{"id":"https://openalex.org/W2906863681","doi":"https://doi.org/10.1051/ro/2019006","title":"An efficient two-phase metaheuristic algorithm for the time dependent traveling Salesman problem","display_name":"An efficient two-phase metaheuristic algorithm for the time dependent traveling Salesman problem","publication_year":2019,"publication_date":"2019-01-03","ids":{"openalex":"https://openalex.org/W2906863681","doi":"https://doi.org/10.1051/ro/2019006","mag":"2906863681"},"language":"en","primary_location":{"id":"doi:10.1051/ro/2019006","is_oa":false,"landing_page_url":"https://doi.org/10.1051/ro/2019006","pdf_url":null,"source":{"id":"https://openalex.org/S20631277","display_name":"RAIRO - Operations Research","issn_l":"0399-0559","issn":["0399-0559","1290-3868","2804-7303"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319748","host_organization_name":"EDP Sciences","host_organization_lineage":["https://openalex.org/P4310319748"],"host_organization_lineage_names":["EDP Sciences"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"RAIRO - Operations Research","raw_type":"journal-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/A5037843488","display_name":"Ha-Bang Ban","orcid":"https://orcid.org/0000-0003-2241-5146"},"institutions":[{"id":"https://openalex.org/I94518387","display_name":"Hanoi University of Science and Technology","ror":"https://ror.org/04nyv3z04","country_code":"VN","type":"education","lineage":["https://openalex.org/I94518387"]}],"countries":["VN"],"is_corresponding":true,"raw_author_name":"Ha Bang Ban","raw_affiliation_strings":["Computer Science Department, School of Information and Communication Technology, Hanoi University of Science and Technology, No. 1 Dai Co Viet, Hanoi, Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, School of Information and Communication Technology, Hanoi University of Science and Technology, No. 1 Dai Co Viet, Hanoi, Vietnam","institution_ids":["https://openalex.org/I94518387"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5037843488"],"corresponding_institution_ids":["https://openalex.org/I94518387"],"apc_list":null,"apc_paid":null,"fwci":1.7057,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.86451591,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"53","issue":"3","first_page":"917","last_page":"935"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.9998000264167786,"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":0.9998000264167786,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9952999949455261,"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/T12176","display_name":"Optimization and Packing Problems","score":0.9861999750137329,"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/tabu-search","display_name":"Tabu search","score":0.8882609605789185},{"id":"https://openalex.org/keywords/travelling-salesman-problem","display_name":"Travelling salesman problem","score":0.8509221076965332},{"id":"https://openalex.org/keywords/metaheuristic","display_name":"Metaheuristic","score":0.7281957864761353},{"id":"https://openalex.org/keywords/variable-neighborhood-search","display_name":"Variable neighborhood search","score":0.6766636967658997},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.6636925935745239},{"id":"https://openalex.org/keywords/2-opt","display_name":"2-opt","score":0.5963661074638367},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5763629078865051},{"id":"https://openalex.org/keywords/combinatorial-optimization","display_name":"Combinatorial optimization","score":0.5663389563560486},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.529185950756073},{"id":"https://openalex.org/keywords/guided-local-search","display_name":"Guided Local Search","score":0.5200702548027039},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5012872219085693},{"id":"https://openalex.org/keywords/local-optimum","display_name":"Local optimum","score":0.47542741894721985},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.44266051054000854},{"id":"https://openalex.org/keywords/bottleneck-traveling-salesman-problem","display_name":"Bottleneck