{"id":"W2074707342","doi":"10.1007/s10479-013-1446-9","title":"Scheduling the two-machine open shop problem under resource constraints for setting the jobs","year":2013,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Job shop scheduling; Theory of computation; Open shop; Computer science; Scheduling (production processes); Schedule; Mathematical optimization; Heuristic; Flow shop scheduling; Job shop; Distributed computing; Operations research; Mathematics; Algorithm; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002786689,0.001051953,0.002898987,0.0007810292,0.0008887521,0.002050147,0.002590729,0.002709942,0.004453423],"category_scores_gemma":[0.008018458,0.001183948,0.001386754,0.001387466,0.00134164,0.003423698,0.001796913,0.002296958,0.0003931116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009264814,"about_ca_system_score_gemma":0.001890791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002887425,"about_ca_topic_score_gemma":0.002459959,"domain_scores_codex":[0.9983317,0.0007721011,0.00007218429,0.0002974941,0.000230359,0.0002960674],"domain_scores_gemma":[0.9953837,0.003323168,0.0002638854,0.0003373182,0.0002691457,0.0004228301],"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":[0.0007950597,0.0003844797,0.0005358946,0.0004246765,0.00007824748,0.0003929877,0.0001226242,0.9177884,0.003201598,0.04153346,0.002937023,0.03180555],"study_design_scores_gemma":[0.00008299595,0.0001010198,0.0001841458,0.0000128755,0.00001324284,0.000042403,0.00004438954,0.9675859,0.0006191799,0.0306491,0.000642564,0.00002201667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1242575,0.000966176,0.8652079,0.0009513524,0.0005496915,0.0002940448,0.0004342871,0.0003320956,0.007007017],"genre_scores_gemma":[0.802238,0.0005927436,0.190155,0.0001581463,0.0003491442,0.0002967308,0.0004261443,0.0001542889,0.005629719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004453423,"threshold_uncertainty_score":0.01489818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1498577471319737,"score_gpt":0.4181182125822349,"score_spread":0.2682604654502613,"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."}}