{"id":"W2097714267","doi":"10.1109/robot.1991.131794","title":"Resource allocation in a flexible manufacturing system by graph matching","year":2002,"lang":"en","type":"article","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Matching (statistics); Graph; Flexible manufacturing system; Resource allocation; Blossom algorithm; Distributed computing; Cardinality (data modeling); Theoretical computer science; Mathematical optimization; Data mining; Mathematics; Computer network; Scheduling (production processes)","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":[0.0006360991,0.0006350054,0.0007006877,0.001242838,0.0006338998,0.001242345,0.001108934,0.0009590727,0.00476028],"category_scores_gemma":[0.001641324,0.0003791832,0.0008433693,0.002576661,0.0007635589,0.001869447,0.001226498,0.0005568187,0.0008840144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001233729,"about_ca_system_score_gemma":0.001323585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005224721,"about_ca_topic_score_gemma":0.004394051,"domain_scores_codex":[0.9992884,0.000293041,0.00002934281,0.00014292,0.0001673672,0.00007885369],"domain_scores_gemma":[0.999727,0.0001340955,0.00003940525,0.00004811402,0.00003077371,0.00002058478],"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":[0.00009877072,0.0000448282,0.0002964457,0.0001517686,0.0000447273,0.0001386378,0.00008657566,0.7711267,0.003344532,0.09918389,0.004444179,0.121039],"study_design_scores_gemma":[0.00004049859,0.00003712641,0.0001492742,0.00002050018,0.00002084132,0.00006890916,0.00003250717,0.8693118,0.001531374,0.1187022,0.01007057,0.00001443262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008895108,0.000320978,0.9842628,0.0002591747,0.00003084854,0.0001037644,0.0001119232,0.000579706,0.005435692],"genre_scores_gemma":[0.2487211,0.001221513,0.7412474,0.000131236,0.00005748918,0.0003317256,0.0006275211,0.0002311447,0.007430818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005224721,"threshold_uncertainty_score":0.01592475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009402348641347362,"score_gpt":0.1897180229863262,"score_spread":0.1803156743449788,"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."}}