{"id":"W1565098934","doi":"10.1007/978-3-540-24749-4_16","title":"On Minimizing the Total Weighted Tardiness on a Single Machine","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Tardiness; Computer science; Scheduling (production processes); Bounded function; Mathematical optimization; Due date; Schedule; Set (abstract data type); Single-machine scheduling; Value (mathematics); Algorithm; Randomized algorithm; Job shop scheduling; Mathematics; Machine learning","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":[0.001330007,0.00228157,0.002532647,0.0009098332,0.0007155184,0.001155055,0.002223638,0.001288642,0.00490346],"category_scores_gemma":[0.002970682,0.0007774701,0.0009426699,0.002494462,0.001018199,0.002223958,0.001155145,0.001526256,0.0007656176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012801,"about_ca_system_score_gemma":0.001153534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002754904,"about_ca_topic_score_gemma":0.002609578,"domain_scores_codex":[0.9993635,0.0002340773,0.00003008983,0.0001099674,0.0001761963,0.0000861372],"domain_scores_gemma":[0.9984603,0.001103336,0.00009724831,0.0001080707,0.0001534656,0.00007762841],"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":[0.000407047,0.0001261833,0.0002736986,0.0004891305,0.0000761922,0.0001147232,0.00009303179,0.8908792,0.00671141,0.01907923,0.004993241,0.07675693],"study_design_scores_gemma":[0.00005018027,0.0002833887,0.0004145897,0.00003452479,0.00003132215,0.00006598022,0.00003897829,0.9464942,0.001703044,0.04924969,0.001613344,0.00002071777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05338467,0.00128563,0.9347036,0.0005429431,0.0004004504,0.000160293,0.0001931293,0.0003303878,0.008998939],"genre_scores_gemma":[0.4369548,0.004317657,0.5242444,0.0004038496,0.0009870947,0.0005113705,0.0006855256,0.001413477,0.03048192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00490346,"threshold_uncertainty_score":0.01640373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01060750677761114,"score_gpt":0.2072830579500217,"score_spread":0.1966755511724106,"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."}}