Process Engineering

Optimization and Mathematical Modeling
A specific initiative of optimization and mathematical modeling are quantitative techniques applied to corporate risk management. The key aspect of the risk management work is the integrated approach to business intelligence, which leverages our expertise in statistics, optimization and finance. The focus of the work is to develop quantitative techniques which aid in decision-making. 

Another area is on electronic packaging where efficient analysis and optimization techniques are needed to address some important problems. Research also includes  development of approximate process models based on Design of Experiments (DOE) and formulation of general mathematical models for industry-specific critical problems.  

Focus Areas

  • Business Intelligence: Using modern mathematical and statistical techniques in business intelligence issues like forecasting, revenue modeling, and risk management.

  • Optimization and Modeling in Engineering: Solving problems in process engineering, reliability engineering, design, and analysis of components by using modeling and optimization techniques. 

Examples

  • Synchronization of rail track geometry data is part of an ongoing TCS project with a major railroad company in the USA. We first developed a heuristic algorithm for this, and later, a formal dynamic programming-based algorithm in collaboration with the University of Illinois of Urbana-Champaign. This technique provides a global optimum solution for the given assumptions.

  • Application of classical optimization techniques such as Sequential Quadratic Programming, as well as newer techniques such as Genetic Algorithms, for optimizing the temperature-time profile of a carburizing furnace.

  • Use of robust optimization techniques for asset allocation among the clients of a company. 

  • Development of a revenue model for multinational corporations. 

Collaboration is underway with the Statistics Department of the University of Pune, to study various statistical modeling techniques. 

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