{"id":"W913363275","doi":"10.1016/j.trc.2015.06.019","title":"An adaptive large neighborhood search heuristic for fleet deployment problems with voyage separation requirements","year":2015,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Norges Forskningsråd","keywords":"Heuristic; Computer science; Scheduling (production processes); Schedule; Computation; Mathematical optimization; Software deployment; Separation (statistics); Vehicle routing problem; Operations research; Routing (electronic design automation); Real-time computing; Algorithm; Artificial intelligence; Engineering; Mathematics; Machine learning; Computer network","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001515156,0.0007429438,0.001486691,0.001179797,0.0005771049,0.0006861963,0.002090623,0.00171753,0.002449993],"category_scores_gemma":[0.004139883,0.0007194356,0.0008133833,0.001009763,0.0006269782,0.001348568,0.001087047,0.0009123468,0.0002735096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009724707,"about_ca_system_score_gemma":0.001336519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007431929,"about_ca_topic_score_gemma":0.008674071,"domain_scores_codex":[0.9995428,0.0002165103,0.00002372429,0.00006256911,0.00009106565,0.00006338736],"domain_scores_gemma":[0.9977441,0.001740874,0.0001423228,0.00007790724,0.0001884006,0.0001064079],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001116438,0.00009174328,0.0002950295,0.00004854161,0.00003170572,0.00004150539,0.00003253985,0.968812,0.000473794,0.003062624,0.001082769,0.02591617],"study_design_scores_gemma":[0.00001705277,0.00002535425,0.00003660649,0.000004300908,0.000005442828,0.000005651345,0.000006785463,0.9989423,0.00005432563,0.0007441089,0.0001556017,0.000002506151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07068131,0.0009489777,0.9210683,0.0003901111,0.00018736,0.0002319966,0.0001175507,0.0004354489,0.005938973],"genre_scores_gemma":[0.5645525,0.000391598,0.4298928,0.0002432313,0.0000982807,0.0004870212,0.000278482,0.0001600301,0.003896062],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007431929,"threshold_uncertainty_score":0.0147773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1453248972075369,"score_gpt":0.4101551641455602,"score_spread":0.2648302669380234,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}