{"id":"W3142556707","doi":"10.1109/wsc.2010.5678889","title":"Dynamic adjustment of replenishment parameters using optimum-seeking simulation","year":2010,"lang":"en","type":"article","venue":"Proceedings of the 2010 Winter Simulation Conference","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Reorder point; Discrete event simulation; Heuristic; Supply chain; Computer science; Simulation-based optimization; Event (particle physics); Point (geometry); Mathematical optimization; Continuous simulation; Service level; Dynamic simulation; Operations research; Simulation; Economic order quantity; Engineering; Mathematics","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":[0.0001915492,0.0001577263,0.0001938597,0.0001118004,0.00005184245,0.0000418074,0.00027303,0.0001079993,0.00006147255],"category_scores_gemma":[0.0001459067,0.0001350537,0.00008970703,0.0001904973,0.00007495633,0.0002659246,0.0000636829,0.0002254767,0.000002052455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004258969,"about_ca_system_score_gemma":0.00001868114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007908485,"about_ca_topic_score_gemma":0.000002053356,"domain_scores_codex":[0.998953,0.000004903656,0.0004472201,0.0001809241,0.0002585372,0.0001554473],"domain_scores_gemma":[0.9989599,0.00008405453,0.0002591957,0.0001704341,0.0004821082,0.00004432035],"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":[0.00001216461,0.00001762684,0.001078392,0.00005755133,0.00002595401,1.954082e-8,0.0005048462,0.9517646,0.04478742,0.00009958012,0.000004273174,0.001647558],"study_design_scores_gemma":[0.00025628,0.00001986892,0.001808849,0.000120285,0.00003618836,0.000001034393,0.000118055,0.977778,0.01949913,0.0002157724,0.00001359406,0.0001329034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8590103,0.00001577212,0.1391046,0.00003935195,0.001147085,0.0002619489,0.00000632539,0.00009316549,0.0003213632],"genre_scores_gemma":[0.921298,0.000003298017,0.07858039,0.00001376622,0.0000286802,0.000004748903,0.000002490731,0.00002276061,0.00004582591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0622877,"threshold_uncertainty_score":0.5507331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02440609163861876,"score_gpt":0.2647795963597145,"score_spread":0.2403735047210958,"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."}}