{"id":"W4205482001","doi":"10.1287/ijoo.2021.0066","title":"Optimal Order Batching in Warehouse Management: A Data-Driven Robust Approach","year":2022,"lang":"en","type":"article","venue":"INFORMS Journal on Optimization","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Data warehouse; Analytics; Order picking; Order (exchange); Work order; Process (computing); Warehouse; Operations research; Industrial engineering; Data mining; Engineering; Reliability engineering","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":[0.003346735,0.00156313,0.002273029,0.001408419,0.000601996,0.002347567,0.002430409,0.001516646,0.001476047],"category_scores_gemma":[0.008228499,0.001572265,0.001351545,0.001670373,0.001066383,0.002255937,0.001225025,0.001995647,0.0002937148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00186297,"about_ca_system_score_gemma":0.002749153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01155597,"about_ca_topic_score_gemma":0.007810591,"domain_scores_codex":[0.9984567,0.0005179477,0.00008551587,0.0003981795,0.0003720389,0.000169674],"domain_scores_gemma":[0.9956101,0.002779164,0.0006915517,0.0002724621,0.0004986769,0.0001480695],"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":[0.00002322979,0.00002446459,0.0002212835,0.00003616707,0.0000201104,0.00002083634,0.00001152396,0.990099,0.0003231178,0.002466173,0.0001788283,0.006575314],"study_design_scores_gemma":[0.000002279455,0.00000936372,0.00003879049,0.000002322706,0.000003115353,0.000002440307,0.000002824816,0.9981791,0.0001379254,0.001550455,0.00006879829,0.000002635724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01396992,0.0003578434,0.9839928,0.0002958958,0.00002365039,0.00006527923,0.0001683308,0.0003702057,0.0007561081],"genre_scores_gemma":[0.6451427,0.0007553534,0.3510871,0.0001714989,0.0001643305,0.0002429789,0.0007547985,0.0002395263,0.001441765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01155597,"threshold_uncertainty_score":0.02297741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02392472256268477,"score_gpt":0.2276994575747005,"score_spread":0.2037747350120157,"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."}}