{"id":"W2764221425","doi":"10.5267/j.ijiec.2017.8.005","title":"A two-agent scheduling problem in a two-machine flowshop","year":2017,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Job shop scheduling; Scheduling (production processes); Mathematical optimization; Computer science; Mathematics; Schedule","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00142331,0.001219161,0.001480722,0.0007491315,0.001565095,0.001732519,0.001399537,0.002454095,0.005706416],"category_scores_gemma":[0.002129206,0.0005793971,0.001020405,0.001183947,0.0008598127,0.001755941,0.001152733,0.00141997,0.0004345986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001244775,"about_ca_system_score_gemma":0.001707587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00531341,"about_ca_topic_score_gemma":0.003578082,"domain_scores_codex":[0.9990242,0.0003638457,0.00005650086,0.0002804134,0.0001214927,0.0001536935],"domain_scores_gemma":[0.9990104,0.0006359322,0.0001035618,0.00005015756,0.00007219163,0.000127608],"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":[0.0002189305,0.0002077031,0.0005561795,0.0002480063,0.00007758633,0.0006997629,0.0001449003,0.9491385,0.002530185,0.02662758,0.001851719,0.01769903],"study_design_scores_gemma":[0.00007981415,0.0001127751,0.0002866802,0.00001426024,0.00002612463,0.0001224922,0.0001118429,0.9760039,0.0008054669,0.01921094,0.003206385,0.00001933137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1514606,0.0006602158,0.8328701,0.001270468,0.0003264984,0.0005925353,0.0006144294,0.0003415483,0.01186374],"genre_scores_gemma":[0.6360043,0.0006126235,0.3495707,0.0002302426,0.0001779361,0.0006105457,0.0006223715,0.0001082721,0.01206295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005706416,"threshold_uncertainty_score":0.01908994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02888169455221849,"score_gpt":0.2932678206633897,"score_spread":0.2643861261111712,"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."}}