Simulation-based multi-objective optimization of a real-world scheduling problem

ABSTRACT

This paper presents a successful application of simulation-based multi-objective optimization of a complex real-world scheduling problem. Concepts of the implemented simulation-based optimization architecture are described, as well as how different components of the architecture are implemented. Multiple objectives are handled in the optimization process by considering the decision makers' preferences using both prior and posterior articulations. The efficiency of the optimization process is enhanced by performing culling of solutions before using the simulation model, avoiding unpromising solutions to be unnecessarily processed by the computationally expensive simulation.

References

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• Computing methodologies

  • Artificial intelligence

    • Search methodologies

• Theory of computation

  • Design and analysis of algorithms

    • Approximation algorithms analysis

    • Online algorithms

      • Online learning algorithms

  • Theory and algorithms for application domains

    • Machine learning theory

      • Reinforcement learning