| id |
caadria2024_384 |
| authors |
Wen, Yi, Ye, Wei and Yu, Gang |
| year |
2024 |
| title |
A Hybrid Multi-objective Model for Multi-story Warehouse Design: A Case Study in Shenzhen |
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 283–292 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.1.283
|
| summary |
The thriving on-demand delivery economy and the increasing focus on addressing the global environmental crisis have spurred the need for efficient and sustainable logistics facilities in urban central areas. This paper delves into the optimization of multi-story logistics warehouse design in Shenzhen, China. Based on comprehensive investigations of existing multi-story warehouses in Shenzhen, the study proposes a hybrid computational model of integer programming and NSGA-II tailored for the generation and optimization of multi-story warehouse general layout design. The prototype, aimed at enhancing efficiency and sustainability, translates these concepts into attainable goals of optimizing land utilization, construction cost, and transportation distance. By addressing multi-objective challenges in the design process, the prototype's effectiveness is validated through a real-world case study. This paper seeks to offer a pragmatic approach to designing cost-effective and resilient multi-story logistics warehouses in the long term, applicable in both Shenzhen and other densely populated urban centers. The insights derived from this study may contribute to the ongoing discourse on optimizing logistics in dynamically evolving industrial landscapes. |
| keywords |
logistics warehouse design, multi-story warehouse, generative design, multi-objective optimization, efficiency, sustainability, integer programming, NSGA-II |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,020,447 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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