{"id":"W3116347600","doi":"10.1155/2020/1385049","title":"Nature-Inspired Metaheuristics for Two-Agent Scheduling with Due Date and Release Time","year":2020,"lang":"en","type":"article","venue":"Complexity","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metaheuristic; Solver; Computer science; Job shop scheduling; Mathematical optimization; Scheduling (production processes); Computation; Cloud computing; Heuristic; Distributed computing; Algorithm; Artificial intelligence; Mathematics; Schedule","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007507865,0.0001471341,0.0002044168,0.00002735064,0.00009502414,0.00005587238,0.0001025173,0.00005949122,0.00005275034],"category_scores_gemma":[0.0001098781,0.0001369747,0.00003115857,0.0001511926,0.00005069535,0.00006034727,0.00003126747,0.000196827,0.00002822994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001247508,"about_ca_system_score_gemma":0.00001421049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000251878,"about_ca_topic_score_gemma":0.000001994267,"domain_scores_codex":[0.9993537,0.00001481568,0.0001493729,0.000205836,0.0001015805,0.0001746617],"domain_scores_gemma":[0.9995249,0.00005416073,0.00003008152,0.0001454287,0.0000685041,0.0001769587],"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":[0.000198854,0.00004903069,0.0003161943,0.0002972795,0.0002673454,0.00004825022,0.0005563695,0.9851418,0.001348901,0.005259287,0.0009235909,0.005593149],"study_design_scores_gemma":[0.0007920566,0.00004558815,0.0001652611,0.00001625757,0.0000386596,0.000008473756,0.0000258024,0.9950184,0.001233293,0.0001817892,0.00228352,0.0001908536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1155666,0.0007011333,0.8808489,0.000831058,0.0001818698,0.0003435641,0.00009375685,0.0005358424,0.0008973168],"genre_scores_gemma":[0.5210456,0.0000164068,0.4781826,0.000381334,0.0001930218,0.0000131886,0.0000936987,0.00004101094,0.00003314951],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.405479,"threshold_uncertainty_score":0.5585665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03339619471410213,"score_gpt":0.2507187593996895,"score_spread":0.2173225646855874,"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."}}