{"id":"W4413948252","doi":"10.1016/j.mfglet.2025.06.018","title":"Scheduling in Industry 4.0: A Digital Twin-based approach for scheduling and smart Material-Handling Considerations","year":2025,"lang":"en","type":"article","venue":"Manufacturing Letters","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Scheduling (production processes); Computer science; Smart manufacturing; Industry 4.0; Industrial engineering; Distributed computing; Manufacturing engineering; Engineering; Embedded system; Operations management","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"],"consensus_categories":[],"category_scores_codex":[0.0001314879,0.0002163301,0.0002162148,0.0004233121,0.000128078,0.0007996381,0.00008929286,0.0002256796,0.00001141964],"category_scores_gemma":[0.00004603775,0.0002582387,0.00005016988,0.000110293,0.00006337462,0.0005045743,0.00002331398,0.000433711,0.000002050516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001224835,"about_ca_system_score_gemma":0.00003365517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001242272,"about_ca_topic_score_gemma":0.000003321941,"domain_scores_codex":[0.9989269,0.000008550061,0.000383362,0.000240269,0.0001003092,0.0003405612],"domain_scores_gemma":[0.9995742,0.0001638147,0.00002992867,0.0001590943,0.00001332808,0.00005959502],"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":[0.00001228402,0.00001778356,0.001321352,0.0004692667,0.00004038118,0.00000380653,0.0001038853,0.9937164,0.002555052,0.0001445576,0.0001128136,0.0015024],"study_design_scores_gemma":[0.004243944,0.00002980007,0.003940865,0.001140745,0.00007474597,0.00002507826,0.001070776,0.5042825,0.4797916,0.00105374,0.002909713,0.001436538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8629378,0.00002120463,0.1328766,0.0005927482,0.0002762768,0.0003685027,0.00004981531,0.000275665,0.002601409],"genre_scores_gemma":[0.9850645,0.000001004673,0.01406877,0.0005690208,0.00006057581,0.000120763,0.00006129962,0.00003453863,0.00001952724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4894339,"threshold_uncertainty_score":0.999987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01501942161616857,"score_gpt":0.2187986469890256,"score_spread":0.2037792253728571,"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."}}