{"id":"W2004594161","doi":"10.1007/s11590-015-0859-6","title":"A note on the algorithm LPT-FF for a flowshop scheduling with two batch-processing machines","year":2015,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Job shop scheduling; Computer science; Scheduling (production processes); Computational intelligence; Mathematical optimization; Job scheduler; Algorithm; Mathematics; Artificial intelligence; Schedule","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":[0.001422101,0.001043643,0.000707614,0.0006410452,0.0009493803,0.001495584,0.001837742,0.001580045,0.01191842],"category_scores_gemma":[0.005544483,0.0003948364,0.001143357,0.001005921,0.001065822,0.002332826,0.001346431,0.0039494,0.004536746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009635846,"about_ca_system_score_gemma":0.001309574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003735936,"about_ca_topic_score_gemma":0.005804978,"domain_scores_codex":[0.9991661,0.0002455859,0.00008278433,0.0001686254,0.0002862453,0.00005064493],"domain_scores_gemma":[0.9987012,0.0006849981,0.00003711288,0.000290213,0.0002411344,0.0000453452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006590238,0.0001707913,0.0005961471,0.0006337244,0.0001174988,0.001020425,0.0001678185,0.09869913,0.01972635,0.1754601,0.08734953,0.6153995],"study_design_scores_gemma":[0.0001624135,0.0002597732,0.00101485,0.0001627264,0.00006326148,0.001186723,0.00005480999,0.5215091,0.01269504,0.2260976,0.236629,0.0001647632],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001787707,0.001243877,0.9815979,0.002391696,0.003222536,0.0001273468,0.0001244504,0.001085304,0.008419126],"genre_scores_gemma":[0.03339738,0.00117051,0.9475207,0.0009980765,0.001856923,0.0001791789,0.0002059587,0.0006629692,0.01400839],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01191842,"threshold_uncertainty_score":0.0398711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01641432805401855,"score_gpt":0.242009478236808,"score_spread":0.2255951501827894,"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."}}