{"id":"W2798036437","doi":"10.1016/j.cie.2018.04.021","title":"Permutation flowshop scheduling with time lag constraints and makespan criterion","year":2018,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China; Natural Science Foundation of Beijing Municipality; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Job shop scheduling; Mathematical optimization; Scheduling (production processes); Benchmark (surveying); Minification; Permutation (music); Computer science; Heuristic; Integer programming; Time complexity; Schedule; Heuristics; Mathematics; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001107087,0.0009343651,0.001089444,0.0006887761,0.0007585526,0.0009867831,0.001188485,0.0006618722,0.002722863],"category_scores_gemma":[0.00253147,0.0004474894,0.0005325417,0.001722713,0.0004142582,0.001406027,0.0007087299,0.0009116842,0.0002973784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008568774,"about_ca_system_score_gemma":0.002443401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002551805,"about_ca_topic_score_gemma":0.003285326,"domain_scores_codex":[0.9994691,0.0001609879,0.00003507796,0.00008045354,0.0001338814,0.0001205603],"domain_scores_gemma":[0.9992404,0.0003669474,0.0000987367,0.00008586996,0.0001252434,0.00008291058],"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":[0.0002576493,0.0001405938,0.0004378557,0.0001741841,0.00004293143,0.0001727799,0.0000437674,0.921389,0.007053614,0.03699907,0.001432095,0.03185645],"study_design_scores_gemma":[0.00002009861,0.0001821505,0.0002508399,0.000006751789,0.00001480512,0.00004194735,0.00001346999,0.9790302,0.002136417,0.01741536,0.000876219,0.00001172015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05259483,0.0002328342,0.9405597,0.000199567,0.0001383005,0.0001396978,0.0002355699,0.000293502,0.005605909],"genre_scores_gemma":[0.8327209,0.0003060856,0.1593116,0.00007614123,0.0001095255,0.0001815859,0.0003050818,0.0001491208,0.006839983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002722863,"threshold_uncertainty_score":0.009108901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01380998666727152,"score_gpt":0.2014354069919974,"score_spread":0.1876254203247259,"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."}}