{"id":"https://openalex.org/W7164515158","doi":"https://doi.org/10.48550/arxiv.2606.13133","title":"Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling","display_name":"Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling","publication_year":2026,"publication_date":"2026-06-11","ids":{"openalex":"https://openalex.org/W7164515158","doi":"https://doi.org/10.48550/arxiv.2606.13133"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.13133","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13133","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.13133","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138525719","display_name":"Kaito Baba","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baba, Kaito","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063171222","display_name":"Evripidis Bampis","orcid":"https://orcid.org/0000-0002-4498-3040"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bampis, Evripidis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138539908","display_name":"Giorgos Mitropoulos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mitropoulos, Giorgos","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":false,"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/T10551","display_name":"Scheduling and Optimization Algorithms","score":0.33480000495910645,"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/T10551","display_name":"Scheduling and Optimization Algorithms","score":0.33480000495910645,"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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.18129999935626984,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10720","display_name":"Complexity and Algorithms in Graphs","score":0.11620000004768372,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/job-shop-scheduling","display_name":"Job shop scheduling","score":0.8238000273704529},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.7110000252723694},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.6237000226974487},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5652999877929688},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5400999784469604},{"id":"https://openalex.org/keywords/extension","display_name":"Extension (predicate logic)","score":0.5396000146865845}],"concepts":[{"id":"https://openalex.org/C55416958","wikidata":"https://www.wikidata.org/wiki/Q6206757","display_name":"Job shop scheduling","level":3,"score":0.8238000273704529},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.7110000252723694},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.6237000226974487},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6194999814033508},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.616100013256073},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5652999877929688},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5400999784469604},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.5396000146865845},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.47510001063346863},{"id":"https://openalex.org/C148764684","wikidata":"https://www.wikidata.org/wiki/Q621751","display_name":"Approximation algorithm","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.36880001425743103},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2827000021934509},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.273499995470047},{"id":"https://openalex.org/C107321475","wikidata":"https://www.wikidata.org/wiki/Q5374254","display_name":"Empirical risk minimization","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.2662999927997589}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.13133","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13133","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.13133","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13133","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.6795758605003357}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recently,":[0],"Antoniadis":[1,42],"et":[2,43],"al.":[3],"(ICLR":[4],"2025)":[5],"proposed":[6],"a":[7,66,89,99],"framework":[8],"for":[9,69,92],"incorporating":[10],"predictions":[11,82,94],"to":[12,51,98],"approximate":[13],"NP-hard":[14],"selection":[15,59],"problems.":[16],"Despite":[17],"its":[18,28],"simplicity,":[19],"this":[20,49],"approach":[21,50],"tightly":[22],"matches":[23],"theoretical":[24],"lower":[25],"bounds,":[26],"making":[27],"generalization":[29],"highly":[30],"compelling.":[31],"We":[32,64,106],"address":[33],"an":[34,111],"open":[35],"question":[36],"raised":[37],"in":[38],"the":[39,46,56,70,103],"work":[40,109],"of":[41,48,58,83,114],"al.,":[44],"concerning":[45],"extension":[47],"other":[52],"important":[53],"problems":[54],"outside":[55],"class":[57],"problems,":[60],"such":[61],"as":[62,102],"scheduling.":[63],"develop":[65],"learning-augmented":[67],"algorithm":[68],"makespan":[71],"minimization":[72],"problem":[73],"on":[74],"unrelated":[75],"machines,":[76],"denoted":[77],"by":[78],"$R\\|C_{\\max}$.":[79],"By":[80],"using":[81],"heavy":[84],"job":[85],"assignments,":[86],"we":[87],"achieve":[88],"polynomial-time":[90],"$(1+\\varepsilon)$-approximation":[91],"accurate":[93],"that":[95],"smoothly":[96],"degrades":[97],"worst-case":[100],"2-approximation":[101],"error":[104],"increases.":[105],"conclude":[107],"our":[108,115],"with":[110],"empirical":[112],"analysis":[113],"method.":[116]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-13T00:00:00"}