{"id":"W2059572074","doi":"10.1142/s021759590500056x","title":"PERMUTATION-BASED GENETIC, TABU, AND VARIABLE NEIGHBORHOOD SEARCH HEURISTICS FOR MULTIPROCESSOR SCHEDULING WITH COMMUNICATION DELAYS","year":2005,"lang":"en","type":"article","venue":"Asia Pacific Journal of Operational Research","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis","funders":"Science and Engineering Research Board; Serbian Academy of Sciences and Arts; National Science Foundation","keywords":"Computer science; Multiprocessor scheduling; Heuristics; Tabu search; Multiprocessing; Permutation (music); Scheduling (production processes); Parallel computing; Permutation matrix; Schedule; Job shop scheduling; Variable neighborhood search; Mathematical optimization; Metaheuristic; Theoretical computer science; Algorithm; Mathematics; Flow shop scheduling","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002906944,0.0001144344,0.00018463,0.0002381549,0.0006175055,0.000666189,0.0006910259,0.00006448309,0.000009490196],"category_scores_gemma":[0.0003951485,0.00009278528,0.00003462052,0.0004723554,0.0001082505,0.0004591997,0.00007301663,0.0004124828,0.000006479834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000965479,"about_ca_system_score_gemma":0.001240461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009812067,"about_ca_topic_score_gemma":0.000003432598,"domain_scores_codex":[0.9977063,0.0003129008,0.0004566765,0.0002266971,0.0009717153,0.0003257117],"domain_scores_gemma":[0.9948928,0.001227284,0.0001390411,0.0002864876,0.003285585,0.0001688136],"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":[0.0002270113,0.0003484144,0.004515181,0.0001186921,0.0001049125,0.00001376663,0.001772204,0.8630346,0.001964855,0.1155596,0.001150019,0.01119071],"study_design_scores_gemma":[0.001256646,0.0004138104,0.00168857,0.0001337639,0.000007279237,0.0001448069,0.0004122842,0.9912946,0.0003713789,0.0009692592,0.003179702,0.0001279435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02130835,0.000829538,0.972962,0.003597461,0.00003822058,0.0003105321,0.00001574221,0.00001301046,0.0009251599],"genre_scores_gemma":[0.6734899,0.0000246014,0.3262127,0.00002611282,0.000133922,0.00001277586,0.00001711873,0.00000850895,0.00007440953],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6521815,"threshold_uncertainty_score":0.6424076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04156626148663788,"score_gpt":0.3300635002312745,"score_spread":0.2884972387446366,"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."}}