{"id":"W3097072875","doi":"10.5267/j.jpm.2020.10.001","title":"Solving a flow shop scheduling problem with missing operations in an Industry 4.0 production environment","year":2020,"lang":"en","type":"article","venue":"Journal of Project Management","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Flow shop scheduling; Tardiness; Job shop scheduling; Computer science; Scheduling (production processes); Mathematical optimization; Permutation (music); Simulated annealing; Industrial engineering; Algorithm; Mathematics; Engineering; Routing (electronic design automation)","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":[0.001694577,0.001020229,0.00113512,0.0006993214,0.0007397541,0.000862683,0.0009528329,0.001618712,0.001814537],"category_scores_gemma":[0.002923732,0.0005498614,0.001357927,0.00076125,0.000501899,0.0008421029,0.0005244296,0.00118955,0.0001291436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007168292,"about_ca_system_score_gemma":0.002047838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005953473,"about_ca_topic_score_gemma":0.004125377,"domain_scores_codex":[0.9993256,0.0002799098,0.00003288509,0.0001158298,0.0001025905,0.0001432057],"domain_scores_gemma":[0.997823,0.001712824,0.0001697341,0.00006187604,0.0001280234,0.0001046323],"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":[0.0001208098,0.0001517881,0.0005365399,0.0001691033,0.00003316249,0.0002515066,0.00006347267,0.9781772,0.00238635,0.001870627,0.0003840796,0.01585524],"study_design_scores_gemma":[0.00004703912,0.0001890654,0.0003444226,0.00001007555,0.00002075706,0.00007177104,0.000067539,0.9947656,0.001595745,0.002399827,0.0004781657,0.00001001396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5095004,0.0005439777,0.4843108,0.0004816876,0.0001188167,0.0002281092,0.0002522255,0.0003174679,0.004246452],"genre_scores_gemma":[0.6996788,0.000392165,0.2973143,0.00007548509,0.00006545438,0.0001898267,0.0004093531,0.0000710857,0.001803379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005953473,"threshold_uncertainty_score":0.0118376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02381794412213724,"score_gpt":0.2392452393588529,"score_spread":0.2154272952367157,"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."}}