{"id":"https://openalex.org/W2132683956","doi":"https://doi.org/10.1109/wsc.2003.1261625","title":"The modal-shift transportation planning problem and its fast steepest descent algorithm","display_name":"The modal-shift transportation planning problem and its fast steepest descent algorithm","publication_year":2004,"publication_date":"2004-08-23","ids":{"openalex":"https://openalex.org/W2132683956","doi":"https://doi.org/10.1109/wsc.2003.1261625","mag":"2132683956"},"language":"en","primary_location":{"id":"doi:10.1109/wsc.2003.1261625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2003.1261625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)","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/A5025560689","display_name":"Masami Amano","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145865","display_name":"IBM Research - Tokyo","ror":"https://ror.org/04915qk43","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145865"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"M. Amano","raw_affiliation_strings":["Tokyo Research Laboratory, IBM Research, Yamato, Kanagawa, Japan","Tokyo Res. Lab., IBM Res., Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Research Laboratory, IBM Research, Yamato, Kanagawa, Japan","institution_ids":["https://openalex.org/I4210145865"]},{"raw_affiliation_string":"Tokyo Res. Lab., IBM Res., Tokyo, Japan","institution_ids":["https://openalex.org/I4210145865"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025301518","display_name":"Terry T. Yoshizumi","orcid":"https://orcid.org/0000-0002-5399-9984"},"institutions":[{"id":"https://openalex.org/I4210145865","display_name":"IBM Research - Tokyo","ror":"https://ror.org/04915qk43","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145865"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"T. Yoshizumi","raw_affiliation_strings":["Tokyo Research Laboratory, IBM Research, Yamato, Kanagawa, Japan","Tokyo Res. Lab., IBM Res., Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Research Laboratory, IBM Research, Yamato, Kanagawa, Japan","institution_ids":["https://openalex.org/I4210145865"]},{"raw_affiliation_string":"Tokyo Res. Lab., IBM Res., Tokyo, Japan","institution_ids":["https://openalex.org/I4210145865"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060261086","display_name":"Hiroyuki Okano","orcid":"https://orcid.org/0000-0001-8198-1354"},"institutions":[{"id":"https://openalex.org/I4210145865","display_name":"IBM Research - Tokyo","ror":"https://ror.org/04915qk43","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145865"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"H. Okano","raw_affiliation_strings":["Tokyo Research Laboratory, IBM Research, Yamato, Kanagawa, Japan","Tokyo Res. Lab., IBM Res., Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Research Laboratory, IBM Research, Yamato, Kanagawa, Japan","institution_ids":["https://openalex.org/I4210145865"]},{"raw_affiliation_string":"Tokyo Res. Lab., IBM Res., Tokyo, Japan","institution_ids":["https://openalex.org/I4210145865"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5025560689"],"corresponding_institution_ids":["https://openalex.org/I4210145865"],"apc_list":null,"apc_paid":null,"fwci":2.5177,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.89717756,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1720","last_page":"1728"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.9997000098228455,"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":0.9997000098228455,"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/T11942","display_name":"Transportation and Mobility Innovations","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10698","display_name":"Transportation Planning and Optimization","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.7170405387878418},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.6573027968406677},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.6241680383682251},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.6190876364707947},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6140179634094238},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5586884021759033},{"id":"https://openalex.org/keywords/descent","display_name":"Descent (aeronautics)","score":0.5537346005439758},{"id":"https://openalex.org/keywords/greedy-algorithm","display_name":"Greedy algorithm","score":0.5449645519256592},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5394389033317566},{"id":"https://openalex.org/keywords/method-of-steepest-descent","display_name":"Method of steepest descent","score":0.45224013924598694},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1935473382472992},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15346351265907288},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1461729109287262},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.13773632049560547}],"concepts":[{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.7170405387878418},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.6573027968406677},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.6241680383682251},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.6190876364707947},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6140179634094238},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5586884021759033},{"id":"https://openalex.org/C2776637919","wikidata":"https://www.wikidata.org/wiki/Q624380","display_name":"Descent (aeronautics)","level":2,"score":0.5537346005439758},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.5449645519256592},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5394389033317566},{"id":"https://openalex.org/C158847443","wikidata":"https://www.wikidata.org/wiki/Q1997812","display_name":"Method of steepest descent","level":2,"score":0.45224013924598694},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1935473382472992},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15346351265907288},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1461729109287262},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.13773632049560547},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wsc.2003.1261625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2003.1261625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5699999928474426}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1971566751","https://openalex.org/W2096654368","https://openalex.org/W2113937063","https://openalex.org/W2133477672","https://openalex.org/W2504348940","https://openalex.org/W7052883899"],"related_works":["https://openalex.org/W2393439324","https://openalex.org/W2081157561","https://openalex.org/W131846159","https://openalex.org/W3197592642","https://openalex.org/W2155993847","https://openalex.org/W2914176832","https://openalex.org/W2088010696","https://openalex.org/W2386196923","https://openalex.org/W2027078210","https://openalex.org/W2368086325"],"abstract_inverted_index":{"The":[0],"modal-shift":[1],"transportation":[2],"planning":[3],"problem":[4,8],"(MSTPP)":[5],"is":[6,100],"the":[7,17,40,67,95],"that":[9],"finds":[10,59],"a":[11,33,45,55,60,75,84],"feasible":[12],"schedule":[13,61],"for":[14,50],"carriers":[15,28],"with":[16],"minimum":[18],"total":[19],"cost":[20],"when":[21],"sets":[22],"of":[23,47,66,77],"facilities,":[24],"delivery":[25,52],"orders,":[26],"and":[27],"are":[29],"given.":[30],"We":[31],"propose":[32],"fast":[34],"steepest":[35],"descent":[36,71],"algorithm":[37],"to":[38],"solve":[39],"MSTPP.":[41],"Our":[42],"solution":[43],"generates":[44],"set":[46],"candidate":[48,68],"routes":[49,69],"each":[51,81],"order":[53],"as":[54],"preprocess.":[56],"Then,":[57],"it":[58],"by":[62,83],"iteratively":[63],"updating":[64],"selections":[65],"in":[70,98],"directions,":[72],"while":[73],"computing":[74],"configuration":[76],"carrier":[78],"movements":[79],"at":[80],"iteration":[82],"greedy":[85],"algorithm.":[86],"Intensive":[87],"numerical":[88],"study":[89],"using":[90],"artificial":[91],"data":[92],"modeled":[93],"from":[94],"manufacturing":[96],"industry":[97],"Japan":[99],"also":[101],"presented.":[102]},"counts_by_year":[{"year":2018,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-02-25T21:11:00.739837","created_date":"2025-10-10T00:00:00"}
