{"id":"W1971378748","doi":"10.1111/j.1475-3995.2011.00814.x","title":"A nonlinear model for optimizing the performance of a multi-product production line","year":2011,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workstation; Throughput; Computer science; Blocking (statistics); Production line; Reduction (mathematics); Production (economics); Buffer (optical fiber); Line (geometry); Nonlinear system; Product (mathematics); Parallel computing; Mathematical optimization; Operating system; Computer network; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001000432,0.00133001,0.000847221,0.0005661112,0.0004902355,0.001654544,0.001838171,0.00216609,0.005812692],"category_scores_gemma":[0.002769892,0.0008827696,0.000787419,0.0009751809,0.0009381018,0.001699199,0.0007177312,0.001340135,0.0009726125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002251839,"about_ca_system_score_gemma":0.001730187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01201438,"about_ca_topic_score_gemma":0.007523309,"domain_scores_codex":[0.9993035,0.0002373724,0.00002490738,0.0001390139,0.0002080222,0.00008724635],"domain_scores_gemma":[0.9990991,0.000563777,0.0001309814,0.00003132083,0.0001352503,0.00003944741],"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":[0.00001595013,0.00001160283,0.00006997201,0.00002300848,0.000004033468,0.00002664824,0.00001187415,0.9959171,0.000424837,0.002635016,0.0001018608,0.0007581133],"study_design_scores_gemma":[0.000005490866,0.00001630361,0.00004534715,0.000002476457,0.000002541348,0.000004810747,0.000003980532,0.9987404,0.0001108809,0.0008652081,0.0001998419,0.000002807361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05113608,0.0004447997,0.9269182,0.0007974204,0.0000749498,0.0001799391,0.0005247914,0.0003375291,0.01958635],"genre_scores_gemma":[0.8754477,0.0008674071,0.08919539,0.000179034,0.0000682008,0.0008166265,0.0004188796,0.0001356572,0.03287103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01201438,"threshold_uncertainty_score":0.02388889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1583595830281928,"score_gpt":0.3711741929876319,"score_spread":0.212814609959439,"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."}}