{"id":"W3192568905","doi":"10.1016/j.trc.2021.103304","title":"Social welfare maximizing fleet charging scheduling through voting-based negotiation","year":2021,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Negotiation; Voting; Social Welfare; Schedule; Computer science; Scheduling (production processes); Operations research; Welfare; Business; Microeconomics; Economics; Operations management; Engineering","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":[0.003181585,0.0007541336,0.002248453,0.0006506795,0.001201021,0.002194889,0.002932459,0.001629756,0.00675818],"category_scores_gemma":[0.008479509,0.0006762523,0.0009528851,0.001112365,0.001077101,0.002419541,0.002007731,0.00160855,0.0006147361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001193163,"about_ca_system_score_gemma":0.001482104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001893715,"about_ca_topic_score_gemma":0.001907067,"domain_scores_codex":[0.9978009,0.001068656,0.00008090082,0.0003253293,0.000313955,0.0004103251],"domain_scores_gemma":[0.9959959,0.002882513,0.0002475974,0.0002436577,0.0003145521,0.0003158634],"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":[0.0006537847,0.0002660758,0.0007021618,0.0001330952,0.00008444746,0.0001606458,0.0002081357,0.8783912,0.003176738,0.06785963,0.003834703,0.04452946],"study_design_scores_gemma":[0.00002770523,0.00003378435,0.00005586601,0.000003828782,0.000007406653,0.00001473794,0.00002584345,0.9844481,0.0002374032,0.01483511,0.0003045769,0.000005647009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07546964,0.0001537474,0.9097065,0.0005196446,0.0001303716,0.0002259161,0.0001066464,0.0002184969,0.01346909],"genre_scores_gemma":[0.9419783,0.00007436241,0.05207293,0.00008795086,0.00005292653,0.0001431992,0.00008874208,0.0000648248,0.005436747],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00675818,"threshold_uncertainty_score":0.0226084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07979171417355621,"score_gpt":0.3521396327058323,"score_spread":0.2723479185322761,"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."}}