{"id":"W2002213774","doi":"10.1007/s00170-006-0444-3","title":"Optimal production-inventory policy for make-to-order versus make-to-stock based on the M/Er/1 queuing model","year":2006,"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":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Erlang (programming language); Stock (firearms); Computer science; Queueing theory; Poisson distribution; Product (mathematics); Build to order; Operations research; Poisson process; Mathematical optimization; Production (economics); Engineering; Mathematics; Economics; Microeconomics; Statistics","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":[0.003157787,0.001032635,0.002685127,0.0007789154,0.001042554,0.002665401,0.002429276,0.001739621,0.006408559],"category_scores_gemma":[0.00415524,0.0009750173,0.001018474,0.000993893,0.00109176,0.002196454,0.001005722,0.002034714,0.0005068818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003636904,"about_ca_system_score_gemma":0.004523246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01677319,"about_ca_topic_score_gemma":0.01156833,"domain_scores_codex":[0.9983332,0.0005579656,0.00006742527,0.0002844387,0.0002095788,0.0005473667],"domain_scores_gemma":[0.9971519,0.001623032,0.0003792827,0.0001353533,0.0003901308,0.0003202745],"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":[0.0002686175,0.00008645775,0.000264647,0.00005694325,0.00002475797,0.00006940129,0.00003361944,0.9703554,0.001314868,0.02159696,0.001098366,0.004829901],"study_design_scores_gemma":[0.00001842238,0.00002316275,0.00007223977,0.000002777355,0.000008306744,0.000004293774,0.000006935675,0.9967692,0.0001445758,0.002866256,0.00007619263,0.000007690377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1452822,0.0009385276,0.8349999,0.001964845,0.0003906333,0.0002328042,0.0003979946,0.0007772265,0.01501591],"genre_scores_gemma":[0.9522325,0.000378104,0.03913424,0.000220272,0.0001236202,0.000076573,0.0001365968,0.0001029681,0.00759511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01677319,"threshold_uncertainty_score":0.03335112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02010464516394365,"score_gpt":0.2640140097774618,"score_spread":0.2439093646135181,"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."}}