{"id":"W2157120658","doi":"10.1016/j.ejor.2003.06.050","title":"A -approximation algorithm for the two-machine routing open-shop problem on a two-node network","year":2004,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; The Scarborough Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Russian Foundation for Basic Research","keywords":"Routing (electronic design automation); Interval (graph theory); Computer science; Node (physics); Job shop scheduling; Mathematical optimization; Approximation algorithm; Upper and lower bounds; Value (mathematics); Open shop; Mathematics; Algorithm; Flow shop scheduling; Combinatorics; Computer network","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":[0.001777395,0.00116596,0.002150467,0.0008209426,0.0009900305,0.00163922,0.003462124,0.002190569,0.006572576],"category_scores_gemma":[0.004348922,0.0007782135,0.0009779386,0.001494381,0.0008871948,0.002762727,0.002067641,0.002025936,0.0008487736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001818799,"about_ca_system_score_gemma":0.002820955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005417665,"about_ca_topic_score_gemma":0.006239195,"domain_scores_codex":[0.9991081,0.0002804046,0.00004321266,0.0001796639,0.0001739359,0.0002146519],"domain_scores_gemma":[0.9975932,0.001454412,0.0001710119,0.0003108188,0.0002384112,0.0002320889],"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":[0.0005789464,0.0002512423,0.0006079922,0.0002052699,0.00007208358,0.00009697964,0.00009599021,0.8862748,0.001523349,0.02272366,0.004510503,0.08305916],"study_design_scores_gemma":[0.00007762749,0.00004282175,0.0000663828,0.000007889804,0.000009855637,0.00001906264,0.00001925219,0.9899229,0.0001907555,0.009226223,0.0004127411,0.000004522195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05900478,0.0005849846,0.9313569,0.0007545194,0.0002053166,0.0002038119,0.000294319,0.001249844,0.006345444],"genre_scores_gemma":[0.3397142,0.0002638936,0.6553478,0.0001646196,0.00007757859,0.0003481782,0.0005274127,0.0001610478,0.003395246],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006572576,"threshold_uncertainty_score":0.02198744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06340619909870253,"score_gpt":0.3467518151207457,"score_spread":0.2833456160220431,"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."}}