Ordinal Optimization with Subset Selection Rule |
| |
| Authors: | Yang M.S. Lee L.H. |
| |
| Affiliation: | (1) Akamai Technologies, Cambridge, Massachusetts;(2) Department of Industrial and Systems Engineering, National University of Singapore, Kent Ridge, Singapore |
| |
| Abstract: | Ordinal optimization (OO) has enjoyed a great degree of success in addressing stochastic optimization problems characterized by an independent and identically distributed (i.i.d.) noise. The methodology offers a statistically quantifiable avenue to find good enough solutions by means of soft computation. In this paper, we extend the OO methodology to a more general class of stochastic problems by relaxing the i.i.d. assumption on the underlying noise. Theoretical results and their applications to simple examples are presented. |
| |
| Keywords: | Ordinal optimization goal softening |
| 本文献已被 SpringerLink 等数据库收录！ |
|
