{"id":"W1552477862","doi":"10.1109/icsmc.1988.754267","title":"An approximate reasoning approach for the implementation of an expert system in aggregate production planning","year":2005,"lang":"en","type":"article","venue":"","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Expert system; Schema (genetic algorithms); Computer science; Aggregate planning; Legal expert system; Rule of inference; Artificial intelligence; Inference; Fuzzy set; Inference engine; Fuzzy logic; Data mining; Machine learning; Production planning; Production (economics)","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":[0.003515899,0.0004803039,0.0006024357,0.0007089961,0.0004711007,0.002147176,0.002171236,0.001124205,0.005082621],"category_scores_gemma":[0.009817231,0.0004277087,0.0009398548,0.0010311,0.0008518858,0.00292197,0.001077212,0.001375804,0.001159408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006153783,"about_ca_system_score_gemma":0.001176676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002001757,"about_ca_topic_score_gemma":0.001820512,"domain_scores_codex":[0.9979552,0.0008203758,0.0003011707,0.0002257802,0.0006176034,0.00007987485],"domain_scores_gemma":[0.9968612,0.001539965,0.0002274099,0.0006360319,0.0006612326,0.0000741714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002779186,0.0002112498,0.00196085,0.0006125239,0.0002272109,0.0004760443,0.001494777,0.2142463,0.02153395,0.275971,0.004456525,0.4785317],"study_design_scores_gemma":[0.00005115249,0.0001803281,0.0003842497,0.000105251,0.000112666,0.000413039,0.0002254897,0.8888569,0.01292908,0.07300404,0.02369191,0.00004589357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001449115,0.00002603646,0.9971398,0.00006335873,0.000007902385,0.00004720316,0.00002644075,0.0005120109,0.0007281089],"genre_scores_gemma":[0.04034533,0.00007391113,0.9587407,0.0000450708,0.00001004626,0.0001350127,0.0000975199,0.00003335562,0.0005191187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005082621,"threshold_uncertainty_score":0.01859409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1659767111076429,"score_gpt":0.4719203702397078,"score_spread":0.3059436591320649,"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."}}