{"id":"W2804702513","doi":"10.1080/17509653.2018.1445045","title":"Iterated local and very-large-scale neighborhood search for a novel uncapacitated exam scheduling model","year":2018,"lang":"en","type":"article","venue":"International Journal of Management Science and Engineering Management","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Iterated local search; Iterated function; Computer science; Scheduling (production processes); Local search (optimization); Mathematical optimization; Benchmark (surveying); Heuristic; Scale (ratio); Mathematics; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00597821,0.0001515276,0.0001931234,0.00143453,0.0002735229,0.0007486104,0.0009520514,0.0000321724,0.00001182254],"category_scores_gemma":[0.0002853285,0.0001248973,0.00006421738,0.0009051134,0.0002724093,0.0008040581,0.000414079,0.0001272648,0.00000855666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001176698,"about_ca_system_score_gemma":0.00004205736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002269515,"about_ca_topic_score_gemma":0.000001124677,"domain_scores_codex":[0.9966848,0.00001345191,0.0005675129,0.0004045811,0.001936201,0.0003934151],"domain_scores_gemma":[0.9978879,0.0001396985,0.0001671138,0.0002106605,0.001413169,0.0001814313],"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":[0.0002475667,0.0003417098,0.0009381785,0.00006722978,0.0006293904,0.00003989374,0.001760927,0.7969663,0.003067162,0.1318125,0.0008401693,0.06328902],"study_design_scores_gemma":[0.001197334,0.00009934725,0.002702837,0.0001099864,0.00004946006,0.00002342078,0.001284125,0.9894682,0.0003309503,0.002091789,0.002498806,0.0001437546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1402757,0.00005924889,0.8567043,0.0009802051,0.0007753001,0.0001556503,0.000008044993,0.00002259855,0.001018951],"genre_scores_gemma":[0.8961364,0.00006587549,0.103137,0.0002040125,0.0001344045,0.000007052497,0.000001207599,0.000009905244,0.0003041202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7558607,"threshold_uncertainty_score":0.7218868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05860504167797429,"score_gpt":0.3409210195313355,"score_spread":0.2823159778533612,"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."}}