{"id":"W2081401598","doi":"10.1007/s10951-009-0109-9","title":"Approximation algorithms for minimizing the total weighted number of late jobs with late deliveries in two-level supply chains","year":2009,"lang":"en","type":"article","venue":"Journal of Scheduling","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Schedule; Job scheduler; Mathematical optimization; Scheduling (production processes); Due date; Supply chain; Job shop scheduling; Batch processing; Batch production; Time complexity; Supply chain management; Parametric statistics; Algorithm; Mathematics; Operations management; Economics; Statistics; Queue; Business","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":[0.0005164652,0.0001655323,0.0003126301,0.0001590438,0.00007229133,0.00005458857,0.0001536987,0.00006486398,0.00001233919],"category_scores_gemma":[0.00005615146,0.0001181942,0.00009871731,0.0003305437,0.00003485498,0.000341383,0.000009086583,0.0002904273,0.000001254308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000570097,"about_ca_system_score_gemma":0.00004789089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005790342,"about_ca_topic_score_gemma":0.000004099817,"domain_scores_codex":[0.9987704,0.00002750218,0.0006117618,0.0001007447,0.0002575702,0.0002319884],"domain_scores_gemma":[0.9991533,0.0001181814,0.0002735648,0.0001096668,0.0002826313,0.00006268357],"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":[0.000102763,0.00003997366,0.0009064198,0.00003589928,0.00006788569,0.000007380976,0.00164859,0.985731,0.001596476,0.000235204,0.000004214501,0.009624173],"study_design_scores_gemma":[0.001836684,0.00009145685,0.004236077,0.0002837456,0.00004550356,0.00008465279,0.000678655,0.9866574,0.005628298,0.0002866971,0.000007685422,0.0001630958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6849781,0.0002146867,0.3140718,0.000283425,0.0001754301,0.0001622211,0.000007098157,0.000026782,0.00008046869],"genre_scores_gemma":[0.5350656,0.00005994195,0.4646715,0.00002638355,0.0001262324,0.000003927397,0.000003719188,0.00001764949,0.00002508134],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1505997,"threshold_uncertainty_score":0.481982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01905853976061846,"score_gpt":0.2621603572587304,"score_spread":0.243101817498112,"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."}}