{"id":"W4296830597","doi":"10.3390/pr10101908","title":"Development of an Improved Water Cycle Algorithm for Solving an Energy-Efficient Disassembly-Line Balancing Problem","year":2022,"lang":"en","type":"article","venue":"Processes","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Metaheuristic; Mathematical optimization; Computer science; Solver; Algorithm; Sensitivity (control systems); Assembly line; Energy consumption; Efficient energy use; Reducer; Remanufacturing; Engineering; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004548673,0.0008302112,0.0007286685,0.0008440173,0.000434967,0.0006332539,0.001007768,0.001114805,0.003016786],"category_scores_gemma":[0.001164258,0.0003472164,0.0006464275,0.0008775896,0.0003170971,0.000762957,0.0005880545,0.0007700224,0.0003983724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006248209,"about_ca_system_score_gemma":0.001677254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007890913,"about_ca_topic_score_gemma":0.007365745,"domain_scores_codex":[0.9997452,0.00005607462,0.00001509825,0.0000575827,0.00008317799,0.00004277575],"domain_scores_gemma":[0.9997545,0.0001167558,0.00002299755,0.00001708394,0.00007605998,0.00001261567],"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":[0.00004158472,0.00004946608,0.0003250291,0.00005142506,0.00001587756,0.00003141366,0.00002749272,0.9287116,0.002185767,0.005417371,0.0008818362,0.0622612],"study_design_scores_gemma":[0.00001039776,0.00001276059,0.00003411044,0.000002333459,0.000002363515,0.000005410713,0.000004232184,0.9984181,0.0003795806,0.00066553,0.0004634172,0.000001864499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01598131,0.0001481167,0.9798943,0.00009985101,0.00003197731,0.00007926106,0.00004739841,0.0003170866,0.003400613],"genre_scores_gemma":[0.2498427,0.0002325311,0.7460263,0.0001107794,0.00003402805,0.0003910176,0.0002891822,0.0001402412,0.002933163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007890913,"threshold_uncertainty_score":0.01568997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007585022086025888,"score_gpt":0.2096171748970472,"score_spread":0.2020321528110214,"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."}}