{"id":"W4406723220","doi":"10.1061/jccee5.cpeng-6042","title":"Automating Pipe Spool Fabrication Shop Scheduling for Modularized Industrial Construction Projects Using Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Scheduling (production processes); Engineering; Construction management; Computer science; Reinforcement learning; Fabrication; Construction engineering; Manufacturing engineering; Systems engineering; Civil engineering; Artificial intelligence; Operations management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008793121,0.0006867782,0.0007498954,0.0004406369,0.0003205641,0.0005346138,0.00114356,0.0006799488,0.001414792],"category_scores_gemma":[0.002348198,0.0004824605,0.0005088089,0.0003931983,0.0004501177,0.0008129296,0.0005959308,0.001434477,0.0002994242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001472418,"about_ca_system_score_gemma":0.002269371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0215619,"about_ca_topic_score_gemma":0.02666668,"domain_scores_codex":[0.9996419,0.00009744514,0.00001902214,0.0001189636,0.00005375861,0.00006885675],"domain_scores_gemma":[0.9987642,0.000783533,0.000154402,0.00006483605,0.0001339555,0.00009911295],"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":[0.0001013051,0.0001299591,0.00278604,0.00004255553,0.00002200867,0.00003672037,0.00002729326,0.9512876,0.0007985666,0.0006443372,0.00073906,0.04338456],"study_design_scores_gemma":[0.000004334715,0.00001179019,0.0001489956,0.000001313415,0.000001625341,0.000001896151,0.000002691637,0.9992805,0.0001568743,0.0003199014,0.00006879628,0.000001351423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3309106,0.0006880834,0.6618538,0.0006127012,0.0000822646,0.0001406078,0.0003763097,0.002378519,0.002957126],"genre_scores_gemma":[0.9317218,0.000101967,0.06641502,0.00009187763,0.00001467314,0.00006591086,0.0003993183,0.00004759508,0.001141868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0215619,"threshold_uncertainty_score":0.04287279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02045016525064404,"score_gpt":0.2509709241220959,"score_spread":0.2305207588714519,"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."}}