{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008741915,0.0006166304,0.0008627291,0.0009720701,0.0005025766,0.0006175257,0.001079942,0.0007854787,0.0007668179],"category_scores_gemma":[0.002108515,0.0004063906,0.0004978281,0.001465178,0.0005930917,0.000748177,0.0003891514,0.0004862995,0.0001498924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001331463,"about_ca_system_score_gemma":0.001305338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008234906,"about_ca_topic_score_gemma":0.006959076,"domain_scores_codex":[0.9995535,0.0002632995,0.00001510537,0.00003593505,0.00008584985,0.00004637405],"domain_scores_gemma":[0.9993628,0.0004251436,0.00007523768,0.0000429437,0.00006279514,0.00003113002],"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":[0.00004894331,0.00003639382,0.0002062751,0.00002873382,0.00002207186,0.00002380489,0.00002819983,0.9654415,0.0003951527,0.004617099,0.0004632784,0.0286886],"study_design_scores_gemma":[0.00002066364,0.00003250235,0.00008386502,0.000003472265,0.00000828439,0.00001182995,0.000009416459,0.9959039,0.0002828402,0.003187164,0.0004516122,0.000004440467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1442318,0.001930456,0.8465071,0.0003192103,0.00009898934,0.0001667153,0.0001371287,0.000911048,0.005697579],"genre_scores_gemma":[0.6495554,0.0007285244,0.3464199,0.0001054932,0.00004203173,0.0002965154,0.0002362849,0.0001386026,0.002477225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008234906,"threshold_uncertainty_score":0.01637399,"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."}}