{"id":"W4415314677","doi":"10.1016/j.omega.2025.103445","title":"Fleet size planning in crowdsourced delivery: Balancing service level and driver utilization","year":2025,"lang":"en","type":"article","venue":"Omega","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov decision process; Fleet management; Sizing; Service (business); Function (biology); Service level; Process (computing); Matching (statistics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002455136,0.000744441,0.0009505189,0.001053041,0.0008060699,0.001620195,0.00179834,0.001030576,0.003554848],"category_scores_gemma":[0.006820566,0.0007018336,0.0006078244,0.001030408,0.0005477601,0.002166727,0.00143122,0.0007756397,0.0004721572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002370171,"about_ca_system_score_gemma":0.002734154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02201706,"about_ca_topic_score_gemma":0.02514548,"domain_scores_codex":[0.9991065,0.0003347929,0.00003107177,0.0002025299,0.0001465389,0.0001786199],"domain_scores_gemma":[0.9968304,0.002133073,0.0001932754,0.0001679145,0.0003557097,0.0003197324],"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":[0.0004227553,0.0002686543,0.004733088,0.0001085274,0.00007691893,0.00009961913,0.0003085274,0.9166062,0.002412133,0.004556618,0.001901781,0.06850509],"study_design_scores_gemma":[0.00003148958,0.0001405169,0.002448659,0.00001760329,0.00003513011,0.00002380272,0.000404422,0.9903627,0.0008561303,0.004810556,0.0008487965,0.00002015994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.466285,0.000499084,0.5132579,0.001111958,0.0002052722,0.0005153716,0.0005046602,0.000782734,0.01683808],"genre_scores_gemma":[0.9757655,0.0000673602,0.02201333,0.000051644,0.00002290607,0.00008253601,0.0001120687,0.000066434,0.00181824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02201706,"threshold_uncertainty_score":0.04377782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02703332678432519,"score_gpt":0.2561021111952106,"score_spread":0.2290687844108855,"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."}}