{"id":"W7117260949","doi":"10.5267/j.ijiec.2025.12.004","title":"Research on integrated optimization of order allocation and lotsizing sequencing for mixed-model parallel assembly lines using improved intelligent optimization algorithm","year":2025,"lang":"","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Assembly Line Balancing Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mass customization; Flexibility (engineering); Production (economics); Minification; Build to order; Automotive industry; Production line; Multi-objective optimization","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001886574,0.000477432,0.0007113466,0.00300634,0.0002115507,0.0003387466,0.000555147,0.0005406084,0.00000613884],"category_scores_gemma":[0.002751437,0.0005438436,0.0001995445,0.00172438,0.00008564277,0.0007402977,0.0001316521,0.001048003,3.58229e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001754736,"about_ca_system_score_gemma":0.001596134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007017019,"about_ca_topic_score_gemma":0.0000045482,"domain_scores_codex":[0.9958499,0.0001681211,0.002290495,0.0004177195,0.0008277945,0.0004459544],"domain_scores_gemma":[0.9895635,0.001341248,0.0009307471,0.0002211869,0.007788061,0.0001552825],"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":[0.0002121013,0.0001442469,0.00001735412,0.0001158446,0.0007619874,0.00000313918,0.0003667216,0.9828669,0.003294555,0.001259531,0.00007937129,0.01087825],"study_design_scores_gemma":[0.002534978,0.0002732066,0.000006776605,0.002806293,0.0002275539,0.0000244948,0.0006873218,0.9894552,0.003475942,0.0001084283,0.00005049822,0.0003493277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007764508,0.0003534193,0.9860895,0.0004538551,0.004232754,0.0009097842,0.00009355685,0.00007056614,0.00003207932],"genre_scores_gemma":[0.3145451,0.0003379096,0.6840033,0.00002090344,0.0007306557,0.00002352404,0.0002230694,0.00009173111,0.00002379487],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3067806,"threshold_uncertainty_score":0.9997013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0658462642163103,"score_gpt":0.3475299368897076,"score_spread":0.2816836726733973,"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."}}