phase, which performs a local search technique to the partial solution found in the improvement phase. The proposed algorithm makes a trade-off between exploration and exploitation of solutions. The experimental results for small (10 jobs) and large size (50 jobs) problems indicate that GHLSA outperforms both genetic algorithm and simulated annealing approaches in terms of solution quality and is similar in terms of efficiency. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER |
| container_issue |
1 |
| title_short |
Scheduling multi-component maintenance with a greedy heuristic local search algorithm |
| url |
https://dx.doi.org/10.1007/s00500-019-03914-7 |
| remote_bool |
true |
| author2 |
Kalam, Sifat Barker, Kash Ramirez-Marquez, Jose E. |
| author2Str |
Kalam, Sifat Barker, Kash Ramirez-Marquez, Jose E. |
| ppnlink |
SPR006469531 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00500-019-03914-7 |
| up_date |
2024-07-03T23:20:22.007Z |
| _version_ |
1803601913585860608 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006511880</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002925.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-019-03914-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006511880</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-019-03914-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hosseini, Seyedmohsen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Scheduling multi-component maintenance with a greedy heuristic local search algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link between sustainment policies and acquisition, this work develops a greedy heuristic-based local search algorithm (GHLSA) to provide a system maintenance schedule for multi-component systems, coordinating recommended component maintenance times to reduce system downtime costs, thereby enabling effective acquisition. The proposed iterative algorithm aims to minimize the sum of downtime, earliness and tardiness costs of scheduling, which contains three phases: (1) the construction phase, which uses a heuristic to construct an initial partial solution, (2) an improvement phase, which aims to improve the partial solution generated in the construction phase, and finally, (3) a local search phase, which performs a local search technique to the partial solution found in the improvement phase. The proposed algorithm makes a trade-off between exploration and exploitation of solutions. The experimental results for small (10 jobs) and large size (50 jobs) problems indicate that GHLSA outperforms both genetic algorithm and simulated annealing approaches in terms of solution quality and is similar in terms of efficiency.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Maintenance scheduling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Downtime</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Repair</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kalam, Sifat</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Barker, Kash</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ramirez-Marquez, Jose E.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">24(2019), 1 vom: 15. März, Seite 351-366</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:24</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:1</subfield><subfield code="g">day:15</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:351-366</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-019-03914-7</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">24</subfield><subfield code="j">2019</subfield><subfield code="e">1</subfield><subfield code="b">15</subfield><subfield code="c">03</subfield><subfield code="h">351-366</subfield></datafield></record></collection>
|
| score |
7.401184 |