{"id":"W2044479004","doi":"10.1061/(asce)0733-9364(2003)129:6(698)","title":"Resource Optimization Using Combined Simulation and Genetic Algorithms","year":2003,"lang":"en","type":"article","venue":"Journal of Construction Engineering and Management","topic":"Resource-Constrained Project Scheduling","field":"Decision Sciences","cited_by":132,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Scheduling (production processes); Genetic algorithm; Software; Set (abstract data type); Mathematical optimization; Resource (disambiguation); Simulation modeling; Simulation software; Industrial engineering; Engineering; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009303987,0.0001019904,0.0001824316,0.00051505,0.00008564014,0.0001786345,0.00007708313,0.000042108,0.0000172759],"category_scores_gemma":[0.0003654141,0.00008849295,0.00004021825,0.0003247983,0.000048158,0.0002064126,0.00002764184,0.0001006211,3.794644e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000287499,"about_ca_system_score_gemma":0.00001256388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.058299e-7,"about_ca_topic_score_gemma":5.374521e-8,"domain_scores_codex":[0.9986598,0.00007252105,0.000558821,0.0001518322,0.0004407572,0.0001163054],"domain_scores_gemma":[0.999162,0.0001855931,0.0003161977,0.0001112955,0.0001396141,0.00008533857],"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":[0.00001008643,0.000005137109,0.0007779078,0.00001208732,0.00003098803,0.00001219035,0.00008169018,0.9719734,0.00005485505,0.001112917,0.000005077553,0.02592372],"study_design_scores_gemma":[0.0006481069,0.00004936331,0.001184587,0.00004615706,0.00004659368,0.0005583448,0.0006328835,0.9925719,0.00004793208,0.00044299,0.003671282,0.0000998387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2516102,0.0001680916,0.7475903,0.00004804223,0.0002228476,0.00008750956,3.459393e-7,0.00001116385,0.0002614882],"genre_scores_gemma":[0.5897508,0.00005782097,0.4101051,0.00001387187,0.00003999495,5.527329e-7,1.442226e-7,0.000007521536,0.00002420736],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3381406,"threshold_uncertainty_score":0.3608638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0389053613313472,"score_gpt":0.3042320755583036,"score_spread":0.2653267142269564,"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."}}