{"id":"W4414748284","doi":"10.1038/s41598-025-17604-y","title":"An integrated TOPSIS and ARAS method multi-criteria decision-making approach for optimizing investment portfolios using goal programming and genetic algorithm model","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sharpe ratio; TOPSIS; Portfolio; Efficient frontier; Portfolio optimization; Asset allocation; Probabilistic logic; Project portfolio management; Investment strategy; Genetic algorithm","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":[0.00324175,0.001724347,0.001466004,0.002781524,0.0007076946,0.002514316,0.001766318,0.001033579,0.003688063],"category_scores_gemma":[0.002971959,0.0006495026,0.002370906,0.002434839,0.0006047232,0.001281785,0.001533442,0.001539961,0.0007777439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001374344,"about_ca_system_score_gemma":0.002466325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005069429,"about_ca_topic_score_gemma":0.004904842,"domain_scores_codex":[0.9969726,0.001321605,0.0001831298,0.0003395742,0.001055697,0.0001273417],"domain_scores_gemma":[0.9988411,0.000549914,0.0001195981,0.00006737439,0.0003723687,0.00004978186],"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":[0.00008602047,0.0001500986,0.001249304,0.0005330774,0.0004581809,0.0002323346,0.0002793504,0.6933714,0.004206957,0.04495391,0.002644279,0.2518352],"study_design_scores_gemma":[0.00001618908,0.0001013274,0.0002428588,0.00004342251,0.00005481869,0.00005869549,0.00005493848,0.9808998,0.0008881305,0.0149775,0.002638211,0.0000241904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003754306,0.0002116754,0.9915193,0.0001443962,0.00002854477,0.0001255065,0.00005739955,0.0003220872,0.003836792],"genre_scores_gemma":[0.206185,0.0003913458,0.7896653,0.0001138772,0.00004004226,0.0005715836,0.0002097296,0.00007665253,0.002746423],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005069429,"threshold_uncertainty_score":0.0171442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02571969503717883,"score_gpt":0.3299792510054088,"score_spread":0.30425955596823,"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."}}