Maximizing Efficiency for Simulation-based or Sample-based Optimization

时 间： 2014年11月20日下午15:00--16：30

地 点： 第25教学楼A区3层教室C

讲座题目： Maximizing Efficiency for Simulation-based or Sample-based Optimization

主 讲 人： Chun-Hung Chen

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.