{"id":"W1969628616","doi":"10.1111/itor.12071","title":"A multiobjective hybrid ant colony optimization approach applied to the assignment and scheduling problem","year":2014,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Resource-Constrained Project Scheduling","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Mathematical optimization; Computer science; Ant colony optimization algorithms; Scheduling (production processes); Job shop scheduling; Multi-objective optimization; Set (abstract data type); Convergence (economics); Ant colony; Pareto principle; Local search (optimization); Pareto optimal; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01048448,0.0001636717,0.0001966883,0.001042422,0.000705052,0.0009434637,0.000867629,0.00007656536,0.0004536218],"category_scores_gemma":[0.002510608,0.0001193571,0.00005821627,0.00113117,0.0002482903,0.0003843596,0.00008776577,0.000677671,0.00007195759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003551162,"about_ca_system_score_gemma":0.0002920432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001232913,"about_ca_topic_score_gemma":0.00009333847,"domain_scores_codex":[0.9944371,0.0006964196,0.0006881608,0.0007608049,0.003052928,0.0003645587],"domain_scores_gemma":[0.9952703,0.003080767,0.00007762824,0.0003270825,0.001103487,0.0001407997],"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":[0.0001134515,0.0001353921,0.0002449873,0.000002494985,0.00002625012,0.000001027757,0.001435358,0.9678515,0.0007106353,0.00931407,0.00006769952,0.02009719],"study_design_scores_gemma":[0.000662713,0.00006925842,0.0007970468,0.00002241424,0.000003374137,0.00003074146,0.002307665,0.9886553,0.0007798279,0.001985485,0.004536535,0.0001495874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01629192,0.0000153798,0.948652,0.008454829,0.0001724871,0.001149205,0.00003477155,0.00002305508,0.02520639],"genre_scores_gemma":[0.8124049,0.00001223518,0.1855998,0.000268357,0.0001662005,0.0006549976,0.000020011,0.00001525096,0.0008582752],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7961129,"threshold_uncertainty_score":0.9097842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1385527995478368,"score_gpt":0.433266879528203,"score_spread":0.2947140799803662,"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."}}