{"id":"W4414865443","doi":"10.35784/iapgos.6996","title":"Development of a reinforcement learning-based adaptive scheduling algorithm for commercial smart kitchens","year":2025,"lang":"en","type":"article","venue":"Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska","topic":"Consumer Retail Behavior Studies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Reinforcement learning; Scheduling (production processes); Job shop scheduling; Replicate; Task (project management); Process (computing); Baseline (sea); Dynamic programming","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":[0.0006509632,0.0005406778,0.0006996472,0.000353331,0.0002951009,0.0004585001,0.001366629,0.0006619532,0.002688526],"category_scores_gemma":[0.001244436,0.0003398313,0.0004993384,0.0003133122,0.0003339881,0.0004716258,0.0006367265,0.0008033675,0.0005381728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007008497,"about_ca_system_score_gemma":0.001410531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009976353,"about_ca_topic_score_gemma":0.004855387,"domain_scores_codex":[0.9997386,0.00004832695,0.00001734765,0.00007424035,0.00007875698,0.00004262041],"domain_scores_gemma":[0.999567,0.0001669527,0.00004822309,0.000029382,0.0001465516,0.00004191541],"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":[0.00006726144,0.00006957745,0.0006808841,0.00004894433,0.00002386466,0.00005697385,0.00003954944,0.8955369,0.003717122,0.002185434,0.000697104,0.09687632],"study_design_scores_gemma":[0.000008112748,0.00001442543,0.00003991529,0.00000144501,0.000001904272,0.000005266706,0.000002913793,0.9991886,0.0003122858,0.0001928624,0.0002309605,0.000001453718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02156395,0.0001033594,0.9743252,0.00006592217,0.00004174779,0.0001083211,0.00002545769,0.001431846,0.002334156],"genre_scores_gemma":[0.5330608,0.0001028122,0.4633168,0.00008406673,0.00002418316,0.0002446804,0.0001157767,0.0001210277,0.002929779],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009976353,"threshold_uncertainty_score":0.0198366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0219589894526519,"score_gpt":0.2628361521678466,"score_spread":0.2408771627151947,"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."}}