The iterative process of genetic algorithm in the upper-level model.
<p>The iterative process of genetic algorithm in the upper-level model.</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
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| _version_ | 1852019212348817408 |
|---|---|
| author | Zhipeng Huang (1759759) |
| author2 | Limin Yang (391341) Jinlian Li (343111) Tao Zhang (43681) Zixian Qu (21568078) Yusen Miao (21568081) |
| author2_role | author author author author author |
| author_facet | Zhipeng Huang (1759759) Limin Yang (391341) Jinlian Li (343111) Tao Zhang (43681) Zixian Qu (21568078) Yusen Miao (21568081) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zhipeng Huang (1759759) Limin Yang (391341) Jinlian Li (343111) Tao Zhang (43681) Zixian Qu (21568078) Yusen Miao (21568081) |
| dc.date.none.fl_str_mv | 2025-06-18T17:50:07Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0326170.g007 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_iterative_process_of_genetic_algorithm_in_the_upper-level_model_/29359541 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Sociology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified xi &# 8217 speed railway timetables speed railway scheduling speed railway corridor speed rail operators speed rail corridor others remain difficult low occupancy rates genetic algorithm combined defined operational cycle various network arcs three key attributes resulting timetable balances xlink "> high level programming model establishing departure times analyzing passenger preferences uniform departure intervals passengers </ p departure times train timetable state three passenger demand integrates preferences dimensional network departure time wolfe method typically based travel demand seat preference seat classes scientific rigor nested frank impedance functions fare structures fare cost diverse demands consistent across case study approach enhances |
| dc.title.none.fl_str_mv | The iterative process of genetic algorithm in the upper-level model. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The iterative process of genetic algorithm in the upper-level model.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_876e3380703bb013dffaea2fd76f3c60 |
| identifier_str_mv | 10.1371/journal.pone.0326170.g007 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29359541 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The iterative process of genetic algorithm in the upper-level model.Zhipeng Huang (1759759)Limin Yang (391341)Jinlian Li (343111)Tao Zhang (43681)Zixian Qu (21568078)Yusen Miao (21568081)SociologyScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedxi &# 8217speed railway timetablesspeed railway schedulingspeed railway corridorspeed rail operatorsspeed rail corridorothers remain difficultlow occupancy ratesgenetic algorithm combineddefined operational cyclevarious network arcsthree key attributesresulting timetable balancesxlink "> highlevel programming modelestablishing departure timesanalyzing passenger preferencesuniform departure intervalspassengers </ pdeparture timestrain timetablestate threepassenger demandintegrates preferencesdimensional networkdeparture timewolfe methodtypically basedtravel demandseat preferenceseat classesscientific rigornested frankimpedance functionsfare structuresfare costdiverse demandsconsistent acrosscase studyapproach enhances<p>The iterative process of genetic algorithm in the upper-level model.</p>2025-06-18T17:50:07ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0326170.g007https://figshare.com/articles/figure/The_iterative_process_of_genetic_algorithm_in_the_upper-level_model_/29359541CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/293595412025-06-18T17:50:07Z |
| spellingShingle | The iterative process of genetic algorithm in the upper-level model. Zhipeng Huang (1759759) Sociology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified xi &# 8217 speed railway timetables speed railway scheduling speed railway corridor speed rail operators speed rail corridor others remain difficult low occupancy rates genetic algorithm combined defined operational cycle various network arcs three key attributes resulting timetable balances xlink "> high level programming model establishing departure times analyzing passenger preferences uniform departure intervals passengers </ p departure times train timetable state three passenger demand integrates preferences dimensional network departure time wolfe method typically based travel demand seat preference seat classes scientific rigor nested frank impedance functions fare structures fare cost diverse demands consistent across case study approach enhances |
| status_str | publishedVersion |
| title | The iterative process of genetic algorithm in the upper-level model. |
| title_full | The iterative process of genetic algorithm in the upper-level model. |
| title_fullStr | The iterative process of genetic algorithm in the upper-level model. |
| title_full_unstemmed | The iterative process of genetic algorithm in the upper-level model. |
| title_short | The iterative process of genetic algorithm in the upper-level model. |
| title_sort | The iterative process of genetic algorithm in the upper-level model. |
| topic | Sociology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified xi &# 8217 speed railway timetables speed railway scheduling speed railway corridor speed rail operators speed rail corridor others remain difficult low occupancy rates genetic algorithm combined defined operational cycle various network arcs three key attributes resulting timetable balances xlink "> high level programming model establishing departure times analyzing passenger preferences uniform departure intervals passengers </ p departure times train timetable state three passenger demand integrates preferences dimensional network departure time wolfe method typically based travel demand seat preference seat classes scientific rigor nested frank impedance functions fare structures fare cost diverse demands consistent across case study approach enhances |