{"id":"W2018407890","doi":"10.1016/j.eswa.2013.03.037","title":"An iterated local search heuristic for multi-capacity bin packing and machine reassignment problems","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Bin packing problem; Mathematical optimization; Benchmark (surveying); Iterated local search; Metaheuristic; Computer science; Packing problems; Scheduling (production processes); Heuristic; Generalization; Iterated function; Upper and lower bounds; Job shop scheduling; Bin; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001358458,0.00017895,0.0001785025,0.000081388,0.0001891294,0.0001972999,0.0001096521,0.00007607823,0.00001947324],"category_scores_gemma":[0.000003215395,0.0001472489,0.00001678759,0.0001880166,0.00005807439,0.0001717218,0.00001050976,0.0001002242,0.00002587441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006841919,"about_ca_system_score_gemma":0.00001341567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00043498,"about_ca_topic_score_gemma":0.00003103783,"domain_scores_codex":[0.9990528,0.00003823685,0.0002586678,0.0002690431,0.000129244,0.0002519988],"domain_scores_gemma":[0.9992881,0.00004294653,0.000039718,0.0003079637,0.0001405786,0.0001806286],"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":[0.00001293019,0.0003048926,0.001820998,0.0009139613,0.0001369426,5.593703e-7,0.006195456,0.9600126,0.01510716,0.004331816,0.001404729,0.009757941],"study_design_scores_gemma":[0.0005642327,0.00005994055,0.0002480472,0.0000860214,0.000006456172,0.00001332952,0.0002578727,0.9913531,0.0004196377,0.0000152615,0.006753202,0.0002229238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00271181,0.000589838,0.9931818,0.00006961641,0.00005119862,0.002794851,0.00004191523,0.0003904737,0.000168525],"genre_scores_gemma":[0.9671525,0.00004056256,0.02615891,0.00003130431,0.00007485361,0.006207644,0.0001343394,0.00005973026,0.0001401304],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9670228,"threshold_uncertainty_score":0.6004637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04250410525338499,"score_gpt":0.2585666167977702,"score_spread":0.2160625115443852,"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."}}