{"id":"W2103088687","doi":"10.1111/j.1475-3995.2012.00844.x","title":"Aggregate planning through the imprecise goal programming model: integration of the manager's preferences","year":2012,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Aggregate planning; Aggregate (composite); Time horizon; Computer science; Production planning; Production (economics); Operations research; Goal programming; Plan (archaeology); Set (abstract data type); Range (aeronautics); Industrial engineering; Mathematical optimization; Economics; Engineering; Mathematics; Microeconomics","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":[0.00257564,0.0006895883,0.0008675539,0.0005100783,0.0003768909,0.002266953,0.00134354,0.001150787,0.001640815],"category_scores_gemma":[0.004036544,0.0005761646,0.0007717233,0.001363191,0.001023666,0.002058023,0.001304993,0.001953035,0.0001954381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001388721,"about_ca_system_score_gemma":0.001228602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005735144,"about_ca_topic_score_gemma":0.005249244,"domain_scores_codex":[0.9980775,0.001082834,0.00006394923,0.0002076418,0.0003973724,0.0001706891],"domain_scores_gemma":[0.9980322,0.0013205,0.0002723545,0.0001035019,0.000172127,0.00009939229],"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":[0.00002792815,0.00001932837,0.0002253194,0.00002620646,0.00002207616,0.000088309,0.000101513,0.9517263,0.0002867801,0.04183027,0.0002986302,0.005347291],"study_design_scores_gemma":[0.000005816491,0.00002117172,0.0000881037,0.000008053372,0.000009329156,0.00001643798,0.00003100072,0.9814648,0.000124713,0.01767754,0.0005458926,0.000007155539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02553246,0.0001768749,0.9681287,0.0006026073,0.00002047074,0.00003653541,0.00009768892,0.00006823156,0.00533645],"genre_scores_gemma":[0.8227623,0.0004804381,0.1727414,0.0001636745,0.00005143424,0.0001934425,0.0001084302,0.00004321624,0.003455658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005735144,"threshold_uncertainty_score":0.01362145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1083537359267395,"score_gpt":0.3908507517331907,"score_spread":0.2824970158064513,"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."}}