{"id":"W4380153691","doi":"10.1007/s10479-023-05397-0","title":"Integrated optimal scheduling and routing of repair crew and relief vehicles after disaster: a novel hybrid solution approach","year":2023,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Crew; Computer science; Scheduling (production processes); Mathematical optimization; Benders' decomposition; Integer programming; Theory of computation; Routing (electronic design automation); Vehicle routing problem; Heuristic; Decomposition; Operations research; Algorithm; Engineering; Mathematics; Computer network","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":[0.001035313,0.001338943,0.001589036,0.001481518,0.0007173287,0.001875159,0.002542164,0.002328674,0.004409458],"category_scores_gemma":[0.001393257,0.001168331,0.001687237,0.001470454,0.0006191933,0.001248902,0.001459444,0.0009792439,0.0003592729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001082922,"about_ca_system_score_gemma":0.002102704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009629468,"about_ca_topic_score_gemma":0.00931281,"domain_scores_codex":[0.9994437,0.0001637151,0.00002047363,0.0001081809,0.0001128063,0.0001511138],"domain_scores_gemma":[0.9993754,0.0003308108,0.00007424524,0.00003519698,0.0001115802,0.00007278205],"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":[0.00006130615,0.00007940039,0.0001720366,0.00003274042,0.00004712647,0.00004405764,0.00002583049,0.98566,0.0007003102,0.002070824,0.0004348242,0.01067152],"study_design_scores_gemma":[0.00001122402,0.00002573744,0.00004111709,0.000001715933,0.000009712247,0.00000613158,0.00001123994,0.9990269,0.00007369389,0.0006825247,0.0001071637,0.000002845663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06412419,0.0003345064,0.9248363,0.0003063184,0.00015053,0.0001564062,0.0001416139,0.0003295575,0.009620646],"genre_scores_gemma":[0.7277179,0.0002628761,0.263722,0.0001641359,0.0001701905,0.0002943315,0.0002437362,0.0001316084,0.007293185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009629468,"threshold_uncertainty_score":0.0191468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1757722031358402,"score_gpt":0.3633938875336605,"score_spread":0.1876216843978203,"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."}}