traveling salesman problem","score":0.44155895709991455},{"id":"https://openalex.org/keywords/traveling-purchaser-problem","display_name":"Traveling purchaser problem","score":0.4382486641407013},{"id":"https://openalex.org/keywords/local-search","display_name":"Local search (optimization)","score":0.4274851679801941},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.4131767749786377},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.387191504240036}],"concepts":[{"id":"https://openalex.org/C123370116","wikidata":"https://www.wikidata.org/wiki/Q1424540","display_name":"Tabu search","level":2,"score":0.8882609605789185},{"id":"https://openalex.org/C175859090","wikidata":"https://www.wikidata.org/wiki/Q322212","display_name":"Travelling salesman problem","level":2,"score":0.8509221076965332},{"id":"https://openalex.org/C109718341","wikidata":"https://www.wikidata.org/wiki/Q1385229","display_name":"Metaheuristic","level":2,"score":0.7281957864761353},{"id":"https://openalex.org/C2776435737","wikidata":"https://www.wikidata.org/wiki/Q7915703","display_name":"Variable neighborhood search","level":3,"score":0.6766636967658997},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.6636925935745239},{"id":"https://openalex.org/C106472803","wikidata":"https://www.wikidata.org/wiki/Q291440","display_name":"2-opt","level":3,"score":0.5963661074638367},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5763629078865051},{"id":"https://openalex.org/C52692508","wikidata":"https://www.wikidata.org/wiki/Q1333872","display_name":"Combinatorial optimization","level":2,"score":0.5663389563560486},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.529185950756073},{"id":"https://openalex.org/C90189156","wikidata":"https://www.wikidata.org/wiki/Q5615481","display_name":"Guided Local Search","level":3,"score":0.5200702548027039},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5012872219085693},{"id":"https://openalex.org/C141934464","wikidata":"https://www.wikidata.org/wiki/Q3305386","display_name":"Local optimum","level":2,"score":0.47542741894721985},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.44266051054000854},{"id":"https://openalex.org/C7668213","wikidata":"https://www.wikidata.org/wiki/Q4949085","display_name":"Bottleneck traveling salesman problem","level":3,"score":0.44155895709991455},{"id":"https://openalex.org/C4331618","wikidata":"https://www.wikidata.org/wiki/Q7836034","display_name":"Traveling purchaser problem","level":4,"score":0.4382486641407013},{"id":"https://openalex.org/C135320971","wikidata":"https://www.wikidata.org/wiki/Q1868524","display_name":"Local search (optimization)","level":2,"score":0.4274851679801941},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.4131767749786377},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.387191504240036},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1051/ro/2019006","is_oa":false,"landing_page_url":"https://doi.org/10.1051/ro/2019006","pdf_url":null,"source":{"id":"https://openalex.org/S20631277","display_name":"RAIRO - Operations Research","issn_l":"0399-0559","issn":["0399-0559","1290-3868","2804-7303"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319748","host_organization_name":"EDP Sciences","host_organization_lineage":["https://openalex.org/P4310319748"],"host_organization_lineage_names":["EDP Sciences"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"RAIRO - Operations Research","raw_type":"journal-article"},{"id":"pmh:oai:numdam.org:RO_2019__53_3_917_0","is_oa":false,"landing_page_url":"https://www.numdam.org/articles/10.1051/ro/2019006/","pdf_url":null,"source":{"id":"https://openalex.org/S4306402264","display_name":"French digital mathematics library (Numdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I70900168","host_organization_name":"Universit\u00e9 Savoie Mont