{"id":"https://openalex.org/W7161607447","doi":"https://doi.org/10.31449/inf.v50i13.9618","title":"Hybrid GRA-GRM Model for Spatiotemporal Forecasting in Port Logistics Using Grey System Theory and Nonlinear Feature Selection","display_name":"Hybrid GRA-GRM Model for Spatiotemporal Forecasting in Port Logistics Using Grey System Theory and Nonlinear Feature Selection","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161607447","doi":"https://doi.org/10.31449/inf.v50i13.9618"},"language":null,"primary_location":{"id":"doi:10.31449/inf.v50i13.9618","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i13.9618","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/9618/6699","source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.informatica.si/index.php/informatica/article/download/9618/6699","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136440876","display_name":"Yanyan Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I46305995","display_name":"Taiyuan University of Science and Technology","ror":"https://ror.org/01wcbdc92","country_code":"CN","type":"education","lineage":["https://openalex.org/I46305995"]},{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyan Wu","raw_affiliation_strings":["School of Vehicle and Traffic Engineering , Taiyuan University of Science and Technology , Taiyuan Shanxi , 030024 , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Vehicle and Traffic Engineering , Taiyuan University of Science and Technology , Taiyuan Shanxi , 030024 , China","institution_ids":["https://openalex.org/I46305995","https://openalex.org/I9086337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5136445787","display_name":"Yang Yang Zhanlong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Yang Zhanlong Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.63428244,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"50","issue":"13","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12368","display_name":"Grey System Theory Applications","score":0.579800009727478,"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"}},"topics":[{"id":"https://openalex.org/T12368","display_name":"Grey System Theory Applications","score":0.579800009727478,"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/T11223","display_name":"Maritime Ports and Logistics","score":0.06679999828338623,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.04179999977350235,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.6481999754905701},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6342999935150146},{"id":"https://openalex.org/keywords/grey-relational-analysis","display_name":"Grey relational analysis","score":0.4975000023841858},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.4934999942779541},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.44620001316070557},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4350000023841858},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4345000088214874},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.424699991941452},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4221999943256378}],"concepts":[{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.6481999754905701},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6342999935150146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6248999834060669},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5457000136375427},{"id":"https://openalex.org/C64734493","wikidata":"https://www.wikidata.org/wiki/Q5608296","display_name":"Grey relational analysis","level":2,"score":0.4975000023841858},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.4934999942779541},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.44620001316070557},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4350000023841858},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4345000088214874},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.424699991941452},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4221999943256378},{"id":"https://openalex.org/C32802771","wikidata":"https://www.wikidata.org/wiki/Q2443617","display_name":"Port (circuit theory)","level":2,"score":0.38600000739097595},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.384799987077713},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.37720000743865967},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.3765999972820282},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36719998717308044},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.35409998893737793},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32440000772476196},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3089999854564667},{"id":"https://openalex.org/C44104985","wikidata":"https://www.wikidata.org/wiki/Q492886","display_name":"Supply chain management","level":3,"score":0.2768999934196472},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.2761000096797943},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.2578999996185303},{"id":"https://openalex.org/C135510737","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance