{"id":"W2026944273","doi":"10.1177/0037549704045046","title":"Using Simulation to Make Order Acceptance/Rejection Decisions","year":2004,"lang":"en","type":"article","venue":"SIMULATION","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Order (exchange); Computer science; Management science; Build to order; Rule-based system; Risk analysis (engineering); Industrial engineering; Operations research; Artificial intelligence; Engineering; Business","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":[0.002609235,0.0007842974,0.0009863253,0.0008712449,0.0006149811,0.001770974,0.001093724,0.0009852796,0.003988196],"category_scores_gemma":[0.008588603,0.0005289171,0.0006937198,0.0006920934,0.0008920255,0.001274246,0.000769694,0.001255785,0.0005338091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001132553,"about_ca_system_score_gemma":0.001585875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005875914,"about_ca_topic_score_gemma":0.004678604,"domain_scores_codex":[0.9978405,0.001312308,0.0001213683,0.000153614,0.0004525816,0.0001195339],"domain_scores_gemma":[0.9924206,0.006076945,0.0003489288,0.0004867013,0.000534268,0.0001325556],"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":[0.00006190487,0.00002797937,0.0006375948,0.00003032721,0.00002884403,0.00004362057,0.0000527418,0.9711654,0.000711798,0.0147054,0.0003926264,0.01214184],"study_design_scores_gemma":[0.00001896704,0.0000249946,0.00006851438,0.00001235349,0.00001023703,0.00001485781,0.00001439372,0.9845544,0.001107297,0.01250177,0.001659552,0.00001271847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03909092,0.0002798841,0.9440519,0.0004934208,0.00008680373,0.0001246173,0.0001031837,0.0008865166,0.01488279],"genre_scores_gemma":[0.7629706,0.0005709098,0.2326501,0.0001242516,0.00003933027,0.0001858877,0.0002139073,0.0001534117,0.0030916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005875914,"threshold_uncertainty_score":0.01379913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0384795019776422,"score_gpt":0.3034836208405523,"score_spread":0.2650041188629101,"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."}}