Integrating optimization and simulation: research and practice
Proceedings of the 32nd conference on Winter simulation
A framework for distributed simulation optimization
Proceedings of the 33nd conference on Winter simulation
Theory of Modeling and Simulation
Theory of Modeling and 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
Simulation-based multi-objective optimization of a real-world scheduling problem
Proceedings of the 38th conference on Winter simulation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Network Models and Optimization: Multiobjective Genetic Algorithm Approach
Network Models and Optimization: Multiobjective Genetic Algorithm Approach
Integrating simulation and optimization to schedule loading operations in container terminals
Computers and Operations Research
Introduction to Evolutionary Multiobjective Optimization
Multiobjective Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
DEVS approximation of infiltration using genetic algorithm optimization of a fuzzy system
Mathematical and Computer Modelling: An International Journal
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The combination of simulation and optimization has been successfully applied to solve real-world decision making problems. However, many of the frameworks used to define the integration between simulation and optimization lack of transparent and coherent structure. This consequently deters the effective use of powerful features the simulation technique by optimization practitioners and vice versa. Furthermore, it also hinders the development of simulation-based optimization methods that have a proper balance between the desired features (i.e. generality, efficiency, high-dimensionality and transparency). This research provides the design of the framework that addresses the knowledge gap above and facilitates the fulfillment of the aforementioned features. The proposed framework is developed based on Zeigler's modeling and simulation framework and the phases of an optimization study in operations research. Finally, the test and evaluation on the framework implementation show that the framework successfully meets the desired features.