{"id":"W4413872281","doi":"10.5267/j.ijiec.2025.7.002","title":"Recall cost-time tradeoffs for remanufacturing shop lot streaming scheduling problem with non mixed production using an improved non-dominated sorting genetic algorithm","year":2025,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Remanufacturing; Sorting; Computer science; Scheduling (production processes); Genetic algorithm; Mathematical optimization; Production (economics); Job shop scheduling; Flow shop scheduling; Sorting algorithm; Algorithm; Industrial engineering; Real-time computing; Operations research; Engineering; Manufacturing engineering; Mathematics; Computer network; Machine learning; Economics; Microeconomics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004225346,0.0002846972,0.0003545132,0.0007778053,0.0001306258,0.0002230277,0.0003255275,0.0001758014,0.000004248701],"category_scores_gemma":[0.0001970848,0.0002943749,0.0001171108,0.0004175278,0.00002445524,0.0004811752,0.0000274522,0.0005088154,6.665426e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004132308,"about_ca_system_score_gemma":0.0002237267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001313401,"about_ca_topic_score_gemma":0.000002139539,"domain_scores_codex":[0.9981685,0.00002578268,0.0009336025,0.0002531329,0.0003195051,0.0002994556],"domain_scores_gemma":[0.9984447,0.0001725083,0.0003331945,0.0001274958,0.0007961186,0.0001259601],"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":[0.00005546127,0.00006234865,0.00002964119,0.00002704381,0.0003663984,0.000009221479,0.0001996937,0.9411838,0.009580269,0.000004810594,0.00001136206,0.04846993],"study_design_scores_gemma":[0.002267105,0.0001153391,0.00009251448,0.0007883529,0.0001265044,0.0001038984,0.0001910047,0.9773011,0.01868427,0.00002672051,0.00002991893,0.0002732612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2453032,0.00003090254,0.7518212,0.00009718678,0.0021596,0.0004453855,0.00001194949,0.0001168683,0.00001377048],"genre_scores_gemma":[0.424053,0.000005940667,0.5748458,0.000008041045,0.0009506372,0.00002075539,0.00004157764,0.00005614084,0.00001813689],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1787498,"threshold_uncertainty_score":0.9999508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01833068092525785,"score_gpt":0.2561280281782371,"score_spread":0.2377973472529792,"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."}}