{"id":"W2099051709","doi":"10.1002/cjce.21766","title":"Composite planning and scheduling algorithm addressing intra‐period infeasibilities of gasoline blend planning models","year":2012,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Process Optimization and Integration","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Time horizon; Computer science; Schedule; Scheduling (production processes); Genetic algorithm; Mathematical optimization; Gasoline; Job shop scheduling; Production planning; Composite number; Algorithm; Operations research; Production (economics); Engineering; Mathematics; Machine learning; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.001650598,0.000824607,0.001086402,0.0009988882,0.0008276484,0.001165347,0.001218637,0.001049306,0.003339157],"category_scores_gemma":[0.002386119,0.0006868139,0.0008442646,0.001042055,0.0008769454,0.000743637,0.001213354,0.001303992,0.0002078534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001545997,"about_ca_system_score_gemma":0.002817835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01350557,"about_ca_topic_score_gemma":0.01134488,"domain_scores_codex":[0.9995666,0.0001416894,0.00001808146,0.0000811559,0.0001191856,0.00007323117],"domain_scores_gemma":[0.9985219,0.0009607108,0.0001394263,0.00008050695,0.0001901585,0.0001073022],"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":[0.00004849882,0.00002022815,0.0001491877,0.00001181929,0.000008304199,0.0000184144,0.0000194221,0.9902596,0.0003006128,0.002529268,0.0001330089,0.006501549],"study_design_scores_gemma":[0.000006433483,0.00001259665,0.00002273085,0.000001120822,0.000002771171,0.000002145342,0.000002949953,0.9988033,0.0001496211,0.0008827559,0.0001120559,0.000001521094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07600469,0.00007390964,0.9173729,0.0001249026,0.00002788351,0.0001102554,0.00008258815,0.0004973977,0.005705524],"genre_scores_gemma":[0.6266317,0.00007096089,0.370085,0.00004416331,0.00002048603,0.0002516125,0.00019447,0.00009655974,0.002605064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01350557,"threshold_uncertainty_score":0.02685392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02451675398286904,"score_gpt":0.2315086087456683,"score_spread":0.2069918547627992,"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."}}