{"id":"https://openalex.org/W2588445526","doi":"https://doi.org/10.1109/liss.2016.7854498","title":"Applying interval knowledge to facilitate seaport container throughput volume forecasting","display_name":"Applying interval knowledge to facilitate seaport container throughput volume forecasting","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2588445526","doi":"https://doi.org/10.1109/liss.2016.7854498","mag":"2588445526"},"language":"en","primary_location":{"id":"doi:10.1109/liss.2016.7854498","is_oa":false,"landing_page_url":"https://doi.org/10.1109/liss.2016.7854498","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Logistics, Informatics and Service Sciences (LISS)","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/A5026183659","display_name":"Anqiang Huang","orcid":"https://orcid.org/0000-0002-1501-4013"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Anqiang Huang","raw_affiliation_strings":["School of Economics and Management, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044312130","display_name":"Zhenji Zhang","orcid":"https://orcid.org/0000-0002-2738-7749"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenji Zhang","raw_affiliation_strings":["School of Economics and Management, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065730977","display_name":"Guowei Hua","orcid":"https://orcid.org/0000-0003-0990-4711"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guowei Hua","raw_affiliation_strings":["School of Economics and Management, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100698002","display_name":"Xianliang Shi","orcid":"https://orcid.org/0000-0003-2035-2989"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianliang Shi","raw_affiliation_strings":["School of Economics and Management, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034690093","display_name":"Zaili Yang","orcid":"https://orcid.org/0000-0003-1385-493X"},"institutions":[{"id":"https://openalex.org/I63098007","display_name":"Liverpool John Moores University","ror":"https://ror.org/04zfme737","country_code":"GB","type":"education","lineage":["https://openalex.org/I63098007"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zaili Yang","raw_affiliation_strings":["Liverpool Logistics, Offshore and Marine Research Institute, Liverpool John Moores University, Liverpool, UK"],"affiliations":[{"raw_affiliation_string":"Liverpool Logistics, Offshore and Marine Research Institute, Liverpool John Moores University, Liverpool, UK","institution_ids":["https://openalex.org/I63098007"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5026183659"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.20682121,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9937000274658203,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9937000274658203,"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":0.9822999835014343,"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/T12135","display_name":"Fuzzy Systems and Optimization","score":0.9814000129699707,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/container","display_name":"Container (type theory)","score":0.7709181308746338},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7290475368499756},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.6627224683761597},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5475788712501526},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.49706342816352844},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.4534792900085449},{"id":"https://openalex.org/keywords/interval-estimation","display_name":"Interval estimation","score":0.4530780017375946},{"id":"https://openalex.org/keywords/delphi-method","display_name":"Delphi method","score":0.43897849321365356},{"id":"https://openalex.org/keywords/delphi","display_name":"Delphi","score":0.4139987826347351},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.41133901476860046},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3990841507911682},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3411654531955719},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33846116065979004},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1579062044620514},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.13540995121002197},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11019939184188843}],"concepts":[{"id":"https://openalex.org/C2781018962","wikidata":"https://www.wikidata.org/wiki/Q5164884","display_name":"Container (type theory)","level":2,"score":0.7709181308746338},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7290475368499756},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.6627224683761597},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5475788712501526},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.49706342816352844},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.4534792900085449},{"id":"https://openalex.org/C205167067","wikidata":"https://www.wikidata.org/wiki/Q3300636","display_name":"Interval