{"id":"W4318604517","doi":"10.1109/ssci51031.2022.10022250","title":"A Constraint Satisfaction Problem (CSP) Approach for the Nurse Scheduling Problem","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Mathematical optimization; Heuristics; Constraint satisfaction problem; Constraint satisfaction; Computer science; Nurse scheduling problem; Constraint programming; Job shop scheduling; Scheduling (production processes); Constraint satisfaction dual problem; Constraint (computer-aided design); Integer programming; Branch and bound; Linear programming; Combinatorial optimization; Constraint logic programming; Mathematics; Artificial intelligence; Routing (electronic design automation); 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":[0.001957148,0.001957766,0.001049676,0.001207486,0.0009202299,0.00153973,0.002081365,0.001639175,0.00532696],"category_scores_gemma":[0.00511501,0.0005975842,0.001923904,0.003794577,0.001053029,0.001660779,0.001390513,0.003862244,0.0009798991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001466139,"about_ca_system_score_gemma":0.004429433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007211177,"about_ca_topic_score_gemma":0.008185652,"domain_scores_codex":[0.996696,0.001579934,0.0001645039,0.000497589,0.0008988177,0.0001630593],"domain_scores_gemma":[0.9972376,0.001903079,0.0002082625,0.0001588261,0.0004039058,0.00008828867],"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":[0.00006981046,0.0002205156,0.000501458,0.0008054834,0.0001337689,0.0003434018,0.0001864446,0.7044852,0.002591327,0.1692889,0.01321782,0.1081558],"study_design_scores_gemma":[0.00004075316,0.00009210591,0.0001554949,0.00008393612,0.00003578809,0.0002290675,0.00008353819,0.9182563,0.001254485,0.0598119,0.01993206,0.00002451845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001272031,0.0003367321,0.9935509,0.0005170836,0.00007476669,0.0001563844,0.0002079889,0.00009258244,0.003791451],"genre_scores_gemma":[0.05765984,0.001508529,0.93588,0.0003938706,0.0002013414,0.0007316535,0.0006939289,0.00009232984,0.002838536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007211177,"threshold_uncertainty_score":0.01782042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08505266767852177,"score_gpt":0.3530456267598732,"score_spread":0.2679929590813514,"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."}}