{"id":"W2944209481","doi":"10.5267/j.ijiec.2019.3.003","title":"Flexible job-shop scheduling with learning and forgetting effect by Multi-Agent System","year":2019,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Forgetting; Job shop; Computer science; Scheduling (production processes); Job shop scheduling; Industrial engineering; Operations management; Operations research; Flow shop scheduling; Engineering; Psychology; Operating system; Cognitive psychology; Schedule","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":[],"consensus_categories":[],"category_scores_codex":[0.0003165149,0.000164346,0.000228745,0.0002904077,0.00004731942,0.0001470238,0.000157951,0.00009217732,0.000007871395],"category_scores_gemma":[0.0001258242,0.0001509461,0.00005811427,0.0001807298,0.00001057355,0.0002213706,0.00002456809,0.0005056415,0.000007663571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001609001,"about_ca_system_score_gemma":0.00003500455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003510442,"about_ca_topic_score_gemma":1.495616e-7,"domain_scores_codex":[0.9989708,0.00002658363,0.0004059865,0.0001102771,0.0003290966,0.0001572435],"domain_scores_gemma":[0.9992263,0.0002412963,0.0001415262,0.00004785433,0.0002391593,0.0001038386],"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":[0.0000185822,0.000008232606,0.003874091,0.00002602767,0.0002196587,0.00001056685,0.0001022018,0.9914988,0.0009107431,0.0000696309,0.00002206854,0.003239417],"study_design_scores_gemma":[0.002113421,0.0001177931,0.00008861617,0.0005147071,0.00003098265,0.0001684546,0.0001974078,0.9944494,0.001792076,5.807309e-7,0.0003641918,0.0001623556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4146434,0.0002483273,0.5828239,0.00005505545,0.001836757,0.0001131081,0.000004772682,0.0001892083,0.00008544634],"genre_scores_gemma":[0.9370653,0.00001451734,0.06243873,0.000005614764,0.0003713333,0.000003580964,0.00001221916,0.00004154136,0.00004713848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5224219,"threshold_uncertainty_score":0.6155404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01152798882352392,"score_gpt":0.2397153687008084,"score_spread":0.2281873798772845,"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."}}