{"id":"W2043472740","doi":"10.3926/jiem.451","title":"Workforce scheduling: A new model incorporating human factors","year":2012,"lang":"en","type":"article","venue":"Journal of Industrial Engineering and Management","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Workforce; Overtime; Human resources; Originality; Workforce management; Scheduling (production processes); Human resource management; Engineering; Operations management; Operations research; Computer science; Labour economics; Economics; Knowledge management; Psychology; Management; Social psychology","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":[0.001781269,0.001625962,0.001497966,0.0009262599,0.0008117607,0.0020456,0.003003385,0.002619741,0.009730148],"category_scores_gemma":[0.003395697,0.0009679036,0.001748699,0.001084639,0.0008141315,0.001455127,0.001386403,0.002368944,0.001120191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002252751,"about_ca_system_score_gemma":0.003092715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02318501,"about_ca_topic_score_gemma":0.01393097,"domain_scores_codex":[0.9988908,0.0003745011,0.00004301028,0.0002850382,0.0001720246,0.0002346488],"domain_scores_gemma":[0.9983699,0.0008350689,0.0002745798,0.0000489452,0.000264994,0.0002065303],"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":[0.00005941241,0.00005391617,0.0005074314,0.00004957536,0.00002839254,0.00006822804,0.00006339999,0.9914964,0.0002811135,0.003054427,0.0005315735,0.003806031],"study_design_scores_gemma":[0.00001875861,0.000042823,0.0001690041,0.00001046295,0.00001527296,0.000011138,0.00002166223,0.9976932,0.00003838885,0.00136381,0.0006079774,0.000007407719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07369135,0.001082905,0.8964167,0.001767487,0.0005120877,0.000373116,0.001205028,0.0004199122,0.02453144],"genre_scores_gemma":[0.8675876,0.001259444,0.09158348,0.0004722417,0.0002874758,0.0009487955,0.0007987656,0.0001688726,0.03689327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02318501,"threshold_uncertainty_score":0.04610014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06319480430748918,"score_gpt":0.254115396641964,"score_spread":0.1909205923344748,"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."}}