{"id":"W1965880809","doi":"10.1016/j.ejor.2005.06.053","title":"Modeling and analysis of a supply–assembly–store chain","year":2005,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Assembly Line Balancing Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Chinese Academy of Sciences; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Supply chain; Markov chain; Computer science; Workstation; Set (abstract data type); Poisson distribution; Safety stock; Process (computing); Product (mathematics); Component (thermodynamics); Markov process; Conveyor system; Industrial engineering; Operations research; Mathematics; Engineering; Business","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000735894,0.0007261421,0.001181646,0.0009644988,0.001019633,0.00238799,0.002496115,0.002951653,0.01060094],"category_scores_gemma":[0.001629957,0.001267725,0.00121902,0.001503856,0.001235368,0.002190638,0.001441975,0.001130238,0.001083313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001829193,"about_ca_system_score_gemma":0.002471217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04415342,"about_ca_topic_score_gemma":0.01543143,"domain_scores_codex":[0.9996469,0.00008986054,0.00001763112,0.0000764027,0.00008201325,0.00008715149],"domain_scores_gemma":[0.9993437,0.0003254575,0.0001074246,0.00003849397,0.0001178097,0.00006705961],"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":[0.00001650582,0.00001200906,0.0001445784,0.00001049557,0.00000709503,0.00004605946,0.00001328553,0.9943145,0.0002947233,0.004238889,0.0001068674,0.0007948755],"study_design_scores_gemma":[0.000005841431,0.000007929403,0.00008486731,0.000002173641,0.000005129636,0.000006821318,0.000008185114,0.9979789,0.00006513788,0.001596601,0.0002353393,0.000003164472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2350143,0.001015114,0.709534,0.00184652,0.0001519504,0.0002160829,0.001007475,0.0005263315,0.05068816],"genre_scores_gemma":[0.9459287,0.0006798473,0.02451198,0.00009495718,0.00005313789,0.0001610577,0.000420546,0.00009393301,0.02805595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04415342,"threshold_uncertainty_score":0.08779281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03919696098079783,"score_gpt":0.3152330231395685,"score_spread":0.2760360621587706,"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."}}