{"id":"W3197789086","doi":"","title":"Trade-off Balancing Between Maximum and Total Completion Times for No-Wait Flow Shop Production","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Heuristics; Mathematical optimization; Heuristic; Benchmark (surveying); Minification; Computer science; Scheduling (production processes); Flow shop scheduling; Mathematics; Parameterized complexity; Job shop scheduling; Algorithm; Routing (electronic design automation)","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":[0.002222774,0.001035836,0.001369865,0.0008793024,0.0005642175,0.0008134131,0.001316057,0.0007004914,0.001397454],"category_scores_gemma":[0.00394427,0.0006805288,0.0005425311,0.0007029994,0.0007669064,0.001094496,0.0005294235,0.0006540256,0.0002243798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006601646,"about_ca_system_score_gemma":0.001104061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002168662,"about_ca_topic_score_gemma":0.001826991,"domain_scores_codex":[0.9991091,0.0003313645,0.00003696474,0.0001745391,0.0002097341,0.0001383292],"domain_scores_gemma":[0.9980458,0.001194512,0.0003517737,0.00009134071,0.0001537994,0.0001626462],"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":[0.0002063276,0.0001094257,0.000668582,0.00009224164,0.0000373879,0.00004584794,0.00003821915,0.9714198,0.002463867,0.003379154,0.0005667945,0.02097238],"study_design_scores_gemma":[0.0000213348,0.0001060279,0.0002614482,0.000004968309,0.000007489984,0.00001414854,0.000008428897,0.9958439,0.0006439525,0.002925227,0.0001564695,0.000006561283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1102587,0.0003329675,0.8870518,0.0002000094,0.00006160903,0.000108064,0.00009137559,0.0002765204,0.001618958],"genre_scores_gemma":[0.8913603,0.0001021714,0.107072,0.00006836082,0.00003939143,0.0001176064,0.0001177801,0.00007026817,0.001052039],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002222774,"threshold_uncertainty_score":0.01175529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009466267643402136,"score_gpt":0.2084541031797133,"score_spread":0.1989878355363112,"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."}}