{"id":"W2068578546","doi":"10.1007/s00170-004-2301-6","title":"An optimal production run for an imperfect production process with allowable shortages and time-varying fraction defective rate","year":2004,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; King Fahd University of Petroleum and Minerals","keywords":"Production (economics); Imperfect; Convexity; Work (physics); Exponential distribution; Process (computing); Mathematical optimization; Mathematics; Economic shortage; Variable (mathematics); Function (biology); Fraction (chemistry); Exponential growth; Exponential function; Control theory (sociology); Statistics; Control (management); Computer science; Engineering; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051664,0.000158786,0.0001618878,0.0004842874,0.0002139214,0.0001458116,0.0003821657,0.00005348588,0.00001167347],"category_scores_gemma":[0.0001166021,0.0001131031,0.00003662079,0.00012056,0.0000950003,0.00271317,0.00005690611,0.0002108214,0.000002184136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001330478,"about_ca_system_score_gemma":0.00002368802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001747785,"about_ca_topic_score_gemma":0.00002086309,"domain_scores_codex":[0.9990653,0.000009653172,0.0002425364,0.000273778,0.0002328192,0.000175868],"domain_scores_gemma":[0.9989609,0.00001597563,0.000455536,0.0001695111,0.0003856853,0.00001238069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003168748,0.0004228471,0.000722766,0.0001173784,0.000404791,0.00002320322,0.0002950336,0.812874,0.1285874,0.002164528,0.000122695,0.05109662],"study_design_scores_gemma":[0.002441206,0.0009762731,0.001894305,0.0003024557,0.0002087139,0.0003152042,0.003118212,0.003485199,0.9303495,0.05225696,0.004197144,0.0004548521],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880266,0.00004299676,0.006859167,0.003797529,0.0005978475,0.000455989,6.050824e-7,0.0001136252,0.0001056717],"genre_scores_gemma":[0.9954464,0.0000365872,0.003223407,0.0002032175,0.0009726145,0.00004656456,0.000009533829,0.00002631659,0.00003533132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8093888,"threshold_uncertainty_score":0.4612209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009552437315591767,"score_gpt":0.2531645729187991,"score_spread":0.2436121356032073,"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."}}