Blanc","host_organization_lineage":["https://openalex.org/I70900168"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.6000000238418579,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W9487217","https://openalex.org/W164198786","https://openalex.org/W187767610","https://openalex.org/W959364997","https://openalex.org/W1491097971","https://openalex.org/W1963722242","https://openalex.org/W1966793805","https://openalex.org/W1984837337","https://openalex.org/W1987671593","https://openalex.org/W2005117954","https://openalex.org/W2009279347","https://openalex.org/W2011595401","https://openalex.org/W2044718761","https://openalex.org/W2054716257","https://openalex.org/W2056425817","https://openalex.org/W2059305197","https://openalex.org/W2064816527","https://openalex.org/W2066528983","https://openalex.org/W2072136110","https://openalex.org/W2080484716","https://openalex.org/W2087852198","https://openalex.org/W2089338760","https://openalex.org/W2090275183","https://openalex.org/W2095776854","https://openalex.org/W2098156747","https://openalex.org/W2104670598","https://openalex.org/W2115556222","https://openalex.org/W2129522056","https://openalex.org/W2149621837","https://openalex.org/W2153278891","https://openalex.org/W2167218946","https://openalex.org/W2242807460","https://openalex.org/W2339500526","https://openalex.org/W2997776631","https://openalex.org/W4255108251","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4389782626","https://openalex.org/W4376138746","https://openalex.org/W2359992618","https://openalex.org/W2361554335","https://openalex.org/W4220987199","https://openalex.org/W2362402143","https://openalex.org/W1984036937","https://openalex.org/W2185228534","https://openalex.org/W2991484371","https://openalex.org/W2624732115"],"abstract_inverted_index":{"The":[0],"Time":[1],"Dependent":[2],"Traveling":[3,54],"Salesman":[4,55],"Problem":[5,51,56],"(TDTSP)":[6],"is":[7,41,92,100,109,124],"a":[8,42,87,146,154,167,203],"class":[9],"of":[10,23,47,121,162,170,206],"NP-hard":[11],"combinatorial":[12],"optimization":[13],"problems":[14],"which":[15,67],"has":[16,32],"many":[17],"practical":[18],"applications.":[19],"To":[20],"the":[21,30,48,69,78,84,90,107,118,137,160,173,176,191,214,221],"best":[22,216],"our":[24,127,187,211],"knowledge,":[25],"developing":[26],"metaheuristic":[27,66],"algorithm":[28,128,188,212,223],"for":[29,158,194],"problem":[31],"not":[33],"been":[34],"studied":[35],"much":[36],"before,":[37],"even":[38],"though":[39],"it":[40],"natural":[43],"and":[44,77,114,134,152],"general":[45],"extension":[46],"Minimum":[49],"Latency":[50],"(MLP)":[52],"or":[53],"(TSP).":[57],"In":[58,86,103],"this":[59],"paper,":[60],"we":[61,144],"propose":[62],"an":[63,96,104],"effective":[64],"two-phase":[65],"combines":[68],"Insertion":[70],"Heuristic":[71],"(IH),":[72],"Variable":[73],"Neighborhood":[74],"Search":[75],"(VNS)":[76],"tabu":[79,122],"search":[80,123,138],"(TS)":[81],"to":[82,94,111,125,135,139,199],"solve":[83],"problem.":[85],"construction":[88],"phase,":[89,106],"IH":[91],"used":[93],"create":[95],"initial":[97],"solution":[98,165],"that":[99,186],"good":[101],"enough.":[102],"improvement":[105],"VNS":[108,151],"employed":[110],"generate":[112],"diverse":[113],"various":[115],"neighborhoods,":[116],"while":[117],"main":[119],"attribute":[120],"prohibit":[126],"from":[129],"getting":[130],"trapped":[131],"into":[132],"cycles,":[133],"guide":[136],"escape":[140],"local":[141],"optima.":[142],"Moreover,":[143],"introduce":[145],"novel":[147],"neighborhoods\u2019":[148],"structure":[149],"in":[150,166,202,218],"present":[153],"O":[155],"(1)":[156],"operation":[157],"calculating":[159],"cost":[161],"each":[163],"neighboring":[164],"special":[168],"case":[169],"TDTSP":[171,174],"where":[172],"becomes":[175],"MLP.":[177],"Extensive":[178],"computational":[179],"experiments":[180],"on":[181],"355":[182],"benchmark":[183],"instances":[184,196],"show":[185],"can":[189],"find":[190],"optimal":[192],"solutions":[193,217],"small":[195],"with":[197,220],"up":[198],"100":[200],"vertices":[201],"reasonable":[204],"amount":[205],"time.":[207],"For":[208],"larger":[209],"instances,":[210],"obtains":[213],"new":[215],"comparison":[219],"state-of-the-art":[222],"solutions.":[224]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-07T06:14:10.401900","created_date":"2025-10-10T00:00:00"}