{"id":"W6891661904","doi":"10.4230/lipics.cp.2024.30","title":"Learning Precedences for Scheduling Problems with Graph Neural Networks","year":2024,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Resource-Constrained Project Scheduling","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; European Commission; Institut de Valorisation des Données; Polytechnique Montréal","keywords":"Leverage (statistics); Scheduling (production processes); Artificial neural network; Graph; Job shop scheduling; Dynamic priority scheduling","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001323114,0.001440254,0.0007967087,0.001381407,0.0004910703,0.0009577505,0.001086902,0.001173812,0.002629001],"category_scores_gemma":[0.008016641,0.000790746,0.0007599631,0.001324739,0.0006953073,0.001828848,0.0009612142,0.002277741,0.0002621913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001975083,"about_ca_system_score_gemma":0.001924548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01639193,"about_ca_topic_score_gemma":0.02141243,"domain_scores_codex":[0.9995298,0.0001698416,0.00003690857,0.0001202748,0.00007749013,0.00006566325],"domain_scores_gemma":[0.9962963,0.002956369,0.0002812152,0.00009485171,0.0002600815,0.0001110813],"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":[0.00003266257,0.00003525,0.000385946,0.00003869997,0.00001073306,0.00001542806,0.00001252237,0.9735931,0.0001469825,0.003460088,0.0004887721,0.02177983],"study_design_scores_gemma":[0.000003992036,0.000004594113,0.00002914616,0.000003501189,0.000001730765,0.000001653695,0.000002059554,0.9949806,0.00005120734,0.004847409,0.00007308878,0.000001014638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06810457,0.001227679,0.9234743,0.0009831357,0.000130393,0.0001291438,0.0004091688,0.0009524599,0.004589071],"genre_scores_gemma":[0.7310718,0.001067671,0.2619336,0.0003884787,0.0001345012,0.0003266941,0.001126532,0.0001545133,0.003796228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01639193,"threshold_uncertainty_score":0.03259307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03480361908635707,"score_gpt":0.3029718832723857,"score_spread":0.2681682641860287,"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."}}