{"id":"W1983050889","doi":"10.1016/j.ejor.2013.08.034","title":"Improvements to a large neighborhood search heuristic for an integrated aircraft and passenger recovery problem","year":2013,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Schedule; Computer science; Heuristic; Operations research; Context (archaeology); Mathematical optimization; Incremental heuristic search; Order (exchange); Search algorithm; Beam search; Engineering; Artificial intelligence; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001327357,0.0008162561,0.00135237,0.0008444134,0.0007190442,0.0008597944,0.002058194,0.001528819,0.00466577],"category_scores_gemma":[0.004022452,0.0005720613,0.0009868433,0.0008178318,0.0005580899,0.001326035,0.001127732,0.001209701,0.0005389972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008524286,"about_ca_system_score_gemma":0.001514804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01018254,"about_ca_topic_score_gemma":0.01091512,"domain_scores_codex":[0.9994255,0.0002447356,0.0000270364,0.0000967991,0.000135834,0.00007003826],"domain_scores_gemma":[0.9984377,0.00100088,0.00009022884,0.000133765,0.000248492,0.00008888845],"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":[0.0001609357,0.0002091863,0.0003021873,0.00006112772,0.00003409526,0.00005904881,0.00004045763,0.9451988,0.001113237,0.005068563,0.001603765,0.04614862],"study_design_scores_gemma":[0.00002067941,0.00003311191,0.0000447817,0.000002829829,0.000006778477,0.000007111537,0.000006788905,0.9985659,0.0001206077,0.0009051797,0.0002831357,0.00000315692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08060577,0.0006346985,0.9077936,0.0003352457,0.0002608447,0.0002263251,0.0001389876,0.0006712824,0.009333252],"genre_scores_gemma":[0.4674866,0.0002485746,0.5251023,0.0001703793,0.0001003479,0.0003194718,0.0003295983,0.0002495568,0.005993265],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01018254,"threshold_uncertainty_score":0.02024657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05616401818405493,"score_gpt":0.3486139602267212,"score_spread":0.2924499420426663,"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."}}