{"id":"W2905262274","doi":"10.1016/j.cor.2018.12.008","title":"Reformulation, linearization, and a hybrid iterated local search algorithm for economic lot-sizing and sequencing in hybrid flow shop problems","year":2018,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Windsor","funders":"","keywords":"Iterated local search; Mathematical optimization; Sizing; Linearization; Local search (optimization); Iterated function; Algorithm; Computer science; Local optimum; Integer programming; Time horizon; Mathematics; Nonlinear 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001526336,0.0007589828,0.001089726,0.0005759553,0.0005039229,0.00085268,0.001421853,0.001025295,0.003139123],"category_scores_gemma":[0.002753182,0.0005134768,0.0007626499,0.0005510855,0.001184832,0.001329099,0.001231308,0.001294993,0.0004715511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008464302,"about_ca_system_score_gemma":0.00119729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004408449,"about_ca_topic_score_gemma":0.004191634,"domain_scores_codex":[0.9994926,0.0002827762,0.00001721012,0.00004928077,0.0001133481,0.00004476901],"domain_scores_gemma":[0.9990523,0.0006342729,0.00006442497,0.00006016055,0.0001540563,0.00003467461],"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":[0.00007049457,0.00008681307,0.0001636951,0.0000877946,0.00002197701,0.00004360259,0.00008942184,0.93393,0.001501682,0.03794903,0.0009943257,0.02506115],"study_design_scores_gemma":[0.00001172573,0.00002800891,0.0000202401,0.000003237873,0.000004153774,0.000003772259,0.000007397031,0.9960201,0.0002133165,0.003497119,0.0001871515,0.000003723138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01951311,0.0001525076,0.9756913,0.0001650312,0.00003263001,0.00006492199,0.00002508685,0.0001757095,0.004179676],"genre_scores_gemma":[0.6489711,0.0002089875,0.3443221,0.0001386492,0.00008377797,0.0003842055,0.0001182992,0.000182057,0.005590774],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004408449,"threshold_uncertainty_score":0.01050138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0403622500725725,"score_gpt":0.3049598838086648,"score_spread":0.2645976337360924,"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."}}