estimation","level":3,"score":0.4530780017375946},{"id":"https://openalex.org/C60641444","wikidata":"https://www.wikidata.org/wiki/Q841602","display_name":"Delphi method","level":2,"score":0.43897849321365356},{"id":"https://openalex.org/C2779495148","wikidata":"https://www.wikidata.org/wiki/Q487378","display_name":"Delphi","level":2,"score":0.4139987826347351},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.41133901476860046},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3990841507911682},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3411654531955719},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33846116065979004},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1579062044620514},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.13540995121002197},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11019939184188843},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/liss.2016.7854498","is_oa":false,"landing_page_url":"https://doi.org/10.1109/liss.2016.7854498","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Logistics, Informatics and Service Sciences (LISS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life below water","score":0.550000011920929,"id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1928988819","https://openalex.org/W1968405115","https://openalex.org/W1968842822","https://openalex.org/W1969567783","https://openalex.org/W1974157859","https://openalex.org/W1980290439","https://openalex.org/W1983396104","https://openalex.org/W1993653877","https://openalex.org/W1997675197","https://openalex.org/W2004884942","https://openalex.org/W2009083767","https://openalex.org/W2011427904","https://openalex.org/W2017563734","https://openalex.org/W2020511379","https://openalex.org/W2032838257","https://openalex.org/W2040255839","https://openalex.org/W2047374208","https://openalex.org/W2054730020","https://openalex.org/W2055206761","https://openalex.org/W2057506862","https://openalex.org/W2064322681","https://openalex.org/W2066320627","https://openalex.org/W2067705975","https://openalex.org/W2067975844","https://openalex.org/W2082510242","https://openalex.org/W2093652038","https://openalex.org/W2093670211","https://openalex.org/W2104546629","https://openalex.org/W2106438008","https://openalex.org/W2109141422","https://openalex.org/W2117305194","https://openalex.org/W2139212933","https://openalex.org/W2166864322","https://openalex.org/W2168868062","https://openalex.org/W2172234679","https://openalex.org/W3123547113","https://openalex.org/W4237403557","https://openalex.org/W4248996458","https://openalex.org/W6640317617","https://openalex.org/W6941056036"],"related_works":["https://openalex.org/W2062050714","https://openalex.org/W2030487194","https://openalex.org/W2971523465","https://openalex.org/W1965155071","https://openalex.org/W1969884608","https://openalex.org/W2041058937","https://openalex.org/W2224226767","https://openalex.org/W2031546510","https://openalex.org/W2001765788","https://openalex.org/W1573061253"],"abstract_inverted_index":{"Substantial":[0],"studies":[1,32],"integrating":[2],"experts'":[3,38],"knowledge":[4,24,40,85,90],"with":[5,97],"statistical":[6,161],"forecasting":[7,27,147],"models":[8,162],"have":[9],"been":[10],"implemented":[11],"to":[12,21,46,109,159],"investigate":[13],"a":[14,80,93,157],"long-lasting":[15],"and":[16,50],"disputing":[17],"issue,":[18],"the":[19,88,98,104,111,120,125,141,146,153],"extent":[20],"which":[22,86],"expert":[23,95],"can":[25],"improve":[26],"performance.":[28,148],"However,":[29],"many":[30],"current":[31],"are":[33,44],"not":[34],"capable":[35],"of":[36,115,124],"applying":[37],"interval":[39,63,84,89,138],"in":[41,73,119,130],"forecasting.":[42,166],"Experts":[43],"expected":[45],"be":[47],"more":[48],"competent":[49],"confident,":[51],"given":[52],"that":[53,137,152],"human":[54],"brains":[55],"do":[56],"much":[57],"better":[58],"on":[59],"fuzzy":[60],"calculation":[61],"like":[62,68],"estimation":[64],"than":[65],"accurate":[66],"computation":[67],"point":[69],"estimation.":[70],"To":[71],"fill":[72],"this":[74,76],"gap,":[75],"paper":[77],"first":[78],"proposes":[79],"new":[81],"methodology":[82,106,155],"incorporating":[83],"combines":[87],"generated":[91],"by":[92],"Delphi-based":[94],"system":[96],"SARIMA":[99],"model.":[100],"For":[101],"validation":[102],"purposes,":[103],"proposed":[105,142,154],"is":[107],"applied":[108],"forecast":[110],"container":[112,164],"throughput":[113,165],"volume":[114],"Qingdao":[116],"port":[117],"lying":[118],"Bohai":[121],"Rim,":[122],"one":[123],"most":[126],"dynamic":[127],"economic":[128],"regions":[129],"China.":[131],"The":[132],"empirical":[133],"results":[134],"clearly":[135],"show":[136],"knowledge,":[139],"following":[140],"methodology,":[143],"significantly":[144],"improves":[145],"This":[149],"finding":[150],"implies":[151],"has":[156],"potential":[158],"sharpen":[160],"for":[163]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
