Distributed simulation: creating distributed simulation using DEVS M&S environments
Proceedings of the 32nd conference on Winter simulation
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Simulation-based optimization: practical introduction to simulation optimization
Proceedings of the 35th conference on Winter simulation: driving innovation
Supply chain and distribution network: semiconductor supply network simulation
Proceedings of the 35th conference on Winter simulation: driving innovation
WSC '04 Proceedings of the 36th conference on Winter simulation
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
ModHel'X: A Component-Oriented Approach to Multi-Formalism Modeling
Models in Software Engineering
System modeling and transformational design refinement in ForSyDe [formal system design]
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
| Hi-index | 0.00 |
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 multi-paradigm 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.