{"id":"W4229068799","doi":"10.1016/j.trc.2022.103677","title":"Crowdshipping: An open VRP variant with stochastic destinations","year":2022,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Secretaría de Educación Superior, Ciencia, Tecnología e Innovación; Polytechnique Montréal","keywords":"Vehicle routing problem; Destinations; Transport engineering; Computer science; Travelling salesman problem; Operations research; Engineering; Mathematical optimization; Geography; Mathematics; Routing (electronic design automation); Algorithm; Computer network; Tourism","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":[0.001117207,0.0008988586,0.001363118,0.0004420031,0.0008159741,0.001470372,0.003764423,0.00228044,0.006092027],"category_scores_gemma":[0.00480886,0.0004020799,0.0008888346,0.0009635349,0.001128632,0.001537937,0.003528307,0.001705567,0.0006781381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006785522,"about_ca_system_score_gemma":0.001222278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009428992,"about_ca_topic_score_gemma":0.007470325,"domain_scores_codex":[0.9990141,0.0002407898,0.0000358583,0.0002447033,0.000222722,0.0002418567],"domain_scores_gemma":[0.99829,0.0007342376,0.00008854485,0.0003768534,0.0002701793,0.0002402656],"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":[0.0005936609,0.0002209381,0.0005983065,0.00009077336,0.00005694337,0.0003525904,0.0001153249,0.8763071,0.003633886,0.05493731,0.00619002,0.05690309],"study_design_scores_gemma":[0.00002924516,0.00003728937,0.000048872,0.000003237136,0.00000508672,0.00002562976,0.0000150763,0.9916408,0.0002632877,0.007088558,0.0008345122,0.000008316019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09335922,0.0003174316,0.8891472,0.0006121044,0.0006021111,0.0001361532,0.0005088464,0.001651996,0.01366493],"genre_scores_gemma":[0.8555296,0.0001119069,0.1339345,0.000175849,0.0001304718,0.00008515848,0.0004797772,0.0002529822,0.009299776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009428992,"threshold_uncertainty_score":0.0203799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09971195842149394,"score_gpt":0.3615742614099143,"score_spread":0.2618623029884204,"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."}}