{"id":"W4409787714","doi":"10.61091/jcmcc127a-511","title":"Design of dynamic scheduling strategy for metering equipment in warehouse environment based on intelligent algorithm","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Industrial Automation and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metering mode; Warehouse; Computer science; Scheduling (production processes); Real-time computing; Algorithm; Engineering; Operations management; Mechanical engineering; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004432635,0.0008575362,0.0009133157,0.0007990348,0.0009500413,0.001177199,0.001392412,0.000656432,0.002061327],"category_scores_gemma":[0.0007367181,0.0004395364,0.0006339399,0.0006473137,0.0003899176,0.001265094,0.0006856269,0.0004599199,0.0003166249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009226908,"about_ca_system_score_gemma":0.00145995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00712156,"about_ca_topic_score_gemma":0.003855561,"domain_scores_codex":[0.9995319,0.00006837778,0.00003423748,0.0001422291,0.0001185647,0.0001048267],"domain_scores_gemma":[0.9997627,0.00005651212,0.0000377107,0.00001889891,0.00009524155,0.00002901907],"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":[0.0001782496,0.0001618002,0.002618285,0.0001586297,0.00006803645,0.000166133,0.000231742,0.8077612,0.01436546,0.01647072,0.002616763,0.155203],"study_design_scores_gemma":[0.00001963651,0.00004647188,0.0002049181,0.000004328672,0.00001228708,0.00002172699,0.00002629508,0.9964497,0.001023665,0.001346598,0.000835477,0.000008974924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02574144,0.0002118865,0.9683082,0.0001205329,0.00005667174,0.00008857967,0.00002490324,0.0006211256,0.004826585],"genre_scores_gemma":[0.8102127,0.0003487372,0.1851761,0.00009112019,0.00004285209,0.0002270547,0.0001249236,0.00006471077,0.003711688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00712156,"threshold_uncertainty_score":0.01416022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02206830114205567,"score_gpt":0.2527060872438948,"score_spread":0.2306377861018391,"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."}}