{"id":"W4401813867","doi":"10.1007/s10107-024-02133-9","title":"A fast combinatorial algorithm for the bilevel knapsack problem with interdiction constraints","year":2024,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Knapsack problem; Interdiction; Continuous knapsack problem; Bilevel optimization; Mathematical optimization; Change-making problem; Relaxation (psychology); Mathematics; Cutting stock problem; Algorithm; Generalized assignment problem; Integer programming; Computer science; Combinatorial optimization; Dynamic programming; Linear programming relaxation; Optimization problem","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001202557,0.001934355,0.002140649,0.001680628,0.001343426,0.003163106,0.002706506,0.002583367,0.0139867],"category_scores_gemma":[0.004401548,0.001385184,0.001497579,0.003342799,0.0008198512,0.002932898,0.002666431,0.003218688,0.00329616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001431645,"about_ca_system_score_gemma":0.003253553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008465788,"about_ca_topic_score_gemma":0.008328427,"domain_scores_codex":[0.9990521,0.0002282654,0.00004767526,0.0002101177,0.0002996317,0.0001622364],"domain_scores_gemma":[0.9981701,0.001148034,0.00009210563,0.0001616322,0.000330728,0.00009738476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002250812,0.0003348107,0.0003669067,0.0002452442,0.00008073527,0.000119332,0.0000835412,0.6590691,0.001918532,0.03714859,0.01176262,0.2886455],"study_design_scores_gemma":[0.0000987977,0.00004577768,0.000071923,0.00002019433,0.00001297429,0.00004284258,0.00002643947,0.9788472,0.0004219722,0.01767837,0.002718492,0.00001508858],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004527725,0.0002194604,0.9883598,0.0002144543,0.0001264999,0.0001040267,0.0001288877,0.0009000169,0.005419029],"genre_scores_gemma":[0.0582476,0.0002586674,0.9358064,0.0001650559,0.00009009564,0.0003673209,0.0005240829,0.0004130254,0.004127807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0139867,"threshold_uncertainty_score":0.04679018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0239749780700208,"score_gpt":0.2525837451105655,"score_spread":0.2286087670405447,"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."}}