UM ESTUDO SOBRE PREVISÃO DA DEMANDA DE ENCOMENDAS UTILIZANDO UMA REDE NEURAL ARTIFICIAL
Bibliographic record
Abstract
Nas últimas décadas, o Brasil passou por diversas transformações, passando de uma economia fechada para uma economia de mercado.Ao transporte, tratamento e distribuição de encomendas restaram acompanhar essas tendências.Em razão disso, o serviço de entrega de encomendas tornou-se altamente complexo e competitivo.Nesse contexto, a previsão da demanda de encomendas surge como diferencial, levando produtividade estruturada e alto nível de serviço ao cliente.O objetivo do artigo é prever a demanda diária de encomendas em um Centro de Tratamento de Encomendas (CTE), durante quinze dias, utilizando Redes Neurais Artificiais (RNAs).A síntese metodológica do artigo consiste no desenvolvimento de uma Rede Neural Artificial do tipo Multilayer Perceptron (MLP), treinada através do algoritmo de error back-propagation.Os dados para a realização dos experimentos foram coletados durante 60 dias úteis, 45 dias para treinamento e 15 dias para teste.Os resultados obtidos com a utilização das RNAs na previsão da demanda de encomendas apresentaram boa aderência aos dados experimentais nas fases de treinamento e teste.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".