{"id":"W2910537293","doi":"10.3166/jesa.51.323-332","title":"An improved bacterial foraging optimization for multi-objective flexible job-shop scheduling problem","year":2018,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Foraging; Computer science; Job shop scheduling; Scheduling (production processes); Mathematical optimization; Ecology; Mathematics; Biology; Embedded system","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":[0.001153296,0.0009645422,0.001691356,0.0008613261,0.0005843179,0.0009361749,0.001282455,0.00180084,0.002500515],"category_scores_gemma":[0.002590372,0.0004630386,0.001025997,0.0008704956,0.0004812014,0.0006907449,0.001126314,0.001059257,0.0003562056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006931201,"about_ca_system_score_gemma":0.001408437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006091967,"about_ca_topic_score_gemma":0.003287595,"domain_scores_codex":[0.9993781,0.0002390969,0.0000234549,0.00006422558,0.0002099851,0.00008510999],"domain_scores_gemma":[0.9994438,0.0002832611,0.00004065467,0.00004167231,0.0001413005,0.0000492389],"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":[0.0001044871,0.00007133006,0.0002877905,0.00008712173,0.00003608529,0.00005457541,0.00003075514,0.9663172,0.002613117,0.003085717,0.0008038152,0.02650803],"study_design_scores_gemma":[0.0000144628,0.00003178454,0.00006311588,0.000004146183,0.000006345418,0.000007065491,0.000003581388,0.9990703,0.0001399701,0.0004156186,0.0002409701,0.000002593462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1022058,0.00117186,0.8825468,0.0005838871,0.0003223819,0.0001632719,0.0001325777,0.0004280228,0.0124454],"genre_scores_gemma":[0.697673,0.0006371575,0.2952578,0.0001843107,0.0001244019,0.0003321008,0.0002221613,0.0001380551,0.005431032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006091967,"threshold_uncertainty_score":0.01211298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02304780594097234,"score_gpt":0.2743759657794912,"score_spread":0.2513281598385189,"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."}}