{"id":"W1984200231","doi":"10.1080/00207543.2013.827806","title":"Mathematical modelling and a meta-heuristic for flexible job shop scheduling","year":2013,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":110,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Heuristics; Job shop scheduling; Benchmark (surveying); Mathematical optimization; Integer programming; Computer science; Simulated annealing; Heuristic; Scheduling (production processes); Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001253707,0.00008340181,0.0001749438,0.0004009169,0.00007195819,0.0001893573,0.0001982117,0.00004712819,0.0002247108],"category_scores_gemma":[0.0006376629,0.00006837991,0.00008037761,0.0001414181,0.00005501667,0.0003451603,0.00002775192,0.0003129886,0.00002639185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006421534,"about_ca_system_score_gemma":0.00003580345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003378832,"about_ca_topic_score_gemma":1.593934e-7,"domain_scores_codex":[0.9986904,0.00003816153,0.0003727665,0.000121051,0.0006064837,0.0001711882],"domain_scores_gemma":[0.9975744,0.0001894589,0.00005577557,0.0000853943,0.002001727,0.00009326309],"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":[0.00002167998,0.00003317894,0.00001471602,0.0000503539,0.0006258479,0.000002289577,0.0002568792,0.9933156,0.0007050362,0.002103919,0.0008920679,0.001978461],"study_design_scores_gemma":[0.0002489771,0.00004019452,0.000009240997,0.0000281722,0.00008997781,0.0001250398,0.000324893,0.9767161,0.003676923,0.01830065,0.000361323,0.00007850646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06597225,0.0008355108,0.9281039,0.003492866,0.0008509078,0.0002611658,0.000002903285,0.00005759818,0.0004228743],"genre_scores_gemma":[0.5325727,0.0002529122,0.4649902,0.00001851743,0.0009888938,0.00005585745,0.000002261512,0.00003039493,0.00108819],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4666005,"threshold_uncertainty_score":0.2788452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1545567162866745,"score_gpt":0.3801708225125963,"score_spread":0.2256141062259218,"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."}}