{"id":"W1965868850","doi":"10.1007/s10898-014-0185-z","title":"A hybrid method based on linear programming and variable neighborhood descent for scheduling production in open-pit mines","year":2014,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; AngloGold Ashanti","keywords":"Mathematical optimization; Open-pit mining; Benchmark (surveying); Linear programming; Scheduling (production processes); Descent (aeronautics); Computer science; Schedule; Variable (mathematics); Production planning; Mathematics; Production (economics); Engineering","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":[0.00111799,0.0008616668,0.001345429,0.0007719303,0.000461669,0.0007165065,0.001615919,0.001121408,0.00228181],"category_scores_gemma":[0.001780994,0.0006603867,0.0007643452,0.0009216123,0.0004294166,0.0006788303,0.0007105966,0.0008804036,0.0004320761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007139682,"about_ca_system_score_gemma":0.00188123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008895929,"about_ca_topic_score_gemma":0.00975909,"domain_scores_codex":[0.9994594,0.0002213003,0.0000189209,0.00009038752,0.0001475276,0.00006240949],"domain_scores_gemma":[0.9992096,0.0005105693,0.00006921658,0.00003423108,0.0001357818,0.00004053815],"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":[0.00006013862,0.0000810535,0.0002460124,0.00005778419,0.0000271058,0.00004147498,0.00003024346,0.947655,0.00133876,0.002810553,0.001090862,0.04656095],"study_design_scores_gemma":[0.000008990039,0.00002112108,0.00002782776,0.000002072057,0.000002498754,0.000004825927,0.000003208121,0.9992087,0.0001459583,0.0003069173,0.0002657777,0.000002068681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02053808,0.0002047669,0.9757054,0.0001369613,0.00005433923,0.00009995161,0.00005141487,0.0005801698,0.002628978],"genre_scores_gemma":[0.210221,0.0001787622,0.7845681,0.0001502527,0.00007043473,0.0004366097,0.0002230703,0.0002165237,0.003935282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008895929,"threshold_uncertainty_score":0.01768833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01375831141325796,"score_gpt":0.2672620278749107,"score_spread":0.2535037164616528,"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."}}