{"id":"W2001478667","doi":"10.1287/trsc.2014.0533","title":"Scheduling Twin Yard Cranes in a Container Block","year":2014,"lang":"en","type":"article","venue":"Transportation Science","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Heuristics; Block (permutation group theory); Scheduling (production processes); Computer science; Container (type theory); Heuristic; Yard; Mathematical optimization; Truck; Travelling salesman problem; Job shop scheduling; Stacking; Schedule; Engineering; Algorithm; Mathematics; Automotive engineering; Operating system","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":[0.0003559174,0.00006567804,0.00008664739,0.0001060503,0.00003996311,0.00002899404,0.0001142035,0.00002441352,0.00005794489],"category_scores_gemma":[0.00003068413,0.00006336147,0.0000153436,0.0003374773,0.00010002,0.000145519,0.000001024735,0.00007005174,0.00001206313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001532078,"about_ca_system_score_gemma":0.00002063679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007827198,"about_ca_topic_score_gemma":0.0004072588,"domain_scores_codex":[0.9993178,0.000004140389,0.0001820975,0.0001315561,0.0001858305,0.0001785685],"domain_scores_gemma":[0.9997644,0.00002665618,0.00001500831,0.00009748464,0.00004130314,0.00005512058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000007386526,0.00002893654,0.07170323,0.00009901835,0.000003339112,0.00001908845,0.001996485,0.8846431,0.0156799,0.01762286,0.00006104596,0.008135663],"study_design_scores_gemma":[0.0004560246,0.00002272639,0.6786185,0.00004294514,0.00000825831,0.000002119773,0.0001351442,0.3118376,0.003157745,0.000876195,0.004573625,0.0002691115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9606985,0.00002750427,0.032747,0.00006995557,0.000167537,0.0000713508,0.00000330851,0.0001143967,0.006100392],"genre_scores_gemma":[0.9972559,0.000006779259,0.002589844,0.00005833509,0.0000246447,0.000005476256,0.000004234339,0.000006524291,0.00004831413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6069153,"threshold_uncertainty_score":0.2583806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01053360702691465,"score_gpt":0.2270025886966646,"score_spread":0.21646898166975,"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."}}