{"id":"W2124761764","doi":"10.1287/ijoc.1080.0307","title":"A Constraint Programming Approach for Solving a Queueing Design and Control Problem","year":2009,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Science Foundation Ireland","keywords":"Queueing theory; Mathematical optimization; Computer science; Constraint (computer-aided design); Decomposition; Benders' decomposition; Constraint programming; Stochastic programming; Control (management); Variety (cybernetics); Operations research; Service (business); Engineering; Mathematics; Artificial intelligence; Computer network; Economics","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":[0.003675616,0.002278983,0.001647759,0.001169071,0.001118611,0.002624808,0.002453974,0.002268168,0.0061037],"category_scores_gemma":[0.006920054,0.001170495,0.001971954,0.002744145,0.001665614,0.001394192,0.001659889,0.003665004,0.0007247038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002788601,"about_ca_system_score_gemma":0.005580838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0182553,"about_ca_topic_score_gemma":0.01240099,"domain_scores_codex":[0.9975474,0.001032505,0.0001359587,0.000407254,0.0006348635,0.0002419724],"domain_scores_gemma":[0.9950913,0.004020139,0.0002105999,0.000115684,0.0004175485,0.0001446836],"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":[0.00004073359,0.00006969836,0.0001353063,0.0001810866,0.00005608924,0.0001005386,0.00006793631,0.9065371,0.0005293828,0.06358296,0.001869336,0.02682984],"study_design_scores_gemma":[0.00003737217,0.00003482493,0.00003528594,0.00002330342,0.00001854926,0.00002159502,0.00001934573,0.9711705,0.0002382057,0.0257529,0.002635559,0.00001260794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001005995,0.0001895113,0.9958994,0.000272018,0.00004016535,0.00009452086,0.00009938024,0.00007409813,0.002324953],"genre_scores_gemma":[0.08156649,0.0009981093,0.9117262,0.00032374,0.0001735564,0.001218341,0.0003810484,0.00008805491,0.003524362],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0182553,"threshold_uncertainty_score":0.0362981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0969777626073913,"score_gpt":0.3589317360212473,"score_spread":0.2619539734138561,"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."}}