A carregar...
Publicação
A statistical analytics framework for decision-ready scheduling in autonomous intralogistics
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.45 MB | Adobe PDF |
Orientador(es)
Resumo(s)
The widespread adoption of Internet of Things (IoT) technologies within Industry 4.0 has increased the need for
more responsive and efficient intralogistics operations, especially as industries move toward highly customized,
small-batch production. Autonomous Mobile Robots (AMRs) provide the flexibility needed for dynamic
industrial environments. However, their effective integration relies on accurate task-duration estimates, which
are difficult to obtain due to system variability and operational uncertainty. This work addresses this challenge
by statistically modeling picking and loading/unloading times using real-world industrial data. Probability
distribution-fitting techniques are employed to capture the random nature of these operations, thereby reducing
the need for unrealistic assumptions often used in scheduling models. The proposed methodology enhances the
statistical basis for robust AMR fleet scheduling, improving coordination and reducing operational variability.
Validation is conducted by directly comparing empirical data with fitted distributions, demonstrating clear
improvements in representativeness and predictive accuracy. The results demonstrate the potential of this
approach to improve decision-making in highly dynamic intralogistics environments.
Descrição
Palavras-chave
Intralogistics analytics Operational efficiency Robust scheduling Statistical distribution fitting Performance analysis Discrete events
Contexto Educativo
Citação
Editora
Elsevier