{"id":"W2479250721","doi":"10.1007/s00170-016-9141-z","title":"A mathematical model for designing reconfigurable cellular hybrid manufacturing-remanufacturing systems","year":2016,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Remanufacturing; Cellular manufacturing; Control reconfiguration; Sustainability; Production planning; Process (computing); Manufacturing engineering; Integer programming; Production (economics); Computer science; Hybrid system; Engineering; Industrial engineering; Systems engineering; Mathematical optimization; Embedded system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004405794,0.00114255,0.001145373,0.0006229206,0.000702926,0.00164266,0.001622414,0.00197928,0.005213781],"category_scores_gemma":[0.001183051,0.0006021291,0.001016592,0.000920788,0.0006611238,0.0008806087,0.00124441,0.00096493,0.0007775742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001389824,"about_ca_system_score_gemma":0.001081377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007491152,"about_ca_topic_score_gemma":0.005715853,"domain_scores_codex":[0.9997079,0.00006983631,0.00001129911,0.0000699513,0.00007819104,0.00006278215],"domain_scores_gemma":[0.9996265,0.0001894885,0.00007109222,0.00002262233,0.00006897398,0.00002126493],"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":[0.00001040181,0.000009736326,0.00006261209,0.00003340732,0.00001035796,0.0000546191,0.00001439299,0.9831993,0.0007659291,0.01308117,0.0002610233,0.002497113],"study_design_scores_gemma":[0.000004216643,0.00001208086,0.00002931349,0.000003866443,0.00000595387,0.00001167143,0.000005529294,0.9970112,0.0001400604,0.002349093,0.0004232541,0.000003690656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0220423,0.0006261539,0.951332,0.0004118647,0.0000967883,0.00008407429,0.0002226894,0.000228683,0.02495537],"genre_scores_gemma":[0.9145342,0.0008792283,0.06356454,0.0001526223,0.00005821353,0.0003766628,0.000206451,0.00007055388,0.02015755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007491152,"threshold_uncertainty_score":0.01744181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01414011329478441,"score_gpt":0.2283486255631208,"score_spread":0.2142085122683364,"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."}}