{"id":"W2119172014","doi":"10.1109/cse.2009.401","title":"A Coarse-Grain Parallel Genetic Algorithm for Flexible Job-Shop Scheduling with Lot Streaming","year":2009,"lang":"en","type":"article","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Job shop scheduling; Scheduling (production processes); Flow shop scheduling; Parallel computing; Distributed computing; Mathematical optimization; Algorithm; Mathematics; Computer network","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":[0.0006879042,0.0004186021,0.0005745799,0.0004287325,0.0004958558,0.0004077241,0.000896349,0.0007199816,0.001244413],"category_scores_gemma":[0.00132408,0.0002965519,0.0004436509,0.0005566422,0.0005664679,0.0004743119,0.0004439665,0.0006299375,0.0001611191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007468606,"about_ca_system_score_gemma":0.001388139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007275896,"about_ca_topic_score_gemma":0.005997944,"domain_scores_codex":[0.9998198,0.0000552755,0.000007488742,0.00003299163,0.00006130782,0.00002316383],"domain_scores_gemma":[0.9997188,0.0001588497,0.00002287819,0.00002732359,0.00005151934,0.00002064205],"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":[0.00003257744,0.0000395562,0.0002516049,0.00001568524,0.00001245293,0.00003063554,0.00002972213,0.9600189,0.001687091,0.004623283,0.0003144669,0.03294409],"study_design_scores_gemma":[0.00001167701,0.0000129838,0.00003069743,7.226329e-7,0.000001891929,0.000004701341,0.000002201344,0.9983736,0.0001637306,0.001252944,0.0001428986,0.000001915862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0334941,0.00006754506,0.9642941,0.0001128589,0.00002670724,0.00009510511,0.0000275188,0.0004046019,0.001477577],"genre_scores_gemma":[0.4215185,0.0001023625,0.5759817,0.00007850925,0.00002154524,0.0002922419,0.0001119616,0.00006318529,0.001829882],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007275896,"threshold_uncertainty_score":0.01446712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01284988954664106,"score_gpt":0.2347401805119272,"score_spread":0.2218902909652862,"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."}}