{"id":"W3013316588","doi":"10.5267/j.ijiec.2020.1.001","title":"A modified tabu search algorithm for the single-machine scheduling problem using additive manufacturing technology","year":2020,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tabu search; Mathematical optimization; Single-machine scheduling; Job shop scheduling; Scheduling (production processes); Computer science; Guided Local Search; Algorithm; Engineering; 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":[0.0002128737,0.0001781414,0.0002216154,0.0003803376,0.00009642233,0.0001383553,0.0004613139,0.0001345349,0.00001247945],"category_scores_gemma":[0.0002393976,0.0001582262,0.0001315962,0.0003353024,0.00003222762,0.0002037033,0.00005144665,0.000619148,0.000001929783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001722395,"about_ca_system_score_gemma":0.00008737257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003925828,"about_ca_topic_score_gemma":2.439578e-7,"domain_scores_codex":[0.9987339,0.00001518729,0.0005600014,0.0001279655,0.0003520928,0.0002108417],"domain_scores_gemma":[0.9987906,0.0003873314,0.0001446681,0.00006867197,0.0004971597,0.0001116203],"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":[0.00001426998,0.00001747053,0.000003030695,0.000004980584,0.0003298064,0.000009873574,0.000193284,0.9097775,0.0005265361,0.0001408032,0.00003075199,0.08895165],"study_design_scores_gemma":[0.001272678,0.0000697931,0.000002818374,0.0001077723,0.00005032015,0.00008320799,0.0001794088,0.9889731,0.008529859,0.00005441635,0.0005262098,0.0001504632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01126761,0.0002401922,0.9849241,0.001504492,0.001568702,0.0002328614,0.00008101628,0.000165315,0.00001566061],"genre_scores_gemma":[0.5181608,0.00002247613,0.4800263,0.0000517068,0.001658506,0.000009700875,0.00002123088,0.00004703268,0.000002299456],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5068932,"threshold_uncertainty_score":0.6452277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04924084801278942,"score_gpt":0.2716585936891527,"score_spread":0.2224177456763633,"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."}}