{"id":"W4410899693","doi":"10.5267/j.dsl.2025.5.001","title":"Multi-period supply chain optimization with contango and backwardation effects using an improved hybrid genetic algorithm","year":2025,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Genetic algorithm; Mathematical optimization; Period (music); Supply chain; Computer science; Algorithm; Mathematics; Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009841204,0.0007648953,0.001023078,0.0006881292,0.0004649085,0.0008575084,0.001003866,0.00112944,0.001277562],"category_scores_gemma":[0.001396793,0.0005566831,0.0008947537,0.000850725,0.0005082199,0.0006566842,0.0008347124,0.0008423726,0.0001284321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008575243,"about_ca_system_score_gemma":0.001399983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01221266,"about_ca_topic_score_gemma":0.007801237,"domain_scores_codex":[0.9996624,0.0001331036,0.00001486774,0.00005975852,0.00007916882,0.00005076933],"domain_scores_gemma":[0.9994621,0.0003390458,0.00006481591,0.00002748958,0.00008195032,0.00002461853],"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":[0.00001855227,0.00001408146,0.0001779669,0.000009209713,0.00001775351,0.00001869102,0.00001324255,0.9929045,0.0002776909,0.0009569412,0.00008687579,0.005504485],"study_design_scores_gemma":[0.000004225648,0.00001057848,0.00003031146,0.000001270925,0.000003012651,0.000002336475,0.000001512386,0.9994931,0.00004962295,0.0003435072,0.00005920687,0.000001367344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06595596,0.0004093494,0.9284943,0.000242909,0.00004981036,0.00007327208,0.00006007966,0.0002774465,0.004436744],"genre_scores_gemma":[0.8056884,0.0002363573,0.1909234,0.0001061967,0.00002503134,0.0002103557,0.0001094801,0.00005702299,0.002643743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01221266,"threshold_uncertainty_score":0.02428317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007766404146318961,"score_gpt":0.2434272556922142,"score_spread":0.2356608515458953,"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."}}