{"id":"https://openalex.org/W7161557405","doi":"https://doi.org/10.31449/inf.v50i13.13706","title":"Multi-Objective Logistics Route Optimization Using a Physics- Informed Neural Network-Assisted Genetic Algorithm","display_name":"Multi-Objective Logistics Route Optimization Using a Physics- Informed Neural Network-Assisted Genetic Algorithm","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161557405","doi":"https://doi.org/10.31449/inf.v50i13.13706"},"language":null,"primary_location":{"id":"doi:10.31449/inf.v50i13.13706","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i13.13706","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/13706/6725","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/13706/6725","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111134392","display_name":"Qianfei Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110925","display_name":"Jiaozuo University","ror":"https://ror.org/024nbxn35","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210110925"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qianfei Liu","raw_affiliation_strings":["Jiaozuo University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiaozuo University","institution_ids":["https://openalex.org/I4210110925"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5136450777","display_name":"Nan Lv","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nan Lv","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"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":0.57644893,"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/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.8708000183105469,"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":0.8708000183105469,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.025100000202655792,"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/T10524","display_name":"Traffic control and management","score":0.009499999694526196,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/vehicle-routing-problem","display_name":"Vehicle routing problem","score":0.5619999766349792},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.5508999824523926},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5404999852180481},{"id":"https://openalex.org/keywords/fitness-function","display_name":"Fitness function","score":0.5273000001907349},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5113000273704529},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.4756999909877777},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.47530001401901245},{"id":"https://openalex.org/keywords/crossover","display_name":"Crossover","score":0.46549999713897705},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.4041999876499176}],"concepts":[{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5921000242233276},{"id":"https://openalex.org/C123784306","wikidata":"https://www.wikidata.org/wiki/Q944041","display_name":"Vehicle routing problem","level":3,"score":0.5619999766349792},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5572999715805054},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.5508999824523926},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5404999852180481},{"id":"https://openalex.org/C176066374","wikidata":"https://www.wikidata.org/wiki/Q629118","display_name":"Fitness function","level":3,"score":0.5273000001907349},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5113000273704529},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.4756999909877777},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.47530001401901245},{"id":"https://openalex.org/C122507166","wikidata":"https://www.wikidata.org/wiki/Q628906","display_name":"Crossover","level":2,"score":0.46549999713897705},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.4041999876499176},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.37560001015663147},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3709000051021576},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.34950000047683716},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.29319998621940613},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C501734568","wikidata":"https://www.wikidata.org/wiki/Q42918","display_name":"Mutation","level":3,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.31449/inf.v50i13.13706","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i13.13706","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/13706/6725","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.v50i13.13706","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i13.13706","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/13706/6725","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/7","display_name":"Affordable and clean energy","score":0.9056875705718994}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7161557405.pdf","grobid_xml":"https://content.openalex.org/works/W7161557405.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modern":[0],"logistics":[1,170,256],"distribution":[2,171,257],"systems":[3],"require":[4],"simultaneous":[5],"optimization":[6],"of":[7],"transportation":[8,106],"cost,":[9,107],"delivery":[10,222],"time,":[11,110],"travel":[12,109,215],"distance,":[13,112,216],"and":[14,20,34,42,85,113,129,136,151,161,182,194,217,246,251],"energy":[15,114,209],"consumption":[16],"under":[17],"vehicle":[18,49,79],"capacity":[19],"time-window":[21,87],"constraints.":[22],"Conventional":[23],"genetic":[24],"algorithm":[25],"(GA)-based":[26],"routing":[27,50],"approaches":[28],"rely":[29],"on":[30,168],"penalty-based":[31,195],"constraint":[32],"handling":[33],"static":[35],"fitness":[36,97,143],"evaluation,":[37],"resulting":[38],"in":[39,203,208,213,220],"unstable":[40],"convergence":[41,204],"reduced":[43],"scalability":[44],"for":[45,63,254],"high-":[46],"dimensional,":[47],"multi-objective":[48,196],"problems":[51],"(VRPs).":[52],"This":[53],"study":[54],"proposes":[55],"a":[56,90,102,121,200],"Physics-Informed":[57],"Neural":[58],"Network":[59],"Assisted":[60],"Genetic":[61],"Algorithm":[62],"Logistics":[64],"Route":[65],"Optimization":[66],"(PINN-GA-RouteOpt),":[67],"integrating":[68],"physics-regularized":[69],"learning":[70],"with":[71,148,173],"adaptive":[72,149],"evolutionary":[73],"search.":[74],"The":[75,116,236],"proposed":[76],"framework":[77,253],"embeds":[78],"motion":[80,135],"dynamics,":[81],"fuel\u2013distance":[82],"nonlinear":[83],"relationships,":[84],"service":[86],"constraints":[88],"into":[89],"physics-informed":[91],"neural":[92],"network":[93,174],"(PINN),":[94],"where":[95],"the":[96],"function":[98,124],"is":[99,118,155],"formulated":[100],"as":[101],"composite":[103],"objective":[104],"minimizing":[105],"total":[108,214],"route":[111,258],"consumption.":[115],"PINN":[117],"trained":[119],"using":[120],"hybrid":[122],"loss":[123,128,132],"combining":[125],"supervised":[126],"data":[127],"physics":[130],"residual":[131],"derived":[133],"from":[134,177],"fuel-consumption":[137],"equations,":[138],"thereby":[139],"generating":[140],"constraint-":[141],"consistent":[142],"landscapes.":[144],"An":[145],"improved":[146,233],"GA":[147,193,197],"mutation":[150],"dynamic":[152],"crossover":[153],"control":[154],"employed":[156],"to":[157,179,186],"enhance":[158],"exploration\u2013exploitation":[159],"balance":[160],"accelerate":[162],"convergence.":[163],"Computational":[164],"experiments":[165],"were":[166],"conducted":[167],"benchmark":[169],"datasets":[172],"sizes":[175,184],"ranging":[176],"100":[178],"500":[180],"nodes":[181],"fleet":[183],"up":[185],"200":[187],"vehicles.":[188],"Comparative":[189],"evaluation":[190],"against":[191],"standard":[192],"variants":[198],"demonstrates":[199],"22%":[201],"reduction":[202,212],"iterations,":[205],"17.1%":[206],"decrease":[207],"consumption,":[210],"12.8%":[211],"15.4%":[218],"improvement":[219],"average":[221],"time.":[223],"Additionally,":[224],"Pareto":[225],"front":[226],"dispersion":[227],"variance":[228],"decreased":[229],"by":[230],"19.6%,":[231],"indicating":[232],"solution":[234],"stability.":[235],"results":[237],"confirm":[238],"that":[239],"PINN-GA-RouteOpt":[240],"achieves":[241],"computational":[242],"efficiency,":[243],"constraint-aware":[244],"optimization,":[245],"scalability,":[247],"establishing":[248],"an":[249],"intelligent":[250],"energy-efficient":[252],"large-scale":[255],"planning.":[259]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-05-19T00:00:00"}
