Combining Gradient and Adaptive Search in Simulation Optimization

Professor Michael Fu (BMGT/ISR/ECE) is the principal investigator and Professor Steve Marcus (ECE/ISR) is the co-PI for a three-year, $350K NSF collaborative research grant, Combining Gradient and Adaptive Search in Simulation Optimization. The researchers will develop new simulation optimization algorithms based on different sequences of the so-called "reference distributions" in a recently developed approach called model reference adaptive search, and new hybrid global-local search algorithms integrating local gradient search and problem structure. They also will conduct rigorous theoretical analysis of the resulting algorithms, both finite-time behavior using an adaptive search framework and asymptotic behavior using a novel connection to stochastic approximation methods. A wide variety of applications from supply chain management to financial engineering will be tested to investigate specific gradient search algorithms and problem structure, and evaluating the effectiveness in terms of empirical behavior. This line of research fills an important part of the "analytics" computational tool kit that has led to increased competitiveness for US businesses from manufacturers and retailers with global supply chains to financial services managing complex risk factors.