{"id":"W2553030351","doi":"10.1111/itor.12331","title":"A robust possibilistic programming approach to multiperiod hospital evacuation planning problem under uncertainty","year":2016,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Mathematical optimization; Computer science; Robust optimization; Metaheuristic; Route planning; Sensitivity (control systems); Operations research; Dynamic programming; Artificial intelligence; Mathematics; Engineering","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":[0.0008369987,0.0001643081,0.0001248292,0.0006365083,0.0002004812,0.0002220003,0.0003299344,0.0001113082,0.0003319528],"category_scores_gemma":[0.0001671893,0.0001390161,0.00005655045,0.0005758253,0.00008484125,0.0005087883,0.00001717978,0.0003555604,0.0001034345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001046027,"about_ca_system_score_gemma":0.0001731922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006092615,"about_ca_topic_score_gemma":0.0001440319,"domain_scores_codex":[0.9977474,0.00008906038,0.0004130218,0.0003763996,0.0009933622,0.0003808025],"domain_scores_gemma":[0.9987826,0.0003017104,0.00001776211,0.000171425,0.0005949421,0.000131527],"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":[0.0000341811,0.0002089412,0.0003936549,0.00001413977,0.00004308196,0.000002269229,0.0008885252,0.9759365,0.001737398,0.006010556,0.0001069352,0.01462382],"study_design_scores_gemma":[0.001170514,0.00008931186,0.006874238,0.0001794162,0.000005285703,0.00001366101,0.0012963,0.9853168,0.0002194053,0.001016975,0.003469311,0.0003488352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07123493,0.00002935959,0.9175989,0.002722187,0.0003750101,0.0008316895,0.00006922131,0.0001570943,0.006981603],"genre_scores_gemma":[0.9695818,0.0000187305,0.02783941,0.00005563512,0.0001233178,0.0007492841,0.00008027895,0.00003611844,0.001515404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8983469,"threshold_uncertainty_score":0.5668914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08456241407253086,"score_gpt":0.3599176752184909,"score_spread":0.27535526114596,"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."}}