{"id":"W3088468425","doi":"10.5539/mas.v14n10p6","title":"Energy Efficiency Improvement through Optimal Batch Sizing in Job Shop","year":2020,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of New South Wales","keywords":"Computer science; Production (economics); Sizing; Job shop; Sustainability; Tardiness; Personalization; Dilemma; Energy consumption; Environmental economics; Manufacturing engineering; Job shop scheduling; Flow shop scheduling; Economics","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":[0.000184877,0.0001452683,0.0001395088,0.00006965513,0.0001165402,0.0001008212,0.000424249,0.00004526381,0.00002236895],"category_scores_gemma":[0.00001613969,0.0001483302,0.00002283672,0.0009345199,0.0001127947,0.0001790891,0.0000962643,0.0001395354,0.00002072634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009300866,"about_ca_system_score_gemma":0.00005775908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001228816,"about_ca_topic_score_gemma":0.00000280584,"domain_scores_codex":[0.9985827,0.000003710872,0.0002212772,0.0003959602,0.000384431,0.0004118759],"domain_scores_gemma":[0.9996299,0.0000183822,0.00002198684,0.0001784404,0.00002653101,0.0001247617],"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":[0.000003254003,0.00001106547,0.00001619715,0.000007864445,0.000001480507,0.0000018561,0.001983702,0.8538138,0.1293078,0.0009838775,0.000009128033,0.01386007],"study_design_scores_gemma":[0.000248321,0.00001779989,0.00002259803,0.000005239826,0.000001798024,5.321303e-7,0.0002365121,0.9615849,0.03747759,0.0001516537,0.0000760396,0.0001769676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05691903,0.0001015251,0.9324453,0.0001673677,0.0001389328,0.00009091404,0.000001345467,0.0002703459,0.009865174],"genre_scores_gemma":[0.9261398,0.00002019548,0.0732945,0.0004269012,0.00005582925,0.00002542199,0.000001385827,0.00001901853,0.00001691107],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8692208,"threshold_uncertainty_score":0.6048729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0149677923292651,"score_gpt":0.2194414207692532,"score_spread":0.2044736284399881,"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."}}