{"id":"W4400222770","doi":"10.1007/s40747-024-01517-w","title":"A novel fuzzy finite-horizon economic lot and delivery scheduling model with sequence-dependent setups","year":2024,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Supply chain; Computer science; Fuzzy logic; Mathematical optimization; Scheduling (production processes); Operations research; Time horizon; Synchronization (alternating current); Holding cost; Job shop scheduling; Computational intelligence; Sequence (biology); Supply chain management; Fuzzy set; Industrial engineering; Engineering; Business; Mathematics; Schedule; Artificial intelligence","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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005098199,0.0003905536,0.000372589,0.0004641325,0.0002168815,0.001314949,0.0003567887,0.00008833925,0.0001713582],"category_scores_gemma":[0.00001481044,0.0003385953,0.0001001368,0.0002179568,0.0001000207,0.0008903032,0.0003077657,0.0002201342,0.0009347841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002572059,"about_ca_system_score_gemma":0.00005982152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001398421,"about_ca_topic_score_gemma":0.0002566215,"domain_scores_codex":[0.9979246,0.00001537232,0.0005452423,0.0007297212,0.0003259916,0.0004591295],"domain_scores_gemma":[0.9992494,0.0000728045,0.0001552965,0.000390871,0.00008396165,0.00004767323],"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":[0.0001389816,0.0001615505,0.001244914,0.003535019,0.0005614469,0.0001167289,0.0004446596,0.6785795,0.002946508,0.2968389,0.008755224,0.006676552],"study_design_scores_gemma":[0.0002521324,0.00004437527,0.00002267328,0.0004866252,0.00009921705,0.0000178079,0.001450999,0.9301085,0.00004449064,0.0007429906,0.06628714,0.0004430673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4989902,0.006075549,0.4130825,0.001316347,0.006934733,0.003532745,0.0001388018,0.001883471,0.06804568],"genre_scores_gemma":[0.9959824,0.00008547933,0.0004136292,0.0004035956,0.001265385,0.0001025194,0.00009214493,0.00008221309,0.001572611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4969922,"threshold_uncertainty_score":0.9999066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0950901874424396,"score_gpt":0.2595464790792956,"score_spread":0.164456291636856,"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."}}