{"id":"W3134399921","doi":"10.1287/trsc.2020.1029","title":"Workforce Scheduling with Order-Picking Assignments in Distribution Facilities","year":2021,"lang":"en","type":"article","venue":"Transportation Science","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Order picking; Scheduling (production processes); Metaheuristic; Schedule; Job shop scheduling; Computer science; Mathematical optimization; Operations research; Order (exchange); Engineering; Mathematics; Economics; Business","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":[0.00009409144,0.00007020613,0.00006626235,0.00004199554,0.00008049821,0.00004034131,0.00006080521,0.00002172156,0.00001702775],"category_scores_gemma":[0.00002774092,0.00006982723,0.000007500687,0.0005456308,0.00008063265,0.000284698,0.000001410813,0.00007740039,0.000002453378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007685919,"about_ca_system_score_gemma":0.0000510499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001021047,"about_ca_topic_score_gemma":0.0001414434,"domain_scores_codex":[0.9993529,0.000004188619,0.0001324664,0.0001599756,0.0001792103,0.0001713097],"domain_scores_gemma":[0.9997588,0.00001817426,0.00001840439,0.00008675559,0.00008276047,0.00003512478],"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":[0.000004108564,0.000005311305,0.003008088,0.00001694016,0.000001002694,0.000006208242,0.0003781295,0.9937075,0.0004667026,0.001470821,5.468304e-7,0.0009346326],"study_design_scores_gemma":[0.001763458,0.00009579163,0.258666,0.0004757783,0.0000274429,0.000008951336,0.004598801,0.5528949,0.17574,0.003097598,0.001608658,0.001022569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3434696,0.00003208857,0.6559192,0.00000955793,0.0000545004,0.00003483869,0.0000119908,0.00007169405,0.0003965534],"genre_scores_gemma":[0.9771086,0.00003337764,0.02263125,0.000006630124,0.000005951064,0.000007648013,0.0001074271,0.000005668398,0.00009341917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.633639,"threshold_uncertainty_score":0.2847472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01555422438098796,"score_gpt":0.2361576495232156,"score_spread":0.2206034251422276,"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."}}