{"id":"W4405639190","doi":"10.1145/3709013","title":"Data Mining-Driven Shift Enumeration for Accelerating the Solution of Large-Scale Personnel Scheduling Problems","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Evolutionary Learning and Optimization","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Concordia University; Group for Research in Decision Analysis","funders":"","keywords":"Enumeration; Scheduling (production processes); Scale (ratio); Computer science; Operations research; Operations management; Engineering; Mathematics; Geography; Cartography","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.002787107,0.0001928029,0.0002311688,0.0004128864,0.002047703,0.0003770955,0.0005640578,0.0001542018,0.000103759],"category_scores_gemma":[0.001966679,0.0001492233,0.0001298467,0.001033745,0.0001345249,0.0009461117,0.00004338008,0.0003960336,0.00001524112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005779036,"about_ca_system_score_gemma":0.0001851985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002792665,"about_ca_topic_score_gemma":0.00005843226,"domain_scores_codex":[0.9973547,0.00029995,0.0006519059,0.0007410953,0.0006312749,0.0003210268],"domain_scores_gemma":[0.9963012,0.002413418,0.0002119917,0.000660349,0.0003389856,0.00007399394],"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":[0.00003146111,0.00007319859,0.0002769162,0.00002837133,0.00005159912,2.045883e-7,0.002823522,0.9853712,0.0001237217,0.0002514174,0.000139999,0.01082838],"study_design_scores_gemma":[0.0003271852,0.0001347456,0.0002565973,0.0001421076,0.0001179913,0.00001083994,0.003254091,0.992384,0.0000311576,0.0007780619,0.002401589,0.0001616704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01083671,0.001302443,0.9828709,0.003610219,0.0005212451,0.0003937552,0.0002114906,0.000183561,0.00006960988],"genre_scores_gemma":[0.6960748,0.0001437449,0.3028498,0.00003189614,0.0001355683,0.00005800201,0.0002478012,0.0000241694,0.0004341271],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6852381,"threshold_uncertainty_score":0.9992515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1120187096598312,"score_gpt":0.3617451143697751,"score_spread":0.2497264047099439,"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."}}