TY - GEN
T1 - Optimization of Cube Storage Warehouse Scheduling Using Genetic Algorithms
AU - Ha, Won Yong
AU - Jiang, Zhong Ping
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, a new scheduling model is presented to speed up the logistics processing in an automatic cube storage warehouse. Automated guided vehicles (AGV) are used to move all items in the warehouse according to the computer's instructions. The tasks to be performed by the AGV are optimally distributed using Genetic Algorithms (GA). The goal of our research is to optimize order scheduling in automatic warehouses to reduce human resources and lower the cost of logistics. The proposed GA's fitness function reflects removing the stacked bin, a cube storage warehouse characteristic, and getting the designated bin. Through extensive computer simulations, it is shown that the higher the generation of the GA we design, the lower the logistics processing time. As compared with other meta-heuristic optimization algorithms, our proposed GA algorithm demonstrates a maximum of 21% reduction in delivery time.
AB - In this paper, a new scheduling model is presented to speed up the logistics processing in an automatic cube storage warehouse. Automated guided vehicles (AGV) are used to move all items in the warehouse according to the computer's instructions. The tasks to be performed by the AGV are optimally distributed using Genetic Algorithms (GA). The goal of our research is to optimize order scheduling in automatic warehouses to reduce human resources and lower the cost of logistics. The proposed GA's fitness function reflects removing the stacked bin, a cube storage warehouse characteristic, and getting the designated bin. Through extensive computer simulations, it is shown that the higher the generation of the GA we design, the lower the logistics processing time. As compared with other meta-heuristic optimization algorithms, our proposed GA algorithm demonstrates a maximum of 21% reduction in delivery time.
UR - http://www.scopus.com/inward/record.url?scp=85174391026&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174391026&partnerID=8YFLogxK
U2 - 10.1109/CASE56687.2023.10260388
DO - 10.1109/CASE56687.2023.10260388
M3 - Conference contribution
AN - SCOPUS:85174391026
T3 - IEEE International Conference on Automation Science and Engineering
BT - 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
PB - IEEE Computer Society
T2 - 19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Y2 - 26 August 2023 through 30 August 2023
ER -