{"id":"W4402436572","doi":"10.1016/j.aei.2024.102804","title":"Optimal charging scheduling for Indoor Autonomous Vehicles in manufacturing operations","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scheduling (production processes); Automotive engineering; Computer science; Engineering; Real-time computing; Manufacturing engineering; Operations management","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009955908,0.0008380178,0.001873157,0.0005489335,0.000766826,0.001424161,0.001326344,0.000750147,0.004328311],"category_scores_gemma":[0.002416382,0.0007248127,0.0007418134,0.001098068,0.0007109234,0.0009937359,0.0007844719,0.0008703077,0.0003387399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001409333,"about_ca_system_score_gemma":0.001960534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01268522,"about_ca_topic_score_gemma":0.009995119,"domain_scores_codex":[0.999261,0.0002491676,0.00001689802,0.00009579208,0.00009709971,0.0002800169],"domain_scores_gemma":[0.9991307,0.0005216629,0.00007836471,0.00004923446,0.0001100658,0.0001100554],"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":[0.0001817731,0.0000587216,0.0003083587,0.000058081,0.00002509554,0.00004396401,0.00003601794,0.9775803,0.001019456,0.005420205,0.001182014,0.014086],"study_design_scores_gemma":[0.0000115746,0.00004162199,0.0002066445,0.000002882355,0.000007362246,0.000009295095,0.00003216108,0.9958086,0.0003028022,0.003300066,0.0002719383,0.000005121035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1854771,0.0006230932,0.8013849,0.0004551236,0.0002851968,0.0001576267,0.0002640022,0.0003589903,0.01099405],"genre_scores_gemma":[0.9699181,0.0001531484,0.0262786,0.0000366086,0.00004530394,0.00004129607,0.0001070244,0.00007039291,0.003349598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01268522,"threshold_uncertainty_score":0.02522272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007096202883981374,"score_gpt":0.2212059478007596,"score_spread":0.2141097449167782,"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."}}