Cross-dock warehouse operation planning
As the competition expands in almost every industry, especially in retail and grocery industries, companies need to search for various ways of reducing total costs throughout the supply chain. Therefore, many companies follow the cross-docking strategy in distribution which has the advantage of fast...
| Главный автор: | |
|---|---|
| Другие авторы: | |
| Формат: | Thesis-Master by Coursework |
| Язык: | English |
| Опубликовано: |
Nanyang Technological University
2020
|
| Предметы: | |
| Online-ссылка: | https://hdl.handle.net/10356/140513 |
| _version_ | 1826120038668566528 |
|---|---|
| author | Wu, Yue |
| author2 | Rajesh Piplani |
| author_facet | Rajesh Piplani Wu, Yue |
| author_sort | Wu, Yue |
| collection | NTU |
| description | As the competition expands in almost every industry, especially in retail and grocery industries, companies need to search for various ways of reducing total costs throughout the supply chain. Therefore, many companies follow the cross-docking strategy in distribution which has the advantage of faster cycle time, less inventory, quicker response, increased customer satisfaction, and cost-effective.
This study focuses on the operational level of cross-dock policy which involves the capacitated vehicle routing problem with soft time windows and scheduling problem of both outbound trucks and inbound trucks, with the objective of minimizing total cost, consisting of transportation cost, earliness penalty cost, tardiness penalty cost, and inventory holding cost. The methodology of “cluster first, route second” is adopted to solve the problem. Two different clustering methods, namely distance- based clustering in terms of the geographical distribution by K-medoid, and order size-based clustering concentrating on the geographical distribution and sum of the customer order by improved K-means, are proposed to generate clusters. Then, a meta-heuristic, Tabu-Search based algorithm is developed to find the optimal route and schedule of trucks in every cluster.
The algorithms are developed using Python 3.0. After computational experiments with different sizes of data set and sensitivity analysis for critical parameters, the optimal solution of the whole policy is shown in the results, and the conclusion of the effectiveness drawn. |
| first_indexed | 2024-10-01T05:10:00Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/140513 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:10:00Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1405132023-03-11T17:46:40Z Cross-dock warehouse operation planning Wu, Yue Rajesh Piplani School of Mechanical and Aerospace Engineering MRPiplani@ntu.edu.sg Engineering::Industrial engineering::Supply chain As the competition expands in almost every industry, especially in retail and grocery industries, companies need to search for various ways of reducing total costs throughout the supply chain. Therefore, many companies follow the cross-docking strategy in distribution which has the advantage of faster cycle time, less inventory, quicker response, increased customer satisfaction, and cost-effective. This study focuses on the operational level of cross-dock policy which involves the capacitated vehicle routing problem with soft time windows and scheduling problem of both outbound trucks and inbound trucks, with the objective of minimizing total cost, consisting of transportation cost, earliness penalty cost, tardiness penalty cost, and inventory holding cost. The methodology of “cluster first, route second” is adopted to solve the problem. Two different clustering methods, namely distance- based clustering in terms of the geographical distribution by K-medoid, and order size-based clustering concentrating on the geographical distribution and sum of the customer order by improved K-means, are proposed to generate clusters. Then, a meta-heuristic, Tabu-Search based algorithm is developed to find the optimal route and schedule of trucks in every cluster. The algorithms are developed using Python 3.0. After computational experiments with different sizes of data set and sensitivity analysis for critical parameters, the optimal solution of the whole policy is shown in the results, and the conclusion of the effectiveness drawn. Master of Science (Supply Chain and Logistics) 2020-05-29T11:04:52Z 2020-05-29T11:04:52Z 2020 Thesis-Master by Coursework https://hdl.handle.net/10356/140513 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Industrial engineering::Supply chain Wu, Yue Cross-dock warehouse operation planning |
| title | Cross-dock warehouse operation planning |
| title_full | Cross-dock warehouse operation planning |
| title_fullStr | Cross-dock warehouse operation planning |
| title_full_unstemmed | Cross-dock warehouse operation planning |
| title_short | Cross-dock warehouse operation planning |
| title_sort | cross dock warehouse operation planning |
| topic | Engineering::Industrial engineering::Supply chain |
| url | https://hdl.handle.net/10356/140513 |
| work_keys_str_mv | AT wuyue crossdockwarehouseoperationplanning |