Simulation Optimization
SIMULATION OPTIMIZATION:
APPLICATIONS IN RISK MANAGEMENT[1]
MARCO BETTER AND FRED GLOVER
OptTek Systems, Inc., 2241 17th Street,
Boulder, Colorado 80302, USA
{better, glover}@opttek.com
GARY KOCHENBERGER
University of Colorado Denver
1250 14th Street, Suite 215
Denver, Colorado 80202, USA
Gary.kochenberger@cudenver.edu
HAIBO WANG
Texas A&M International University
Laredo, TX 78041, USA hwang@tamiu.edu Simulation Optimization is providing solutions to important practical problems previously beyond reach. This paper explores how new approaches are significantly expanding the power of Simulation Optimization for managing risk. Recent advances in Simulation Optimization technology are leading to new opportunities to solve problems more effectively. Specifically, in applications involving risk and uncertainty, Simulation Optimization surpasses the capabilities of other optimization methods, not only in the quality of solutions, but also in their interpretability and practicality. In this paper, we demonstrate the advantages of using a Simulation Optimization approach to tackle risky decisions, by showcasing the methodology on two popular applications from the areas of finance and business process design.
Keywords: optimization, simulation, portfolio selection, risk management.
1. Introduction
Whenever uncertainty exists, there is risk. Uncertainty is present when there is a possibility that the outcome of a particular event will deviate from what is expected. In some cases, we can use past experience and other information to try to estimate the probability of occurrence of different events. This allows us to estimate a probability distribution for all possible events. Risk can be defined as the probability of occurrence of an event that would have a negative effect on a goal. On the other hand, the probability of occurrence of an event that would have a positive impact is
APPLICATIONS IN RISK MANAGEMENT[1]
MARCO BETTER AND FRED GLOVER
OptTek Systems, Inc., 2241 17th Street,
Boulder, Colorado 80302, USA
{better, glover}@opttek.com
GARY KOCHENBERGER
University of Colorado Denver
1250 14th Street, Suite 215
Denver, Colorado 80202, USA
Gary.kochenberger@cudenver.edu
HAIBO WANG
Texas A&M International University
Laredo, TX 78041, USA hwang@tamiu.edu Simulation Optimization is providing solutions to important practical problems previously beyond reach. This paper explores how new approaches are significantly expanding the power of Simulation Optimization for managing risk. Recent advances in Simulation Optimization technology are leading to new opportunities to solve problems more effectively. Specifically, in applications involving risk and uncertainty, Simulation Optimization surpasses the capabilities of other optimization methods, not only in the quality of solutions, but also in their interpretability and practicality. In this paper, we demonstrate the advantages of using a Simulation Optimization approach to tackle risky decisions, by showcasing the methodology on two popular applications from the areas of finance and business process design.
Keywords: optimization, simulation, portfolio selection, risk management.
1. Introduction
Whenever uncertainty exists, there is risk. Uncertainty is present when there is a possibility that the outcome of a particular event will deviate from what is expected. In some cases, we can use past experience and other information to try to estimate the probability of occurrence of different events. This allows us to estimate a probability distribution for all possible events. Risk can be defined as the probability of occurrence of an event that would have a negative effect on a goal. On the other hand, the probability of occurrence of an event that would have a positive impact is
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