M. Affenzeller, A. Beham, S. Vonolfen, E. Pitzer, S. M. Winkler, S. Hutterer, M. Kommenda, M. Kofler, G. K. Kronberger, S. Wagner - Simulation-Based Optimization with HeuristicLab in Applied Simulation and Optimization (Contributions to Book: Part/Chapter/Section 1), (Editors: M. Mujica Mota, I. De La Mota, D. Guimarans Serrano) - Springer Gabler, 2015, pp. 3-38
Dynamic and stochastic problemenvironments are often difficult tomodel using standard problem formulations and algorithms. One way to model and then solve themis simulation-based optimization: Simulations are integrated into the optimization process in order to evaluate the quality of solution candidates and to identify optimized system configurations. Potential solutions are evaluated with a simulation model, which leads to new challenges regarding runtime performance, robustness, and distributed evaluation. In order to design, compare, and parameterize algorithmic approaches it is beneficial to use an optimization framework for algorithm design and evaluation. On the one hand, this chapter shows how arbitrary simulators can be coupled with the open-source HeuristicLab optimization framework. This coupling
is implemented in a generic way so that the simulators act as external evaluators. On the other hand, we demonstrate how arbitrary optimizers available within HeuristicLab can be called from a simulator in order to perform complex optimization tasks within the simulation model. In order to lustrate the applicability of these approaches, real-world examples investigated by the authors are discussed.We show here application examples from different fields, namely logistics network design,
vendor managed inventory routing, steel slab logistics, production optimization with
dispatching rule scheduling, material flow simulation, and layout optimization.