{"id":"W4399681246","doi":"10.5267/j.ijiec.2024.3.001","title":"A novel hybrid algorithm of genetic algorithm, variable neighborhood search and constraint programming for distributed flexible job shop scheduling problem","year":2024,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Shandong Province; Liaocheng University; National Natural Science Foundation of China","keywords":"Algorithm; Variable neighborhood search; Computer science; Job shop scheduling; Hybrid algorithm (constraint satisfaction); Constraint programming; Mathematical optimization; Scheduling (production processes); Variable (mathematics); Constraint logic programming; Metaheuristic; Mathematics; Stochastic programming; Schedule","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":[0.0006257473,0.00105149,0.001164515,0.0008450252,0.0006214962,0.0007644792,0.001414301,0.001023673,0.001430263],"category_scores_gemma":[0.001030973,0.000434198,0.0008133193,0.001359164,0.000413483,0.0008311844,0.0009263927,0.0009271632,0.0002364632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008460037,"about_ca_system_score_gemma":0.002173113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01205583,"about_ca_topic_score_gemma":0.008951198,"domain_scores_codex":[0.9994103,0.0001542688,0.00002428217,0.0001312267,0.0002004751,0.0000794075],"domain_scores_gemma":[0.9997558,0.0001109462,0.00002796072,0.00001723226,0.00006530878,0.00002270242],"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":[0.00006188363,0.00006681829,0.0004947554,0.00009537783,0.00006583842,0.00009950153,0.00004809373,0.8780923,0.0019267,0.01400312,0.003015395,0.1020302],"study_design_scores_gemma":[0.00001773491,0.00002550288,0.00005701395,0.000004791083,0.000007599272,0.00002774245,0.000006677494,0.9963545,0.0002499713,0.002120786,0.001122714,0.000004931923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01201158,0.0005346574,0.9821537,0.0001725302,0.0000734452,0.00007443658,0.0000425327,0.0003011162,0.004636017],"genre_scores_gemma":[0.3870372,0.0007709438,0.6055673,0.0002604711,0.00008948977,0.0005313663,0.000350723,0.0001222772,0.005270092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01205583,"threshold_uncertainty_score":0.02397126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02386977824688337,"score_gpt":0.265703349061134,"score_spread":0.2418335708142506,"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."}}