{"id":"https://openalex.org/W7134245968","doi":"https://doi.org/10.48550/arxiv.2603.05760","title":"MIRACL: A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation","display_name":"MIRACL: A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation","publication_year":2026,"publication_date":"2026-03-05","ids":{"openalex":"https://openalex.org/W7134245968","doi":"https://doi.org/10.48550/arxiv.2603.05760"},"language":"en","primary_location":{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/56d3c09a-7fe7-4cff-ae2c-a37da99c4ae9","is_oa":true,"landing_page_url":"https://research.manchester.ac.uk/en/publications/56d3c09a-7fe7-4cff-ae2c-a37da99c4ae9","pdf_url":"https://pure.manchester.ac.uk/ws/files/1843294128/2603.05760v1.pdf","source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Rachman, R, Tingey, J, Allmendinger, R, Pan, W, Shukla, P & Nasution, B I 2026 'Miracl : A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation' arXiv, pp. 1-14. https://doi.org/10.48550/arXiv.2603.05760","raw_type":"info:eu-repo/semantics/preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure.manchester.ac.uk/ws/files/1843294128/2603.05760v1.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116465069","display_name":"Rifny Rachman","orcid":"https://orcid.org/0000-0001-9906-1812"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rachman, Rifny","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055457679","display_name":"Josh Tingey","orcid":"https://orcid.org/0000-0003-3776-674X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tingey, Josh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053181919","display_name":"Richard Allmendinger","orcid":"https://orcid.org/0000-0003-1236-3143"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Allmendinger, Richard","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128411797","display_name":"Pan, Wei, Ph. D. Massachusetts Institute of Technology","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128617221","display_name":"Pradyumn Shukla","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shukla, Pradyumn","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5028652945","display_name":"Bahrul Ilmi Nasution","orcid":"https://orcid.org/0000-0001-6526-7185"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nasution, Bahrul Ilmi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5116465069"],"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.6935999989509583,"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"}},"topics":[{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.6935999989509583,"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"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.0778999999165535,"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"}},{"id":"https://openalex.org/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.017400000244379044,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.800599992275238},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.7095999717712402},{"id":"https://openalex.org/keywords/supply-chain","display_name":"Supply chain","score":0.6416000127792358},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6324999928474426},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5340999960899353},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.4943999946117401},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.4059000015258789}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.800599992275238},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.7095999717712402},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6764000058174133},{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.6416000127792358},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6324999928474426},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5340999960899353},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.4943999946117401},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41359999775886536},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4059000015258789},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38190001249313354},{"id":"https://openalex.org/C44104985","wikidata":"https://www.wikidata.org/wiki/Q492886","display_name":"Supply chain management","level":3,"score":0.33180001378059387},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3140999972820282},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C199185054","wikidata":"https://www.wikidata.org/wiki/Q552299","display_name":"Chain (unit)","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.26010000705718994},{"id":"https://openalex.org/C2781018962","wikidata":"https://www.wikidata.org/wiki/Q5164884","display_name":"Container (type theory)","level":2,"score":0.25870001316070557}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/56d3c09a-7fe7-4cff-ae2c-a37da99c4ae9","is_oa":true,"landing_page_url":"https://research.manchester.ac.uk/en/publications/56d3c09a-7fe7-4cff-ae2c-a37da99c4ae9","pdf_url":"https://pure.manchester.ac.uk/ws/files/1843294128/2603.05760v1.pdf","source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Rachman, R, Tingey, J, Allmendinger, R, Pan, W, Shukla, P & Nasution, B I 2026 'Miracl : A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation' arXiv, pp. 1-14. https://doi.org/10.48550/arXiv.2603.05760","raw_type":"info:eu-repo/semantics/preprint"},{"id":"pmh:doi:10.48550/arxiv.2603.05760","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.05760","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05760","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/56d3c09a-7fe7-4cff-ae2c-a37da99c4ae9","is_oa":true,"landing_page_url":"https://research.manchester.ac.uk/en/publications/56d3c09a-7fe7-4cff-ae2c-a37da99c4ae9","pdf_url":"https://pure.manchester.ac.uk/ws/files/1843294128/2603.05760v1.pdf","source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Rachman, R, Tingey, J, Allmendinger, R, Pan, W, Shukla, P & Nasution, B I 2026 'Miracl : A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation' arXiv, pp. 1-14. https://doi.org/10.48550/arXiv.2603.05760","raw_type":"info:eu-repo/semantics/preprint"},"sustainable_development_goals":[{"score":0.6285465955734253,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G4100044805","display_name":"Centre for Data Analytics and Society","funder_award_id":"ES/T002085/1","funder_id":"https://openalex.org/F4320334630","funder_display_name":"Economic and Social Research Council"}],"funders":[{"id":"https://openalex.org/F4320314731","display_name":"UK Research and Innovation","ror":"https://ror.org/001aqnf71"},{"id":"https://openalex.org/F4320334630","display_name":"Economic and Social Research Council","ror":"https://ror.org/03n0ht308"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7134245968.pdf","grobid_xml":"https://content.openalex.org/works/W7134245968.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multi-objective":[0],"reinforcement":[1],"learning":[2],"(MORL)":[3],"is":[4,27,96,116],"effective":[5],"for":[6,32,55,69,159],"multi-echelon":[7],"combinatorial":[8,106],"supply":[9,112],"chain":[10,113],"optimisation,":[11],"where":[12],"tasks":[13,79],"involve":[14],"high":[15],"dimensionality,":[16],"uncertainty,":[17],"and":[18,35,73,90,119,147],"competing":[19],"objectives.":[20],"However,":[21],"its":[22],"deployment":[23],"in":[24,88,105,110,136,163],"dynamic":[25,123],"environments":[26],"hindered":[28],"by":[29],"the":[30,97,111,155],"need":[31],"task-specific":[33],"retraining":[34],"substantial":[36],"computational":[37],"cost.":[38],"We":[39],"introduce":[40],"MIRACL":[41,62,115,131,158],"(Meta":[42],"multI-objective":[43],"Reinforcement":[44],"leArning":[45],"with":[46,102],"Composite":[47],"Learning),":[48],"a":[49,56,75,81],"hierarchical":[50],"Meta-MORL":[51,101],"framework":[52],"that":[53,130],"allows":[54],"few-shot":[57],"generalisation":[58],"across":[59,78],"diverse":[60],"tasks.":[61],"decomposes":[63],"each":[64],"task":[65],"into":[66],"structured":[67],"subproblems":[68],"efficient":[70,161],"policy":[71,77],"adaptation":[72,83,162],"meta-learns":[74],"global":[76],"using":[80],"Pareto-based":[82],"strategy":[84],"to":[85,121,138,143],"encourage":[86],"diversity":[87],"meta-training":[89],"fine-tuning.":[91],"To":[92],"our":[93],"knowledge,":[94],"this":[95],"first":[98],"integration":[99],"of":[100,157],"such":[103],"mechanisms":[104],"optimisation.":[107],"Although":[108],"validated":[109],"domain,":[114],"theoretically":[117],"domain-agnostic":[118],"applicable":[120],"broader":[122],"multi-objective":[124,164],"decision-making":[125],"problems.":[126,165],"Empirical":[127],"evaluations":[128],"show":[129],"outperforms":[132],"conventional":[133],"MORL":[134],"baselines":[135],"simple":[137],"moderate":[139],"tasks,":[140],"achieving":[141],"up":[142],"10%":[144],"higher":[145],"hypervolume":[146],"5%":[148],"better":[149],"expected":[150],"utility.":[151],"These":[152],"results":[153],"underscore":[154],"potential":[156],"robust,":[160]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2026-03-10T00:00:00"}
