{"id":"W2071299513","doi":"10.1080/03052150701551461","title":"Coupling ant colony optimization and the extended great deluge algorithm for the discrete facility layout problem","year":2007,"lang":"en","type":"article","venue":"Engineering Optimization","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ant colony optimization algorithms; Tabu search; Mathematical optimization; Metaheuristic; Heuristics; Computer science; Simulated annealing; Quadratic assignment problem; Algorithm; Genetic algorithm; Combinatorial optimization; Parallel metaheuristic; Ant colony; Meta-optimization; Mathematics","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":[0.0006602986,0.0002523261,0.0002058567,0.00008024347,0.0002954182,0.0001091838,0.0001413413,0.0001146566,0.000008137623],"category_scores_gemma":[0.0001487115,0.0001769306,0.00005716667,0.0001989962,0.00008290954,0.0002017963,0.00003527816,0.0001886802,7.293702e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009823598,"about_ca_system_score_gemma":0.000009685481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009433596,"about_ca_topic_score_gemma":0.000003290509,"domain_scores_codex":[0.9988941,0.000009874269,0.0003524401,0.0002391561,0.0001589547,0.0003455077],"domain_scores_gemma":[0.9990019,0.0005089419,0.00007043546,0.0002474195,0.0001056282,0.00006574195],"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":[0.00002774747,0.00000532529,0.000004563509,0.00004799314,0.00004182053,6.164267e-7,0.0001693288,0.9962392,0.000006565446,0.0004207946,0.00003628678,0.002999697],"study_design_scores_gemma":[0.000985952,0.00002165574,0.00005187199,0.00002169914,0.0000672306,0.000006247702,0.00005044072,0.9978539,0.0001946329,0.00004207654,0.0004784438,0.0002257853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000107865,0.0006431084,0.9972363,0.00009330195,0.0003588394,0.0009697503,0.00004354096,0.0004867698,0.00006052472],"genre_scores_gemma":[0.1589999,0.000575237,0.8396134,0.00003347102,0.0001870532,0.0001755498,0.0002459523,0.00007661712,0.00009272191],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1588921,"threshold_uncertainty_score":0.7215019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006671249574654596,"score_gpt":0.2106323036794777,"score_spread":0.2039610541048231,"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."}}