Sreenath Chalil Madathil

Sreenath Chalil Madathil

Research Scientist
Watson Institute of Systems Excellence (WISE)

About Me

I currently work as a research scientist at WISE, part of SUNY Research Foundation, at Binghamton University. I completed my Ph.D. from the Department of Industrial Engineering at Clemson University. I received my M.S. in Industrial Engineering from Clemson University in August 2013. My research interests covers the application of optimization and operations research for supply chain management, energy, and healthcare. At Clemson, I am advised by Dr. Scott J. Mason.

I am an avid Clemson football fan and watch all the Clemson football games. I love playing volleyball and racquetball. Reading articles about spirituality and Hinduism is one of my favorite leisure time activities.

Professional Experience

Research Scientist - Watson Institute of Systems Excellence (WISE) (2017 - Current)

Graduate Intern - Los Alamos National Lab (2015 - 2016)
Graduate Research Assistant - Clemson University (2011 - 2017)
Graduate Teaching Assistant - Clemson University (2011 - 2017)
Research Scholar - BMW Manufacturing Co (Summer 2014)
Analyst - Cognizant Technology Solutions (2006 - 2011)

          ✪ First Data Corporation - Omaha, NE (2009 - 2011)
           ✪ WellPoint Inc - Richmond, VA (Spring 2009)
           ✪ WellPoint Inc - New Haven, CT (Fall 2008)
           ✪ WellPoint Inc - Chennai, India (2006 - 2008)

Honors and Awards

Winner of Best Poster Presentation

Student Symposium: University of New Mexico - Los Alamos (August 2016)

Automated Storage/Retrieval Systems Honor Scholarship

Material Handling Education Foundation, Inc. (2015 - 2016)

Outstanding Graduate Teaching Assistant Award

Department of Industrial Engineering, Clemson University (2013 - 2014)

Outstanding Teaching Assistant of the Year Award

Department of Industrial Engineering, Clemson University (2012 - 2013)

Latest Projects

Off-grid microgrid design and operation

A research for a resilient and sustainable energy for the future[1]

New
Over the past century the electric power industry has evolved to support the delivery of power over long distances with highly interconnected transmission systems that span large geographic regions. Despite this evolution, in some parts of the United States and other parts of the world, some communities are not connected to these systems. These communities rely on small, disconnected distribution systems, i.e., microgrids to deliver power. More importantly, perhaps, there is a general trend to support microgrid development within large transmission systems for economic, environmental, and reliability reasons. However, microgrids are not held to same reliability standards as transmission grids can place many communities at risk for extended black-outs. To address this issue, we develop an optimization model and algorithm for capacity planning and operations of microgrids that includes N-1 security and other modeling features. The effectiveness of the approach is demonstrated using the IEEE 13 node test feeder and a model of the Nome, Alaska distribution system.

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Scheduling system for semiconductor industry

Developed a mathematical mixed integer programming (MIP) model to solve a scheduling problem in semiconductor manufacturing industry that includes flexible flowshop system of cluster tools as well as stand-alone tools for the photolithography stage of semiconductor wafer fabrication. A permutation-based heuristic algorithm was also developed and the performance of the algorithm is compared with the mathematical model. The major observation from the results was that the heuristic algorithm found optimal or near-optimal solutions in a very short span of time.

Find out more

Roll Size Analysis

Roll Size Analysis of a fabric manufacturing company

A company with multiple distribution centers (DC) across the country has data about their current inventory as well as number of orders and the length of fabrics that were ordered for every month. Analysis of data revealed important insights regarding amount and length of fabric that they need to keep at each of their DCs in the future. We were able to identify locations where they are keeping more inventory than required and our analysis helped them to reduce their losses and wastage of clothes.

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Model Allocation to paintshop with varying flexibility

A decision support system to assign various models of cars coming from body shop to paint shop is developed using VBA in Excel and Solver. Various objectives like utilization, idle time, and number of resources were considered while allocating models to the shops. The decisions are also made based on the level of flexibility of each of the paint shops. Various resources also need to be scheduled to account for the different shift schedules between the shops. A cost comparison of current schedule with respect to the improved schedule proved efficiency of the model