| Autor: |
Qingsong Ai, Jun Zhang, Quan Liu, Chuanjie Zhang, Qiyuan Chen, Junwei Yan |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 11, Pp 139575-139586 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3340217 |
| Popis: |
Transportation wastage is inevitable during transportation. With the emergence of container transportation, the transportation wastage generated by most cargo through container transportation has been greatly reduced. However, for products such as grain, oil, and sand transported in bulk, significant transportation wastage still occurs during the process. Under the background of vigorously developing intermodal transportation in the transportation industry, there is still limited research on transportation wastage in bulk intermodal transportation. This study proposes a multi-objective model to determine appropriate transport routes and modes for inland waterway dry bulk transport, minimizing transport time and transport costs considering transport wastage. Using existing machine learning algorithms to predict wastage in multimodal transportation of bulk cargo. The goal is to accurately predict transportation wastage, determine transportation routes and modes. Considering the poor generalization ability of heuristic algorithms, a hyper-heuristic algorithm based on the hypervolume indicator is designed to solve the model, and it is validated through simulation experiments. The analysis results show that the model can effectively reduce the wastage rate during bulk intermodal transportation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
