{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001762954,0.0009493311,0.002160317,0.001315886,0.0007437507,0.001038666,0.002960289,0.002038881,0.004360852],"category_scores_gemma":[0.003857631,0.0009281916,0.001235178,0.001158565,0.0009554497,0.001424329,0.001387525,0.001267853,0.0006221409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00128662,"about_ca_system_score_gemma":0.00171868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006750838,"about_ca_topic_score_gemma":0.006705343,"domain_scores_codex":[0.9991986,0.0002933149,0.0000353388,0.00009765222,0.0002467231,0.0001283787],"domain_scores_gemma":[0.9981521,0.001164996,0.0001505564,0.0001078726,0.0003068859,0.0001176581],"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":[0.0001758391,0.0001763168,0.0002900817,0.0001102479,0.00005225198,0.000119185,0.0001134056,0.9292717,0.001548582,0.005357047,0.001631388,0.061154],"study_design_scores_gemma":[0.00002861648,0.00005005627,0.00004330377,0.000007801436,0.0000136989,0.0000136413,0.00001249201,0.9983945,0.0002475597,0.0008752978,0.0003063565,0.000006560264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04868378,0.0006344042,0.9413729,0.0002306477,0.0001375845,0.0002122853,0.00005338733,0.001003242,0.00767174],"genre_scores_gemma":[0.4717933,0.0003004557,0.5204991,0.0002078399,0.00009342624,0.0005206884,0.0001694917,0.000312912,0.006102719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006750838,"threshold_uncertainty_score":0.01458853,"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."}}