{"id":"W4409787760","doi":"10.61091/jcmcc127a-512","title":"Simulated annealing algorithm-based scheduling optimization of lean manufacturing line and evaluation of the calibration effect of electrical measuring instruments","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Simulated annealing; Calibration; Computer science; Scheduling (production processes); Lean manufacturing; Algorithm; Industrial engineering; Manufacturing engineering; Engineering; Mathematics; Operations management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001833646,0.0001814253,0.0005469177,0.0002411147,0.0001019857,0.00004049264,0.0001607382,0.0001325453,0.000001521392],"category_scores_gemma":[0.0003887791,0.000145582,0.00009226437,0.0002886259,0.00004381338,0.0001363453,0.00006321327,0.0002388753,7.65578e-9],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007194732,"about_ca_system_score_gemma":0.00008449911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006399368,"about_ca_topic_score_gemma":1.12415e-7,"domain_scores_codex":[0.9980196,0.0001183257,0.001024487,0.0001078986,0.0005952366,0.0001344043],"domain_scores_gemma":[0.9981537,0.000379897,0.0008253003,0.0001285868,0.00047148,0.00004098636],"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":[0.0000583388,0.00006414759,0.000270039,0.0009596662,0.00009680681,1.624213e-7,0.0001704759,0.9874222,0.0007421458,0.001548785,6.091938e-7,0.008666572],"study_design_scores_gemma":[0.002856897,0.0001871804,0.00008152813,0.0008314534,0.0002077793,0.000001531489,0.00001748185,0.7587171,0.2305821,0.006438502,5.353131e-7,0.00007792433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8780916,0.000273837,0.119435,0.000007600034,0.001851255,0.0002903897,0.000001083898,0.000015399,0.00003384956],"genre_scores_gemma":[0.9929418,0.00002477671,0.006917881,0.000001475453,0.00009304142,8.51363e-7,0.000001795663,0.00001811683,2.718393e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.22984,"threshold_uncertainty_score":0.5936661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01060779014921749,"score_gpt":0.2420959068308305,"score_spread":0.231488116681613,"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."}}