Join us in welcoming Subramanian Pazhani, Senior Operations Research Specialist at the SAS Institute, as he discusses optimization applications. Alums and friends of the program are always welcome.
https://ncsu.zoom.us/j/99042438351?pwd=OTRIWG3ySiA21TubNEVBxpsvnxLRoa.1
Meeting ID: 990 4243 8351
Passcode: 973594
Applications of optimization for real-world problems: Implementations in a semiconductor industry and a logistics industry
Optimization is the act of making something as fully effective as possible and we benefit from its application every day in our lives. There are numerous applications of optimization in industries. In this talk, I will discuss two optimization-based solution implementations in real industry.
In the first part, we will discuss an implementation in a semiconductor manufacturing facility to minimize operating cost and improve throughput for wafer cleaning processes. Wafer cleaning process in semiconductor fabrication comprises multiple steps at various stages of the product flow to remove particles and oxidize organic contaminants, using different chemicals. We consider J equipment groups which run I clean process steps and uses M different chemicals. We propose a bi-criteria mixed integer linear programming model to allocate daily demands to the equipment groups with the objectives of improving cycle time & throughput by minimizing the maximum utilization of the equipment groups and minimize operating costs by reducing chemical usage across these equipment groups. We also propose a priority based dispatching algorithm to translate the model output and implement in a dispatching system. Results show that the model reduces chemical pre-dispense resulting in significant cost saving, improved throughput due to cascading, and balanced utilization across the equipment groups.
In the second part, we will focus on custom-built solutions that combine heuristics with optimization algorithms for optimizing routing decisions for a large logistics provider in the US. Complex business problems typically need advanced analytics tools and solutions to efficiently solve them, with flexibility to create analytics pipelines that use complementary solution techniques. We will discuss customers’ business problems and share how to use multi-stage analytics solution approaches to efficiently solve them.
Subramanian (Subbu) Pazhani is a Senior Data Scientist with the Applied AI & Modelling, R&D, SAS Institute, Cary, North Carolina. His main responsibilities include designing, developing, and implementing optimization-based solutions for a variety of industries including Health Care, Transportation, Manufacturing, and Education. Subbu is also an adjunct faculty member at the North Carolina State University and teaches Data Analytics for Industrial Engineers, and Design & Analysis of Production and Service Systems. He holds a Ph.D. in Industrial Engineering & Operations Research from the Pennsylvania State University, University Park & a Master’s degree in Supply Chain Management from IIT Madras, India. Prior to SAS, Subbu also has 6+ years of semiconductor manufacturing experience where he developed analytical and optimization models for improving wafer manufacturing operations, focused on improving cycle times, productivity, and variability reduction using optimization & dispatching.
His research areas include network design in closed loop supply chains, inventory optimization in supply chains, inventory routing problems, designing supply chains for resiliency, and dispatching and cycle time optimization in Semiconductor fabs. He has published a focus book titled Design and Analysis of Closed Loop Supply Chain Networks and contributed to book chapters. He has also published several research articles in prestigious peer-reviewed journals such as International Journal of Production Economics, Computers & Industrial engineering, International Journal of Production Research, Applied Mathematical Modelling, Applied Soft Computing, and International Journal of Operational Research.
