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Maximizing Efficiency for Simulation-based or Sample-based Optimization

Date：2014-11-17 From：

Topic :

Maximizing Efficiency for Simulation-based or Sample-based Optimization

Lecturer:

Prof.Chun-Hung Chen Dept. of Systems Engineering & Operations Research ,George Mason University, USA

Time:Saturday 15:00–16:30, Nov. 20

Place:Building25-A, 3F, Room C

ABSTRACT

Simulation and optimization are two popular tools in industrial engineering and operations research. Stochastic simulation is powerful for analyzing modern complex systems. Detailed dynamics of complex, stochastic systems can be modeled in simulation. This capability complements the inherent limitation of traditional optimization, so the combining use of simulation and optimization is growing in popularity. This seminar discusses what computational issues we have to face such a combination, and presents our new development to address these issues. A key component of our methodologies is a new technique called Optimal Computing Budget Allocation (OCBA) initially developed by the speaker, which intends to maximize the overall simulation or sampling efficiency for finding an optimal decision. OCBA also provides a good solution to the well-known exploration (search) vs. exploitation (estimation) tradeoff. Further, OCBA can optimally allocate samples for partition-based random search optimization algorithms.

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