{"id":"W4308799544","doi":"10.1016/j.asoc.2022.109791","title":"Mathematical modeling of Vehicle Routing Problem in Omni-Channel retailing","year":2022,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"University of Tehran","keywords":"Routing (electronic design automation); Computer science; Heuristic; Channel (broadcasting); Pareto principle; Product (mathematics); Function (biology); Vehicle routing problem; Distribution (mathematics); Operations research; Mathematical optimization; Computer network; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005136066,0.0001550445,0.0002972049,0.000104482,0.0001282817,0.00001246164,0.0002022774,0.00005037837,0.00004678707],"category_scores_gemma":[0.00000957284,0.0001845944,0.00005351102,0.0003032917,0.00002404706,0.00002702267,0.00009904063,0.0004488351,0.000008478124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008383863,"about_ca_system_score_gemma":0.00001641794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009303315,"about_ca_topic_score_gemma":0.000001549294,"domain_scores_codex":[0.998615,0.0000148459,0.000572384,0.0002059121,0.0002310593,0.0003608375],"domain_scores_gemma":[0.9996014,0.0001132992,0.00005457915,0.0001695552,0.00001619592,0.0000449716],"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":[0.000007381844,0.0000369211,0.0001794603,0.0001497879,0.00001466306,0.00000774353,0.001507125,0.9849824,0.001126865,0.01081498,0.000006354503,0.001166321],"study_design_scores_gemma":[0.0003212941,0.00001359145,0.0000213264,0.00004137777,0.00001123009,0.000003849778,0.0005992276,0.9923332,0.0002179866,0.00623415,0.00001764734,0.0001851254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3098657,0.0001428744,0.6673007,0.000008531336,0.00007852547,0.0002634944,0.000004976599,0.0003632639,0.02197189],"genre_scores_gemma":[0.9845565,0.00000134643,0.01530532,0.00001724573,0.00003817619,0.00001316503,0.00001037235,0.00004755832,0.00001034505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6746907,"threshold_uncertainty_score":0.7527542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02272546618253651,"score_gpt":0.1905539200416689,"score_spread":0.1678284538591324,"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."}}