{"id":"W2062705997","doi":"10.1007/s00500-008-0337-5","title":"Using an enhanced scatter search algorithm for a resource-constrained project scheduling problem","year":2008,"lang":"en","type":"article","venue":"Soft Computing","topic":"Resource-Constrained Project Scheduling","field":"Decision Sciences","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Benchmark (surveying); Job shop scheduling; Computer science; Crossover; Mathematical optimization; Algorithm; Scheduling (production processes); Schedule; Computational complexity theory; Mathematics; Artificial intelligence","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.007999549,0.0005389038,0.0008470656,0.001047515,0.001868612,0.0006818376,0.001558667,0.0002839688,0.00005672935],"category_scores_gemma":[0.002160894,0.0004783075,0.0004111284,0.00205235,0.0006318945,0.0006591814,0.0004662751,0.0007085474,0.00004958337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001598993,"about_ca_system_score_gemma":0.001001268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008911913,"about_ca_topic_score_gemma":0.000005558623,"domain_scores_codex":[0.9923077,0.0007257782,0.00170457,0.001780811,0.001970168,0.001510924],"domain_scores_gemma":[0.9939245,0.003141491,0.0006174218,0.001021073,0.0009635146,0.0003320148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000119304,0.000276835,0.002840419,0.00006371739,0.000115529,0.00006933017,0.02292006,0.08767954,0.02829403,0.0001789445,0.000140233,0.8573021],"study_design_scores_gemma":[0.001509466,0.0002273872,0.0001380863,0.0001704135,0.00002427458,0.000449181,0.006291837,0.9800974,0.008815959,0.0008997784,0.0006884695,0.0006878093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4379901,0.00004414081,0.5592026,0.00009918374,0.0001340218,0.001080783,0.00001653307,0.0002330164,0.001199668],"genre_scores_gemma":[0.4837402,5.998334e-7,0.5152292,0.0002165357,0.0006034621,0.00001797142,0.00001328904,0.0000601795,0.000118603],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8924178,"threshold_uncertainty_score":0.9997669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2173452877011113,"score_gpt":0.4178145060016266,"score_spread":0.2004692183005153,"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."}}