{"id":"W4224298152","doi":"10.1007/s10479-022-04661-z","title":"Sustainable closed-loop supply chain with energy efficiency: Lagrangian relaxation, reformulations and heuristics","year":2022,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"University of Melbourne","keywords":"Supply chain; Lagrangian relaxation; Heuristics; Remanufacturing; Computer science; Profit (economics); Environmental economics; Operations research; Supply chain management; Theory of computation; Mathematical optimization; Business; Economics; Manufacturing engineering; Microeconomics; Marketing; Mathematics; Engineering","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":[0.002444809,0.00102053,0.001260319,0.001066201,0.0006153241,0.001880677,0.001130418,0.001601718,0.003121756],"category_scores_gemma":[0.005137476,0.0007530836,0.001067571,0.001595678,0.001483591,0.001542928,0.001282122,0.001732814,0.0003122358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001982576,"about_ca_system_score_gemma":0.002175847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009293496,"about_ca_topic_score_gemma":0.005945018,"domain_scores_codex":[0.9987674,0.0007602,0.00002994827,0.00009990167,0.0001680788,0.0001744561],"domain_scores_gemma":[0.9971808,0.002236617,0.000217432,0.00007557468,0.0002123542,0.00007725806],"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":[0.00002249004,0.00003694948,0.00008621873,0.00005612281,0.00001050494,0.00003966926,0.00002787721,0.9809,0.0001057266,0.01347634,0.0004592959,0.004778834],"study_design_scores_gemma":[0.00001429012,0.00002467506,0.00002881879,0.00001892177,0.000005646624,0.000007395342,0.00003038154,0.9875422,0.000113556,0.01171325,0.0004965373,0.00000431839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02999193,0.001151643,0.9534422,0.000633351,0.00006630754,0.0001973444,0.0001568214,0.0001438456,0.01421652],"genre_scores_gemma":[0.6596766,0.001500963,0.3320368,0.0002133792,0.0000932469,0.0004828448,0.0003134092,0.0001088445,0.005573967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009293496,"threshold_uncertainty_score":0.01847881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04350973340699243,"score_gpt":0.3126777133944018,"score_spread":0.2691679799874094,"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."}}