{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001216715,0.001206536,0.0009333832,0.001057713,0.000590467,0.0008199794,0.001344558,0.0008311717,0.002718334],"category_scores_gemma":[0.003612395,0.0006094365,0.001310083,0.001375552,0.0003785365,0.001036709,0.0008838882,0.001525703,0.0005564661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007287918,"about_ca_system_score_gemma":0.002183859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006478704,"about_ca_topic_score_gemma":0.007461315,"domain_scores_codex":[0.9994155,0.0001916472,0.0000388335,0.0001323224,0.0001360921,0.0000856474],"domain_scores_gemma":[0.9986137,0.0007799437,0.0001490884,0.000127881,0.0002248317,0.0001045341],"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":[0.0001682121,0.0003575639,0.002898036,0.0002986957,0.0000814829,0.0001136114,0.0001273214,0.8586963,0.003629577,0.005118849,0.00336914,0.1251413],"study_design_scores_gemma":[0.00002897266,0.00004369558,0.0002128658,0.000009625899,0.0000091569,0.00001625658,0.00005035647,0.9948566,0.0009171919,0.002844701,0.001006457,0.000004075684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1321138,0.0009340429,0.8574968,0.0009203453,0.0002169604,0.0003586569,0.0007837762,0.002447922,0.004727697],"genre_scores_gemma":[0.2824925,0.0002523472,0.7140555,0.0001737172,0.00005166864,0.0002724168,0.001363708,0.0001602802,0.001177794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006478704,"threshold_uncertainty_score":0.01288199,"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."}}