{"id":"W578809198","doi":"10.15807/jorsj.49.174","title":"BATCH SCHEDULING IN CUSTOMER-CENTRIC SUPPLY CHAINS(<Special Issue>Advanced Planning and Scheduling for Supply Chain Management)","year":2006,"lang":"en","type":"article","venue":"Journal of the Operations Research Society of Japan","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Supply chain; Computer science; Scheduling (production processes); Mathematical optimization; Supply chain management; Flow shop scheduling; Dynamic priority scheduling; Job shop scheduling; Fair-share scheduling; Operations research; Schedule; Mathematics; Business; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001120781,0.0007731822,0.0009799168,0.0005177243,0.0008376405,0.001270743,0.001028597,0.0008668797,0.002470075],"category_scores_gemma":[0.001192937,0.0006694003,0.0004751923,0.002239204,0.0006739111,0.001528616,0.0005846621,0.0009294429,0.0003932334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001470097,"about_ca_system_score_gemma":0.001132874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004753896,"about_ca_topic_score_gemma":0.00451575,"domain_scores_codex":[0.9993691,0.0001881004,0.00003005269,0.0001458378,0.0001641966,0.0001027503],"domain_scores_gemma":[0.9992208,0.0004620374,0.0001126541,0.00004156887,0.0000914111,0.0000714619],"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":[0.0002150689,0.000105121,0.0006131093,0.0002207651,0.00004254322,0.0001904136,0.00008181353,0.9151444,0.006887314,0.03079792,0.003592287,0.0421092],"study_design_scores_gemma":[0.00002869582,0.00008299239,0.0003215114,0.00001012053,0.00001505451,0.00004383237,0.00001961418,0.9708645,0.001912922,0.02411954,0.002568954,0.0000123284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1019233,0.001913645,0.889218,0.0006554572,0.0002130612,0.0001174559,0.0002820436,0.0003108861,0.005366093],"genre_scores_gemma":[0.6994203,0.003282346,0.2851962,0.0001802252,0.000391029,0.0001705122,0.0006522407,0.0001595578,0.01054746],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004753896,"threshold_uncertainty_score":0.01066637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01843656744289311,"score_gpt":0.2991157838919881,"score_spread":0.2806792164490949,"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."}}