{"id":"W2214231849","doi":"10.5267/j.ijiec.2015.9.004","title":"M-machine, no-wait flowshop scheduling with sequence dependent setup times and truncated learning function to minimize the makespan","year":2015,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Job shop scheduling; Simulated annealing; Learning effect; Computer science; Mathematical optimization; Scheduling (production processes); Sequence (biology); Integer programming; Artificial intelligence; Algorithm; Mathematics; 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":[0.0003799972,0.0001573717,0.0001640227,0.0002873883,0.00006234618,0.000187101,0.0002197366,0.00007553773,0.00001586025],"category_scores_gemma":[0.0005348535,0.000123567,0.00004006593,0.0002684418,0.00001881051,0.0002207827,0.0000308597,0.0005131305,0.00001289147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001367357,"about_ca_system_score_gemma":0.0001056848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001316789,"about_ca_topic_score_gemma":0.00000150322,"domain_scores_codex":[0.9988753,0.00003328615,0.000382215,0.0001124185,0.0004540069,0.000142734],"domain_scores_gemma":[0.9987966,0.000186171,0.0001087993,0.00006928127,0.0006611146,0.0001780715],"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":[0.00007623131,0.00001094714,0.000336055,0.000002316755,0.0001834405,0.00001384723,0.0002950466,0.9945392,0.0001509202,0.00009965374,0.00008255849,0.004209757],"study_design_scores_gemma":[0.001288651,0.0001119723,0.0001829845,0.0001236929,0.00004358993,0.0001937102,0.0002664345,0.9964923,0.0001179433,0.00002019987,0.001007386,0.0001510968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07719077,0.0001649261,0.918635,0.000789367,0.002746843,0.000124567,0.00001165105,0.0001533985,0.0001834897],"genre_scores_gemma":[0.905623,0.00001484678,0.09330856,0.00005948411,0.0008624834,0.000006420508,0.00002241092,0.00003696632,0.00006583432],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8284322,"threshold_uncertainty_score":0.5038917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02869135250695101,"score_gpt":0.2454441630202029,"score_spread":0.2167528105132519,"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."}}