APPLYING LEARNING- AND OPTIMIZATION-BASED METHODS IN AUTOMATED WAREHOUSING SYSTEMS
Dr. Lin Xie
Assistant Professor, IEBIS Department, University of Twente.
It is expected that retail e-commerce sales will reach 7.4 trillion U.S. dollars worldwide in 2025, which will be about 5 times more compared with the number in 2014. Besides the increasing amount and frequency of orders, the new challenges in e-commerce and online retail are coming from the diversity of ordered products (many of them are small items), customers’ expectations of same-day delivery and pressure from competition. So it is important to increase efficiency, especially in the order picking process in warehouses, since it is one of the most work- and cost-intensive processes. In traditional order picking systems, 70% of human pickers’ working hours are spent on unproductive work, namely searching and traveling in the warehouse. Therefore there are more and more automated picking systems in operation to reduce this unproductive work. To operate an automated system, we have to face many decision problems, such as job assignments for robots and path planning of robots. In this talk, I will introduce two automated picking systems and show our learning- and optimization-based methods to improve picking efficiency.
Lin Xie is an assistant professor of AI/OR Smart & Sustainable Industry at the Department of Industrial Engineering and Business Information Systems (IEBIS) at the University of Twente and a visiting professor at the Leuphana University of Lüneburg (Germany). She holds a bachelor's and a master's degree (2008 and 2010) in Information Systems and did her Ph.D. at the University of Paderborn (2014). After finishing her Ph.D., Lin did her postdoc at the same university. From 2015 to 2022, she was an assistant professor of Information Systems and Operations Research at the Leuphana University of Lüneburg. Her research focuses lie at the intersection of digital logistics, sustainable transportation, operations research and machine learning.
Industry 4.0 evolution and implementation
Dr. Mahak Sharma
Assistant Professor, IEBIS Department, University of Twente.
Dr. Sharma is predominantly working in the area of Fourth Industrial Revolution. She will be sharing her research on Industry 4.0 evolution and implementation.
The focus of the seminar is on two of her contemporary works:
1. Discuss the possibility if Industry 4.0 (I4.0) can explore and exploit orientation strategies for better manufacturing processes. The research uses theoretical lens of dynamic capability theory, and contextual factors - institutional pressures.
2. Emergence of Industry 5.0 (I5.0): In this talk she will discuss I5.0’s ability to facilitate real-time synchronization of production processes and if I5.0 can assist in moving a step closer to sustainable supply chain.
Dr. Sharma is a fellow from National Institute of Industrial Engineering (NITIE), India. She holds Masters of Technology and Bachelors of Technology in Computer Science and Engineering and is graduated with Honours. She is a recipient of POMS Honourable Mention for research work. She has publications in various journals of high repute. She is on the Editorial Review Board of prestigious journals such as IEEE Transactions of Engineering Management. She also has six years of industry experience in product management and software development.
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