A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems:
Abstract: "The general job-shop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark job-shop scheduling problems, better than previous efforts using genetic algorithms for this task, and comparable...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Edinburgh
1993
|
| Series: | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper
623 |
| Subjects: | |
| Summary: | Abstract: "The general job-shop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark job-shop scheduling problems, better than previous efforts using genetic algorithms for this task, and comparable to existing conventional search-based methods. The representation used is a variant of one known to work moderately well for the traveling salesman problem. It has the considerable merit that crossover will always produce legal schedules. A novel method for performance enhancement is examined based on dynamic sampling of the convergence rates in different parts of the genome. Our approach also promises to effectively address the open-shop scheduling problem and the job-shop rescheduling problem." |
| Physical Description: | 9 S. |
Staff View
MARC
| LEADER | 00000nam a2200000 cb4500 | ||
|---|---|---|---|
| 001 | BV010463139 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | t | ||
| 008 | 951106s1993 |||| 00||| engod | ||
| 035 | |a (OCoLC)32307137 | ||
| 035 | |a (DE-599)BVBBV010463139 | ||
| 040 | |a DE-604 |b ger |e rakddb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-91G | ||
| 100 | 1 | |a Fang, Hsiao-Lan |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems |c Hsiao-Lan Fang, Peter Ross, and Dave Corne |
| 264 | 1 | |a Edinburgh |c 1993 | |
| 300 | |a 9 S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |v 623 | |
| 520 | 3 | |a Abstract: "The general job-shop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark job-shop scheduling problems, better than previous efforts using genetic algorithms for this task, and comparable to existing conventional search-based methods. The representation used is a variant of one known to work moderately well for the traveling salesman problem. It has the considerable merit that crossover will always produce legal schedules. A novel method for performance enhancement is examined based on dynamic sampling of the convergence rates in different parts of the genome. Our approach also promises to effectively address the open-shop scheduling problem and the job-shop rescheduling problem." | |
| 650 | 7 | |a Applied statistics, operational research |2 sigle | |
| 650 | 7 | |a Mathematics |2 sigle | |
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Genetic algorithms | |
| 700 | 1 | |a Ross, Peter |e Verfasser |4 aut | |
| 700 | 1 | |a Corne, Dave |e Verfasser |4 aut | |
| 810 | 2 | |a Department of Artificial Intelligence: DAI research paper |t University <Edinburgh> |v 623 |w (DE-604)BV010450646 |9 623 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-006971556 | ||
Record in the Search Index
| _version_ | 1804124895049678848 |
|---|---|
| any_adam_object | |
| author | Fang, Hsiao-Lan Ross, Peter Corne, Dave |
| author_facet | Fang, Hsiao-Lan Ross, Peter Corne, Dave |
| author_role | aut aut aut |
| author_sort | Fang, Hsiao-Lan |
| author_variant | h l f hlf p r pr d c dc |
| building | Verbundindex |
| bvnumber | BV010463139 |
| ctrlnum | (OCoLC)32307137 (DE-599)BVBBV010463139 |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02031nam a2200349 cb4500</leader><controlfield tag="001">BV010463139</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">951106s1993 |||| 00||| engod</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)32307137</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV010463139</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakddb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91G</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Fang, Hsiao-Lan</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems</subfield><subfield code="c">Hsiao-Lan Fang, Peter Ross, and Dave Corne</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Edinburgh</subfield><subfield code="c">1993</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">9 S.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">University <Edinburgh> / Department of Artificial Intelligence: DAI research paper</subfield><subfield code="v">623</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Abstract: "The general job-shop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark job-shop scheduling problems, better than previous efforts using genetic algorithms for this task, and comparable to existing conventional search-based methods. The representation used is a variant of one known to work moderately well for the traveling salesman problem. It has the considerable merit that crossover will always produce legal schedules. A novel method for performance enhancement is examined based on dynamic sampling of the convergence rates in different parts of the genome. Our approach also promises to effectively address the open-shop scheduling problem and the job-shop rescheduling problem."</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Applied statistics, operational research</subfield><subfield code="2">sigle</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Mathematics</subfield><subfield code="2">sigle</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematik</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Genetic algorithms</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ross, Peter</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Corne, Dave</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="810" ind1="2" ind2=" "><subfield code="a">Department of Artificial Intelligence: DAI research paper</subfield><subfield code="t">University <Edinburgh></subfield><subfield code="v">623</subfield><subfield code="w">(DE-604)BV010450646</subfield><subfield code="9">623</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-006971556</subfield></datafield></record></collection> |
| id | DE-604.BV010463139 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T17:52:55Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-006971556 |
| oclc_num | 32307137 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM |
| owner_facet | DE-91G DE-BY-TUM |
| physical | 9 S. |
| publishDate | 1993 |
| publishDateSearch | 1993 |
| publishDateSort | 1993 |
| record_format | marc |
| series2 | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |
| spelling | Fang, Hsiao-Lan Verfasser aut A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems Hsiao-Lan Fang, Peter Ross, and Dave Corne Edinburgh 1993 9 S. txt rdacontent n rdamedia nc rdacarrier University <Edinburgh> / Department of Artificial Intelligence: DAI research paper 623 Abstract: "The general job-shop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark job-shop scheduling problems, better than previous efforts using genetic algorithms for this task, and comparable to existing conventional search-based methods. The representation used is a variant of one known to work moderately well for the traveling salesman problem. It has the considerable merit that crossover will always produce legal schedules. A novel method for performance enhancement is examined based on dynamic sampling of the convergence rates in different parts of the genome. Our approach also promises to effectively address the open-shop scheduling problem and the job-shop rescheduling problem." Applied statistics, operational research sigle Mathematics sigle Mathematik Genetic algorithms Ross, Peter Verfasser aut Corne, Dave Verfasser aut Department of Artificial Intelligence: DAI research paper University <Edinburgh> 623 (DE-604)BV010450646 623 |
| spellingShingle | Fang, Hsiao-Lan Ross, Peter Corne, Dave A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems Applied statistics, operational research sigle Mathematics sigle Mathematik Genetic algorithms |
| title | A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems |
| title_auth | A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems |
| title_exact_search | A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems |
| title_full | A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems Hsiao-Lan Fang, Peter Ross, and Dave Corne |
| title_fullStr | A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems Hsiao-Lan Fang, Peter Ross, and Dave Corne |
| title_full_unstemmed | A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems Hsiao-Lan Fang, Peter Ross, and Dave Corne |
| title_short | A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems |
| title_sort | a promising genetic algorithm approach to job shop scheduling rescheduling and open shop scheduling problems |
| topic | Applied statistics, operational research sigle Mathematics sigle Mathematik Genetic algorithms |
| topic_facet | Applied statistics, operational research Mathematics Mathematik Genetic algorithms |
| volume_link | (DE-604)BV010450646 |
| work_keys_str_mv | AT fanghsiaolan apromisinggeneticalgorithmapproachtojobshopschedulingreschedulingandopenshopschedulingproblems AT rosspeter apromisinggeneticalgorithmapproachtojobshopschedulingreschedulingandopenshopschedulingproblems AT cornedave apromisinggeneticalgorithmapproachtojobshopschedulingreschedulingandopenshopschedulingproblems |