{"id":"W3015682257","doi":"10.1002/nav.21901","title":"Efficient algorithms for flexible job shop scheduling with parallel machines","year":2020,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Job shop scheduling; Computer science; Scheduling (production processes); Schedule; Upper and lower bounds; Mathematical optimization; Algorithm; Flow shop scheduling; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008702821,0.0002619589,0.0003049915,0.0001856764,0.0003113958,0.0002037277,0.0004365702,0.0001627705,0.00006096331],"category_scores_gemma":[0.001655245,0.0002247379,0.00008018841,0.0008063423,0.0002038288,0.00004506954,0.0001023935,0.0007352559,0.0001163411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001139677,"about_ca_system_score_gemma":0.0001465257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002122404,"about_ca_topic_score_gemma":0.000005397231,"domain_scores_codex":[0.9974288,0.00006636359,0.0003306583,0.0004527495,0.0008817972,0.000839575],"domain_scores_gemma":[0.9980311,0.0005839201,0.00003263959,0.0003026033,0.0006067713,0.0004429855],"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":[0.000127556,0.00003645477,0.0001305831,0.0001830545,0.00004893692,0.00002153109,0.00025491,0.991729,0.0002251126,0.002385925,0.0005605415,0.004296344],"study_design_scores_gemma":[0.001024981,0.0003062809,0.00004540499,0.00003960913,0.00001883128,0.000005153976,0.0002342765,0.9959961,0.0006717317,0.0003037516,0.001063685,0.0002901885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005121766,0.0006910298,0.988597,0.0008852493,0.0002580415,0.0006098367,0.00007551636,0.0007176167,0.003043976],"genre_scores_gemma":[0.3512381,0.00008142359,0.6470777,0.0001172984,0.0007358426,0.0001374675,0.00006744317,0.0001185065,0.0004262025],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3461164,"threshold_uncertainty_score":0.9164544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1385095194211587,"score_gpt":0.3646678265404282,"score_spread":0.2261583071192695,"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."}}