{"id":"https://openalex.org/W4223983589","doi":"https://doi.org/10.48550/arxiv.2204.03061","title":"Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem","display_name":"Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem","publication_year":2022,"publication_date":"2022-04-06","ids":{"openalex":"https://openalex.org/W4223983589","doi":"https://doi.org/10.48550/arxiv.2204.03061"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2204.03061","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.03061","pdf_url":"https://arxiv.org/pdf/2204.03061","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2204.03061","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008401832","display_name":"Anik\u00f3 Kopacz","orcid":"https://orcid.org/0000-0001-5300-9955"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kopacz, Anik\u00f3","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060326176","display_name":"\u00c1gnes Mester","orcid":"https://orcid.org/0000-0003-3097-7464"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mester, \u00c1gnes","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046642626","display_name":"S\u00e1ndor Kolumb\u00e1n","orcid":"https://orcid.org/0000-0001-9017-0801"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kolumb\u00e1n, S\u00e1ndor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5048206013","display_name":"Lehel Csat\u00f3","orcid":"https://orcid.org/0000-0003-1052-1849"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Csat\u00f3, Lehel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14011","display_name":"Elevator Systems and Control","score":0.9463000297546387,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14011","display_name":"Elevator Systems and Control","score":0.9463000297546387,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9434999823570251,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.8803313970565796},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7474893927574158},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7020472884178162},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.608758270740509},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5596179366111755},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5109264254570007},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5004096031188965},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.481403112411499},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4795927107334137},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45860183238983154},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.25687605142593384},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1426900029182434}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.8803313970565796},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7474893927574158},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7020472884178162},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.608758270740509},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5596179366111755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5109264254570007},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5004096031188965},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.481403112411499},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4795927107334137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45860183238983154},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.25687605142593384},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1426900029182434},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2204.03061","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.03061","pdf_url":"https://arxiv.org/pdf/2204.03061","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2204.03061","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2204.03061","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2204.03061","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.03061","pdf_url":"https://arxiv.org/pdf/2204.03061","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.46000000834465027}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322192","display_name":"Magyar Tudom\u00e1nyos Akad\u00e9mia","ror":"https://ror.org/02ks8qq67"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4223983589.pdf","grobid_xml":"https://content.openalex.org/works/W4223983589.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W618248309","https://openalex.org/W2377336366","https://openalex.org/W1568097102","https://openalex.org/W2487162673","https://openalex.org/W2793211469","https://openalex.org/W1601203902","https://openalex.org/W2075798043","https://openalex.org/W4390419160","https://openalex.org/W2949152769","https://openalex.org/W4225671779"],"abstract_inverted_index":{"We":[0,27,76],"propose":[1],"a":[2,8,46,103],"train":[3],"rescheduling":[4],"algorithm":[5],"which":[6,32,50,102],"applies":[7],"standardized":[9],"feature":[10,96],"selection":[11],"based":[12],"on":[13],"pairwise":[14],"conflicts":[15],"in":[16,68],"order":[17],"to":[18,66],"serve":[19],"as":[20],"input":[21],"for":[22],"the":[23,52,69,72,82,86,94],"reinforcement":[24,73],"learning":[25,74],"framework.":[26,75],"implement":[28],"an":[29],"analytical":[30],"method":[31],"identifies":[33],"and":[34],"optimally":[35],"solves":[36],"every":[37],"conflict":[38],"arising":[39],"between":[40],"two":[41],"trains,":[42],"then":[43,64],"we":[44],"design":[45],"corresponding":[47],"observation":[48],"space":[49,97],"features":[51],"most":[53],"relevant":[54],"information":[55],"considering":[56],"these":[57],"conflicts.":[58],"The":[59,89],"data":[60],"obtained":[61],"this":[62],"way":[63],"translates":[65],"actions":[67],"context":[70],"of":[71,85],"test":[77],"our":[78],"preliminary":[79],"model":[80],"using":[81],"evaluation":[83],"metrics":[84],"Flatland":[87],"Challenge.":[88],"empirical":[90],"results":[91],"indicate":[92],"that":[93],"suggested":[95],"provides":[98],"meaningful":[99],"observations,":[100],"from":[101],"sensible":[104],"scheduling":[105],"policy":[106],"can":[107],"be":[108],"learned.":[109]},"counts_by_year":[],"updated_date":"2026-08-04T08:18:43.703281","created_date":"2025-10-10T00:00:00"}