{"id":"W2148717184","doi":"10.1007/978-3-540-24840-8_59","title":"Scheduling Using Constraint-Directed Search","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Job shop scheduling; Computer science; Constraint satisfaction problem; Schedule; Mathematical optimization; Scheduling (production processes); Constraint satisfaction; Constraint programming; Constraint satisfaction dual problem; Constraint (computer-aided design); Local consistency; Mathematics; 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":[0.0009091197,0.001305489,0.001428083,0.001210264,0.0008096687,0.001452563,0.002260019,0.001142468,0.01168174],"category_scores_gemma":[0.002442475,0.0009491,0.0009336549,0.002597787,0.000608797,0.001220223,0.00108139,0.001486222,0.002113075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001469023,"about_ca_system_score_gemma":0.002581561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00954889,"about_ca_topic_score_gemma":0.01362549,"domain_scores_codex":[0.9994577,0.0001685571,0.00002830322,0.000103487,0.0001759531,0.00006591518],"domain_scores_gemma":[0.999022,0.0006220295,0.00005317629,0.0001334997,0.0001225409,0.00004664537],"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":[0.000109947,0.0001623578,0.0001653219,0.0003741253,0.00008383743,0.00006692024,0.00005722371,0.7156834,0.002044979,0.04625909,0.02031774,0.214675],"study_design_scores_gemma":[0.00003568198,0.00002114726,0.00003832636,0.0000224546,0.00001554366,0.00001923524,0.00001124844,0.9599019,0.0009510866,0.03342013,0.00555468,0.000008586178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003112408,0.0004505111,0.9800197,0.0001764363,0.0001044848,0.0001584339,0.0002721469,0.001639071,0.01406689],"genre_scores_gemma":[0.1003467,0.0008816095,0.8854979,0.0002171808,0.00007277587,0.0004540319,0.001129895,0.0005872544,0.01081257],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01168174,"threshold_uncertainty_score":0.03907931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.023723774562969,"score_gpt":0.2514761038039608,"score_spread":0.2277523292409918,"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."}}