indicator","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C206041023","wikidata":"https://www.wikidata.org/wiki/Q1751970","display_name":"Wilcoxon signed-rank test","level":3,"score":0.25380000472068787},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.31449/inf.v50i13.9618","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i13.9618","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/9618/6699","source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.31449/inf.v50i13.9618","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i13.9618","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/9618/6699","source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4443015158176422,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7161607447.pdf","grobid_xml":"https://content.openalex.org/works/W7161607447.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"and":[1,15,21,30,54,67,108,118,167,209,226,236,240,259,275,298,314,333,340],"timely":[2],"port":[3,75,128,272,345],"logistics":[4,20,76,129,346],"demand":[5,77,249,283],"forecast":[6,250],"is":[7,330],"essential":[8],"for":[9,304],"increasing":[10],"operational":[11,341],"efficiency,":[12],"cutting":[13],"costs,":[14],"raising":[16],"customer":[17],"satisfaction":[18],"in":[19,24,74,84,181,343],"transportation":[22],"networks":[23,171],"the":[25,44,62,124,149,224,229,234,245,287],"age":[26],"of":[27,64,86,183,191,199,207,211,214,228,233,247,254],"artificial":[28],"intelligence":[29],"quickly":[31],"changing":[32],"supply":[33],"chain":[34],"dynamics.":[35],"This":[36],"study":[37],"introduces":[38],"a":[39,91],"new":[40],"hybrid":[41],"model,":[42,47,230],"called":[43],"GRA-GRM":[45,151],"combination":[46],"that":[48,71,148,222,243,271,329],"combines":[49],"Grey":[50,55],"Relational":[51,56],"Analysis":[52],"(GRA)":[53],"Modelling":[57],"(GRM)":[58],"to":[59,295,302],"successfully":[60],"handle":[61],"difficulties":[63],"feature":[65,266],"selection":[66],"nonlinear":[68,136],"pattern":[69],"extraction":[70],"are":[72,279],"present":[73],"forecasting.":[78],"The":[79,112,131],"algorithm":[80],"outperforms":[81,153],"traditional":[82],"techniques":[83],"terms":[85,182],"predicted":[87],"accuracy":[88,246],"by":[89,121],"using":[90,138],"comprehensive":[92,109],"real-":[93],"world":[94],"dataset":[95],"from":[96],"five":[97],"major":[98],"Chinese":[99],"cities,":[100],"which":[101,269],"includes":[102],"over":[103],"10":[104],"million":[105],"package":[106],"transactions":[107],"courier":[110,276],"trajectories.":[111],"model's":[113],"GRA":[114],"component":[115],"reduces":[116],"dimensionality":[117],"improves":[119],"interpretability":[120],"thoroughly":[122],"identifying":[123],"key":[125],"variables":[126],"influencing":[127,282],"demand.":[130],"GRM":[132,158],"then":[133],"models":[134,155],"intricate":[135,344],"interactions":[137],"these":[139],"chosen":[140],"characteristics,":[141],"improving":[142],"prediction":[143],"accuracy.":[144],"Performance":[145],"evaluation":[146],"shows":[147],"suggested":[150],"model":[152,288],"baseline":[154],"like":[156,292],"standalone":[157],"(MAE":[159,173],"8.43,":[160],"RMSE":[161,175],"10.25,":[162],"MAPE":[163,177],"12.3%,":[164],"R\u00b2":[165,179],"0.841)":[166],"Long":[168],"Short-Term":[169],"Memory":[170],"(LSTM)":[172],"7.92,":[174],"9.38,":[176],"10.7%,":[178],"0.865)":[180],"predictive":[184],"metrics,":[185],"with":[186,310],"Mean":[187,195,202],"Absolute":[188,203],"Error":[189,197,205],"(MAE)":[190],"5.91":[192],"minutes,":[193,201],"Root":[194],"Square":[196],"(RMSE)":[198],"7.42":[200],"Percentage":[204],"(MAPE)":[206],"7.6%,":[208],"coefficient":[210],"determination":[212],"(R\u00b2)":[213],"0.918.":[215],"Additional":[216],"studies":[217],"include":[218],"error":[219],"distribution":[220],"histograms":[221],"demonstrate":[223],"resilience":[225],"dependability":[227],"time-series":[231],"comparisons":[232],"projected":[235],"real":[237],"logistical":[238],"demand,":[239],"geographical":[241],"heatmaps":[242],"show":[244,270],"regional":[248],"across":[251],"many":[252],"Areas":[253],"Interest":[255],"(AOIs).":[256],"Logistics":[257],"managers":[258],"policymakers":[260],"may":[261],"take":[262],"action":[263],"based":[264],"on":[265],"significance":[267],"rankings,":[268],"container":[273],"throughput":[274],"delivery":[277],"frequency":[278],"important":[280],"factors":[281],"swings.":[284],"Even":[285],"while":[286],"performs":[289],"well,":[290],"issues":[291],"its":[293],"susceptibility":[294],"input":[296],"noise":[297],"real-time":[299],"flexibility":[300],"point":[301],"areas":[303],"further":[305],"study,":[306],"such":[307],"as":[308],"integrating":[309],"IoT":[311],"data":[312],"streams":[313],"sophisticated":[315],"deep":[316],"learning":[317],"architectures.":[318],"All":[319],"things":[320],"considered,":[321],"this":[322],"research":[323],"offers":[324],"an":[325],"AI-driven":[326],"forecasting":[327],"framework":[328],"scalable,":[331],"interpretable,":[332],"efficient,":[334],"facilitating":[335],"more":[336],"intelligent":[337],"resource":[338],"allocation":[339],"decision-making":[342],"settings.":[347]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-05-19T00:00:00"}