1. The decision rule approach to optimization under uncertainty: methodology and applications
- Author
-
Georghiou, Angelos, Kuhn, Daniel, Wiesemann, Wolfram, Engineering & Physical Science Research Council (E, and Georghiou, Angelos [0000-0003-4490-4020]
- Subjects
Mathematical optimization ,Operations Research ,POWER ,0211 other engineering and technologies ,Social Sciences ,Stochastic programming ,02 engineering and technology ,Management Information Systems ,FACILITY LOCATION ,DESIGN ,DUALITY ,0102 Applied Mathematics ,021108 energy ,Mathematics ,021103 operations research ,FINITE ADAPTABILITY ,Stochastic process ,business.industry ,0103 Numerical and Computational Mathematics ,Feasible region ,Robust optimization ,Decision rule ,Social Sciences, Mathematical Methods ,Decision problem ,Partition (database) ,ADJUSTABLE ROBUST OPTIMIZATION ,1503 Business and Management ,Decision rules ,Artificial intelligence ,business ,Optimization under uncertainty ,Mathematical Methods In Social Sciences ,Information Systems ,Curse of dimensionality ,GENERATION - Abstract
Dynamic decision-making under uncertainty has a long and distinguished history in operations research. Due to the curse of dimensionality, solution schemes that naïvely partition or discretize the support of the random problem parameters are limited to small and medium-sized problems, or they require restrictive modeling assumptions (e.g., absence of recourse actions). In the last few decades, several solution techniques have been proposed that aim to alleviate the curse of dimensionality. Amongst these is the decision rule approach, which faithfully models the random process and instead approximates the feasible region of the decision problem. In this paper, we survey the major theoretical findings relating to this approach, and we investigate its potential in two applications areas. 16 4 545 576
- Published
- 2018