{"id":"W3188632549","doi":"10.1115/msec2021-60408","title":"A Dynamic Programming Approach to Solve the Facility Layout Problem for Reconfigurable Manufacturing","year":2021,"lang":"en","type":"article","venue":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Set (abstract data type); Heuristic; Dynamic programming; Mathematical optimization; Decomposition; State (computer science); Genetic algorithm; Metaheuristic; Point (geometry); Algorithm; Mathematics; Programming language; Artificial intelligence","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":[0.0008179441,0.001047805,0.001247834,0.001193191,0.0005201634,0.001127683,0.001348094,0.001553896,0.003860972],"category_scores_gemma":[0.001509618,0.0008896768,0.001241522,0.001611142,0.0006626924,0.0006590842,0.0008985414,0.001262947,0.0002359477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001405955,"about_ca_system_score_gemma":0.00147591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01044439,"about_ca_topic_score_gemma":0.007983247,"domain_scores_codex":[0.9995114,0.00019107,0.00001885892,0.00009696042,0.00007873953,0.0001029729],"domain_scores_gemma":[0.9992799,0.0005206288,0.0000719505,0.00001553551,0.00007478287,0.00003719017],"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":[0.00001883897,0.00002892835,0.0001154256,0.0000441951,0.00001544453,0.00004027123,0.00001407316,0.9892831,0.0003580629,0.002865314,0.0002368095,0.006979565],"study_design_scores_gemma":[0.000009795825,0.000025491,0.00004864318,0.000006489191,0.000005689841,0.000007872262,0.00001060996,0.9978974,0.000125166,0.001478598,0.0003805932,0.000003676677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03983909,0.0007135143,0.9482864,0.0003989545,0.00005902256,0.0001861956,0.0001942851,0.0002386953,0.01008371],"genre_scores_gemma":[0.5123706,0.0005973119,0.4791639,0.0001556524,0.00005982174,0.0007233611,0.0003619267,0.00009488424,0.00647249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01044439,"threshold_uncertainty_score":0.02076721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01867304781515853,"score_gpt":0.247967889241196,"score_spread":0.2292948414260375,"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."}}