Integrating Discrete-Event and Time-Based Models with Optimization for Resource Allocation
By Teresa Hubscher-Younger, Pieter J. Mosterman, Seth DeLand, Omar Orqueda, and Doug Eastman, MathWorks
Optimization’s importance for technical systems’ performance can hardly be overstated. Even small improvements can result in substantial cost, resources, and time savings. A constructive approach to dynamic system optimization can formalize the optimization problem in a mathematical sense. The complexity of modern systems, however, often prohibits such formalization, especially when different modeling paradigms interact. Phenomena such as parasitic effects present additional complexity. This work employs a generative approach to optimization, where computational simulation of the problem space is combined with a computational optimization approach in the solution space. To address the multiparadigm nature, simulation relies on a unifying semantic domain in the form of an abstract execution framework that can be made concrete. Because of the flexibility of the computational infrastructure, a highly configurable integrated environment is made available to the optimization expert. The overall approach is illustrated with a resource allocation problem, which combines continuous-time, discrete-event, and state-transition systems.
This paper was published in the 2012 Winter Simulation Conference proceedings.