{"id":"W2141292481","doi":"10.1504/ijmme.2012.047998","title":"Mixed-Integer Linear Programming formulation for block-cave sequence optimisation","year":2012,"lang":"en","type":"article","venue":"International Journal of Mining and Mineral Engineering","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Canadian Natural Resources; University of Alberta","funders":"","keywords":"Integer programming; Planner; Scheduling (production processes); Linear programming; Mathematical optimization; Block (permutation group theory); Schedule; Net present value; Computer science; Production (economics); Engineering; Operations research; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003014784,0.0001130515,0.0001369754,0.0001625557,0.00001947887,0.00003798706,0.0001037086,0.00006161386,0.000003412344],"category_scores_gemma":[0.00008694447,0.0001133026,0.00006695799,0.00003137597,0.000005346573,0.0003709599,0.00001738555,0.00009266339,4.579291e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009188746,"about_ca_system_score_gemma":0.000008157974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000187871,"about_ca_topic_score_gemma":6.439635e-7,"domain_scores_codex":[0.999328,0.000002924211,0.0003323739,0.00005819993,0.00009886611,0.0001796771],"domain_scores_gemma":[0.9995942,0.00007178557,0.00009367037,0.00004423841,0.0001143055,0.00008177039],"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":[0.00007899931,0.00005366081,0.003297579,0.0001489005,0.0003633313,0.00001023301,0.003523723,0.8622452,0.03972992,0.003709106,0.00176512,0.08507422],"study_design_scores_gemma":[0.0004674269,0.00008152795,0.0003235505,0.0001801811,0.00002991001,0.000315262,0.0001549623,0.9767395,0.00553816,0.00002032909,0.01593799,0.0002112088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7683246,0.0002555079,0.2299115,0.00004501021,0.001232997,0.00006784191,0.000005779148,0.00007665513,0.00008018883],"genre_scores_gemma":[0.8228348,0.00004633182,0.1762912,0.00001101134,0.000755475,0.000007535368,0.000007540126,0.00002203813,0.0000240364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1144943,"threshold_uncertainty_score":0.4620344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03291618499245851,"score_gpt":0.2588687798311251,"score_spread":0.2259525948386666,"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."}}