{"id":"W2548380795","doi":"","title":"Production scheduling and routing problem in the textile industry","year":2013,"lang":"en","type":"article","venue":"Industrial Engineering and Systems Management (IESM), Proceedings of 2013 International Conference on","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Column generation; Scheduling (production processes); Solver; Integer programming; Linear programming; Mathematical optimization; Computer science; Job shop scheduling; Routing (electronic design automation); Algorithm; Mathematics; Embedded 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007695386,0.0001853019,0.0001927209,0.000286727,0.00004452976,0.00027783,0.0002159252,0.0001891507,0.00001219388],"category_scores_gemma":[0.0001052319,0.0001613408,0.00001846015,0.0002138357,0.00002889812,0.0003872751,0.00006022235,0.0005073658,0.000002835036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005444679,"about_ca_system_score_gemma":0.000006205096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006704448,"about_ca_topic_score_gemma":3.922617e-7,"domain_scores_codex":[0.9988408,0.00001249179,0.0004190798,0.000234919,0.0002919242,0.0002007815],"domain_scores_gemma":[0.9995884,0.00003764425,0.0001143183,0.00007174765,0.0001464456,0.00004141492],"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":[0.00002153341,0.00006079893,0.008180399,0.000622425,0.0002642974,0.000002161928,0.001539991,0.8157558,0.006529014,0.1554299,0.002071156,0.009522491],"study_design_scores_gemma":[0.0005828771,0.00004977456,0.002131475,0.001158427,0.00002294283,0.00001460168,0.004566576,0.9901222,0.000293301,0.0001645891,0.0006299103,0.0002633311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9784173,0.00005955792,0.0007322694,0.0009991792,0.0008141206,0.001037568,0.000003025236,0.0001772811,0.01775968],"genre_scores_gemma":[0.996973,0.00009342541,0.002152135,0.000008289117,0.0002849913,0.000147978,0.000002967085,0.00002209811,0.00031514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1743664,"threshold_uncertainty_score":0.6579285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0431540004619801,"score_gpt":0.2498587461022365,"score_spread":0.2067047456402564,"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."}}