{"id":"W4293252855","doi":"10.5267/j.ijiec.2022.7.002","title":"More effective heuristics for a two-machine no-wait flowshop to minimize maximum lateness","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Heuristics; Dominance (genetics); Mathematical optimization; Scheduling (production processes); Computer science; Constructive; Relation (database); Job shop scheduling; Mathematics; Data mining","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":[0.001108806,0.001317114,0.001133306,0.001016274,0.0006615194,0.001030558,0.001363532,0.001088429,0.002688729],"category_scores_gemma":[0.002816811,0.0005814903,0.0009139425,0.0008061712,0.0005101548,0.001411394,0.0007989796,0.001264295,0.0003044817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00107564,"about_ca_system_score_gemma":0.002347029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003067844,"about_ca_topic_score_gemma":0.004314301,"domain_scores_codex":[0.9992965,0.0002517257,0.00003064323,0.0001102636,0.0001457883,0.0001650025],"domain_scores_gemma":[0.9988191,0.0007407808,0.0001131814,0.00006642106,0.0001425842,0.000118014],"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":[0.0001200868,0.0002640632,0.0003870573,0.0001914938,0.00004993629,0.0001295219,0.0001208413,0.9250797,0.005580449,0.02152508,0.002372944,0.0441788],"study_design_scores_gemma":[0.00008627976,0.0001006836,0.0001296807,0.00001693706,0.00001628841,0.00003983116,0.0000363484,0.9870314,0.001565059,0.009344623,0.001617643,0.0000151557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02922853,0.0004085043,0.9665326,0.0003251543,0.00009575595,0.0001792095,0.00007779619,0.0001767125,0.002975696],"genre_scores_gemma":[0.3498398,0.0004519641,0.644598,0.000302352,0.0001014773,0.000319418,0.0002493622,0.0001590631,0.003978576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003067844,"threshold_uncertainty_score":0.008994699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01606371459052399,"score_gpt":0.2644645305354911,"score_spread":0.2484008159449671,"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."}}