{"id":"W4232359923","doi":"10.1002/aic.14444","title":"Inventory pinch based, multiscale models for integrated planning and scheduling‐part II: Gasoline blend scheduling","year":2014,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Process Optimization and Integration","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Scheduling (production processes); Mathematical optimization; Schedule; Computer science; Nonlinear system; Nonlinear programming; Production planning; Swing; Job shop scheduling; Pinch analysis; Integer programming; Linear programming; Operations research; Mathematics; Engineering; Process integration; Production (economics); Process engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004732045,0.0001756375,0.0001926977,0.0001651196,0.0002720586,0.0001155427,0.0001133305,0.000120135,0.0000395845],"category_scores_gemma":[0.0002083895,0.0001594708,0.00005618811,0.000121994,0.00002779216,0.0004814926,0.00001759688,0.0003960292,0.000002598459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000595814,"about_ca_system_score_gemma":0.00004977111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002430346,"about_ca_topic_score_gemma":0.000005362891,"domain_scores_codex":[0.9990821,0.00002904297,0.0003535485,0.0001435472,0.0001382414,0.0002535375],"domain_scores_gemma":[0.9993912,0.00005507779,0.00008915294,0.00009607993,0.0002067249,0.000161769],"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":[0.00002525664,0.00002012634,0.0002156171,0.00005270069,0.00002681909,6.070374e-7,0.0002366379,0.9940561,0.001301866,0.0003067408,0.0009864656,0.002771086],"study_design_scores_gemma":[0.0009926112,0.00008087233,0.00001402754,0.000236042,0.00002587384,0.0000239238,0.0001234667,0.9887615,0.003525131,0.0007355349,0.005295962,0.0001850413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07346045,0.001114997,0.9239752,0.0002032652,0.0003590618,0.0001064729,0.000005922913,0.0001359871,0.0006385791],"genre_scores_gemma":[0.8554454,0.0001317949,0.1434485,0.0003101805,0.0004108551,0.00001756664,0.00004364018,0.00004831027,0.0001437967],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7819849,"threshold_uncertainty_score":0.6503029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02261452417471495,"score_gpt":0.2486544316255306,"score_spread":0.2260399074508157,"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."}}