{"id":"W2273624835","doi":"10.1007/s11590-016-0998-4","title":"A Lagrangian heuristic for concave cost facility location problems: the plant location and technology acquisition problem","year":2016,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Lagrangian relaxation; Mathematical optimization; Heuristic; Facility location problem; Lagrangian; Branch and bound; Computational intelligence; Upper and lower bounds; Mathematics; Minification; Set (abstract data type); Computer science; Integer (computer science); Applied 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001818459,0.0009531094,0.001747312,0.001180475,0.0007041874,0.001784467,0.002176739,0.002630188,0.006214733],"category_scores_gemma":[0.004405913,0.0008787057,0.001006442,0.001850516,0.001182476,0.001584137,0.001538874,0.001738266,0.0006471043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001965055,"about_ca_system_score_gemma":0.003391274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009209069,"about_ca_topic_score_gemma":0.01066293,"domain_scores_codex":[0.9990371,0.0005367567,0.00002488532,0.0001023591,0.0001537504,0.0001451319],"domain_scores_gemma":[0.9979195,0.00155569,0.0001179072,0.00008895295,0.0001760409,0.0001419339],"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":[0.0001007973,0.0001039979,0.0002226176,0.000102234,0.00002548262,0.00008766243,0.00004789164,0.945067,0.0003969466,0.02384244,0.004603372,0.02539941],"study_design_scores_gemma":[0.00004635601,0.00003007375,0.00006261569,0.0000156389,0.000008216368,0.00001940399,0.00002204181,0.9908288,0.0001195557,0.007818725,0.001021747,0.000006891915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01956839,0.000508382,0.966265,0.0009105065,0.0001143646,0.0001567517,0.0002163213,0.000230894,0.01202943],"genre_scores_gemma":[0.3525205,0.000579764,0.6361476,0.0004074029,0.0001260347,0.0003763453,0.0004561657,0.0002321582,0.009154157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009209069,"threshold_uncertainty_score":0.0207904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01575863942459743,"score_gpt":0.2026103310353574,"score_spread":0.18685169161076,"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."}}