Time Slot: Track 2 14:40-15:10 Language: English Speaker: Mr. Kang Hao | ShenZhen, China
Objective: In this talk, we will delve into the latest advancements, cross-industry applications, and development trends of Python and its ecosystem in the smart logistics field. We will focus on the following aspects:
Latest advancements in AMR scheduling optimization: Share the latest discoveries, technologies, and methods in using Python and machine learning to optimize AMR intelligent scheduling/simulation systems since last year. Demonstrate how to further improve task execution efficiency and reduce the likelihood of vehicle conflicts.
Cross-industry applications of Python in smart logistics: Explore how Python and machine learning technologies are widely applied in logistics-related fields, such as warehousing and distribution, to expand their application scope in smart logistics. Share practical cases to showcase innovative solutions and their resulting achievements in different scenarios.
Development trends of Python ecosystem in smart logistics: Analyze and predict the development trends of the Python ecosystem in the smart logistics field, and discuss potential new technologies and applications. Additionally, discuss how to further promote the development of the Python ecosystem in the smart logistics field to meet the constantly changing demands of the industry.
Through this talk, we hope to provide attendees with a comprehensive understanding of innovations and cross-domain applications of Python and machine learning in the smart logistics field, inspiring more people to explore and innovate in this domain.
Mr. Kang Hao
Kang Hao, a Master of Mechanical and Electronic Engineering, is currently working at a logistics research center focusing on innovations and cross-domain applications in the smart logistics field. Leveraging Python and its rich ecosystem, he specializes in optimizing AMR scheduling systems and applying machine learning technologies in logistics-related industries, such as warehousing and distribution. Throughout his research, he has successfully achieved the goal of completing large-scale scheduling systems and scenario simulations with minimal manpower input and explored development trends in the smart logistics field.