{"id":"W2033855616","doi":"10.1016/j.trb.2009.06.003","title":"Bidline scheduling with equity by heuristic dynamic constraint aggregation","year":2009,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"","keywords":"Heuristics; Rounding; Mathematical optimization; Heuristic; Computer science; Scheduling (production processes); Computation; Constraint (computer-aided design); Equity (law); Operations research; Mathematics; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005975264,0.000216531,0.0003431187,0.0001830189,0.0001935084,0.00007098314,0.0002291192,0.000209174,0.0002482265],"category_scores_gemma":[0.0008131352,0.0001834571,0.0000613686,0.0008094598,0.000246597,0.0001581201,0.00000585241,0.0007816625,0.00002213847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001093945,"about_ca_system_score_gemma":0.00005536031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007155196,"about_ca_topic_score_gemma":0.00001519204,"domain_scores_codex":[0.9965034,0.001214026,0.0005086172,0.000407611,0.0007512168,0.0006151731],"domain_scores_gemma":[0.9976544,0.001503755,0.00006065785,0.0002549293,0.0002953401,0.0002309023],"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":[0.0002552896,0.0001746986,0.001671933,0.000134846,0.00006740999,0.00007953946,0.0004275032,0.8203339,0.05470498,0.004695539,0.0005835966,0.1168708],"study_design_scores_gemma":[0.002387383,0.001417187,0.05230938,0.000320857,0.00009482516,0.00002427331,0.0005943078,0.8929426,0.035471,0.01079123,0.002481755,0.001165173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2482769,0.0001890639,0.7495286,0.0004657612,0.00005984988,0.0003538759,0.00003746404,0.0004870006,0.0006015046],"genre_scores_gemma":[0.5990738,0.0001345833,0.4004322,0.00005278356,0.00003370039,0.00002879273,0.0001740922,0.00002018934,0.00004988625],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3507969,"threshold_uncertainty_score":0.7481165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2351313843162557,"score_gpt":0.4631425726380631,"score_spread":0.2280111883218074,"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."}}