{"id":"W2567598518","doi":"10.1016/j.cor.2019.05.008","title":"An RLT approach for solving the binary-constrained mixed linear complementarity problem","year":2019,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Mixed complementarity problem; Complementarity theory; Linear complementarity problem; Complementarity (molecular biology); Linear programming; Mathematical optimization; Binary number; Mathematics; Generalized linear mixed model; Applied mathematics; Computer science; Nonlinear system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00262993,0.0001529198,0.0001827027,0.0001974192,0.0007143313,0.0003561417,0.0006148443,0.00007438302,0.00006509347],"category_scores_gemma":[0.00005530845,0.0001301145,0.00005990369,0.0005571485,0.0001159988,0.0002847052,0.0001284397,0.0004412332,0.00002623597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000129335,"about_ca_system_score_gemma":0.000104319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005974934,"about_ca_topic_score_gemma":0.00004397332,"domain_scores_codex":[0.9981263,0.0003636495,0.0003179937,0.0003378544,0.0003506836,0.0005035686],"domain_scores_gemma":[0.9984988,0.0004037025,0.00001277968,0.0005828849,0.0003939338,0.0001079124],"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":[0.00000620336,0.00006332823,0.000351014,0.00006758024,0.00003510154,2.976377e-7,0.0006522795,0.9873452,0.005516652,0.002166185,0.001180598,0.002615558],"study_design_scores_gemma":[0.0005341894,0.0001274916,0.0003369837,0.00001632344,0.000005411722,0.000003597521,0.0004911335,0.9964544,0.00089412,0.00003720567,0.0009444342,0.0001546937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08680813,0.00003332175,0.9097365,0.0003874051,0.0002069072,0.001891786,0.00002815258,0.0002410659,0.000666739],"genre_scores_gemma":[0.4427179,0.000004313892,0.5566691,0.0000365276,0.0001156795,0.0001535346,0.0001872552,0.00003292727,0.00008283665],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3559097,"threshold_uncertainty_score":0.5494131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08351850770169487,"score_gpt":0.3778910483446238,"score_spread":0.2943725406429289,"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."}}