{"id":"W2073418197","doi":"10.1007/s10951-012-0277-x","title":"A branch and bound algorithm for the response time variability problem","year":2012,"lang":"en","type":"article","venue":"Journal of Scheduling","topic":"Assembly Line Balancing Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Branch and bound; Computer science; Mathematical optimization; Scheduling (production processes); Integer programming; Algorithm; Range (aeronautics); Linear programming; Upper and lower bounds; Branch and cut; Integer (computer science); Mathematics","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":[0.002532504,0.001754521,0.002754638,0.001684635,0.001254571,0.002250047,0.002448611,0.002596859,0.009658743],"category_scores_gemma":[0.007327345,0.001211554,0.001226831,0.002497358,0.001048146,0.002554684,0.002150118,0.004563375,0.001948872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001826607,"about_ca_system_score_gemma":0.00313968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00918505,"about_ca_topic_score_gemma":0.007263094,"domain_scores_codex":[0.9985058,0.0004413031,0.00006482089,0.0002623011,0.0004344033,0.0002914369],"domain_scores_gemma":[0.9952118,0.003873285,0.0001645934,0.0002036651,0.0003676555,0.0001790174],"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":[0.0004035156,0.0002974098,0.0003986518,0.0001356024,0.00008464797,0.00005608582,0.00008114279,0.7773547,0.002153327,0.01983275,0.0073224,0.1918799],"study_design_scores_gemma":[0.00005748949,0.00002854392,0.00005439529,0.000007128281,0.00001032444,0.000008907896,0.00000693696,0.9908771,0.0002236803,0.008146931,0.00057249,0.000006014711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005627695,0.0004007947,0.9896516,0.0003071148,0.00007845259,0.000071559,0.00009166598,0.0008611617,0.002909881],"genre_scores_gemma":[0.1438412,0.0004229174,0.8491628,0.0002456838,0.0001726506,0.0004347262,0.0005424297,0.0005566178,0.004621107],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009658743,"threshold_uncertainty_score":0.03231174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007735413333746032,"score_gpt":0.2306258595686345,"score_spread":0.2228904462348885,"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."}}