{"id":"https://openalex.org/W4309344368","doi":"https://doi.org/10.1109/smc53654.2022.9945420","title":"The Neural-Prediction based Acceleration Algorithm of Column Generation for Graph-Based Set Covering Problems","display_name":"The Neural-Prediction based Acceleration Algorithm of Column Generation for Graph-Based Set Covering Problems","publication_year":2022,"publication_date":"2022-10-09","ids":{"openalex":"https://openalex.org/W4309344368","doi":"https://doi.org/10.1109/smc53654.2022.9945420"},"language":"en","primary_location":{"id":"doi:10.1109/smc53654.2022.9945420","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc53654.2022.9945420","pdf_url":null,"source":{"id":"https://openalex.org/S4363607746","display_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101328590","display_name":"Haofeng Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haofeng Yuan","raw_affiliation_strings":["Tsinghua University,Department of Automation and BNRist,Beijing,China,100084"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation and BNRist,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001339397","display_name":"Peng Jiang","orcid":"https://orcid.org/0000-0002-9266-0780"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Jiang","raw_affiliation_strings":["Tsinghua University,Department of Automation and BNRist,Beijing,China,100084"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation and BNRist,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068624597","display_name":"Shiji Song","orcid":"https://orcid.org/0000-0001-7361-9283"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiji Song","raw_affiliation_strings":["Tsinghua University,Department of Automation and BNRist,Beijing,China,100084"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation and BNRist,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4116,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.89232886,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1115","last_page":"1120"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.9995999932289124,"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"}},{"id":"https://openalex.org/T12401","display_name":"Scheduling and Timetabling Solutions","score":0.9872999787330627,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.968500018119812,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/column-generation","display_name":"Column generation","score":0.6188850402832031},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5613430142402649},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5528450012207031},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5229759216308594},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.506965160369873},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4955228567123413},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47064417600631714},{"id":"https://openalex.org/keywords/crew-scheduling","display_name":"Crew scheduling","score":0.46862319111824036},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.43018850684165955},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3946112096309662},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22825995087623596},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17176023125648499},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1442246437072754}],"concepts":[{"id":"https://openalex.org/C168956720","wikidata":"https://www.wikidata.org/wiki/Q3123181","display_name":"Column generation","level":2,"score":0.6188850402832031},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5613430142402649},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5528450012207031},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5229759216308594},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.506965160369873},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4955228567123413},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47064417600631714},{"id":"https://openalex.org/C2776986690","wikidata":"https://www.wikidata.org/wiki/Q1139830","display_name":"Crew scheduling","level":3,"score":0.46862319111824036},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.43018850684165955},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3946112096309662},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22825995087623596},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17176023125648499},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1442246437072754}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc53654.2022.9945420","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc53654.2022.9945420","pdf_url":null,"source":{"id":"https://openalex.org/S4363607746","display_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322108","display_name":"Ministry of Science and Technology","ror":"https://ror.org/032e49973"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W194441499","https://openalex.org/W311217234","https://openalex.org/W1560337332","https://openalex.org/W1836465849","https://openalex.org/W1970355999","https://openalex.org/W1987569535","https://openalex.org/W2128968951","https://openalex.org/W2167580124","https://openalex.org/W2408262133","https://openalex.org/W2604384742","https://openalex.org/W2607264901","https://openalex.org/W2804057010","https://openalex.org/W2804168480","https://openalex.org/W2949978034","https://openalex.org/W3006854955","https://openalex.org/W3152893301","https://openalex.org/W6638667902","https://openalex.org/W6752110883"],"related_works":["https://openalex.org/W2408706507","https://openalex.org/W825094129","https://openalex.org/W1979086645","https://openalex.org/W2978976313","https://openalex.org/W596388258","https://openalex.org/W2003066927","https://openalex.org/W2200888301","https://openalex.org/W2587401452","https://openalex.org/W2328733308","https://openalex.org/W4213208265"],"abstract_inverted_index":{"Set":[0],"covering":[1,39],"problem":[2],"is":[3],"an":[4,26,129],"important":[5],"class":[6],"of":[7],"combinatorial":[8],"optimization":[9],"problems,":[10],"which":[11],"has":[12],"been":[13],"widely":[14],"applied":[15],"and":[16,81,102],"studied":[17],"in":[18,58,154],"many":[19],"fields.":[20],"In":[21,119,139],"this":[22,82],"paper,":[23],"we":[24,123,143],"propose":[25],"improved":[27],"column":[28,107],"generation":[29,108],"algorithm":[30,67,96],"with":[31,77,128,149],"neural":[32,45,48],"prediction":[33,49],"(CG-P)":[34],"for":[35,62,115],"solving":[36],"graph-based":[37],"set":[38],"problems.":[40],"We":[41,92,110],"leverage":[42],"a":[43,69,126,146,150],"graph":[44,71,83],"network":[46],"based":[47],"model":[50],"to":[51,55,137],"predict":[52],"the":[53,59,75,89,94,105,120,134,140],"probability":[54],"be":[56],"included":[57],"final":[60],"solution":[61,90,113,127,148],"each":[63],"edge.":[64],"Our":[65],"CG-P":[66,95,117],"constructs":[68],"reduced":[70],"that":[72],"only":[73,155],"contains":[74],"edges":[76],"higher":[78],"predicted":[79],"probability,":[80],"reduction":[84],"process":[85],"significantly":[86],"speeds":[87],"up":[88],"process.":[91],"evaluate":[93],"on":[97],"railway":[98],"crew":[99],"scheduling":[100],"problems":[101],"it":[103],"outperforms":[104],"baseline":[106],"algorithm.":[109,118],"provide":[111],"two":[112],"modes":[114],"our":[116],"optimal":[121],"mode,":[122,142],"can":[124,144],"obtain":[125,145],"optimality":[130,152],"guarantee":[131],"while":[132],"reducing":[133],"time":[135],"cost":[136],"63.12%.":[138],"fast":[141],"sub-optimal":[147],"7.62%":[151],"gap":[153],"2.91%":[156],"computation":[157],"time.":[158]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}