{"id":"W4413997978","doi":"10.18280/jesa.580709","title":"Multi-Objective Identical Parallel Flow Shop Scheduling Using NSGA-II and MOPSO with a Novel Load Balancing Procedure","year":2025,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Flow shop scheduling; Scheduling (production processes); Parallel computing; Mathematical optimization; Job shop scheduling; Mathematics; Schedule; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000799909,0.001056229,0.0007795484,0.0008352808,0.0004331192,0.0007123771,0.001264189,0.0007850411,0.001519895],"category_scores_gemma":[0.001328278,0.0004495333,0.0008923205,0.0007667143,0.0004541464,0.0005354283,0.0009314429,0.0008519686,0.0002286405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008596604,"about_ca_system_score_gemma":0.002245624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01092271,"about_ca_topic_score_gemma":0.008880193,"domain_scores_codex":[0.9995843,0.0001384622,0.00002072104,0.00006336436,0.0001288747,0.00006425763],"domain_scores_gemma":[0.9996525,0.0001468846,0.00007289477,0.00002822439,0.00006348398,0.00003593093],"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":[0.0000284213,0.00004332164,0.0001887407,0.00003928383,0.00002019697,0.00002767468,0.00001596714,0.982875,0.0009106236,0.001697504,0.0002913787,0.01386183],"study_design_scores_gemma":[0.000009656073,0.00002550673,0.00006621581,0.000003985464,0.000005251558,0.000005230626,0.000005730377,0.9988645,0.0002319207,0.000470391,0.0003086838,0.000002953658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06570094,0.0003255419,0.9242699,0.000288878,0.00009559812,0.0002952787,0.0001403653,0.0006705286,0.008212989],"genre_scores_gemma":[0.6453096,0.0002576658,0.3495591,0.0001751048,0.00004485185,0.0006282205,0.0002771215,0.0001000389,0.003648343],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01092271,"threshold_uncertainty_score":0.02171826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01451092074622379,"score_gpt":0.2512650097766114,"score_spread":0.2367540890303876,"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."}}