Simulation-Based Optimization with HeuristicLab: Practical Guidelines and Real-World Applications

Michael Affenzeller (), Andreas Beham, Stefan Vonolfen, Erik Pitzer, Stephan M. Winkler, Stephan Hutterer, Michael Kommenda, Monika Kofler, Gabriel Kronberger and Stefan Wagner
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Michael Affenzeller: University of Applied Sciences Upper Austria, Research Center Hagenberg, Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media
Andreas Beham: University of Applied Sciences Upper Austria, Research Center Hagenberg, Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media
Stefan Vonolfen: University of Applied Sciences Upper Austria, Research Center Hagenberg, Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media
Erik Pitzer: University of Applied Sciences Upper Austria, Research Center Hagenberg, Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media
Stephan M. Winkler: University of Applied Sciences Upper Austria, Research Center Hagenberg, Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media
Stephan Hutterer: University of Applied Sciences Upper Austria, Research Center Hagenberg, Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media
Michael Kommenda: University of Applied Sciences Upper Austria, Research Center Hagenberg, Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media
Monika Kofler: University of Applied Sciences Upper Austria, Research Center Hagenberg, Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media
Gabriel Kronberger: University of Applied Sciences Upper Austria, Research Center Hagenberg, Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media
Stefan Wagner: University of Applied Sciences Upper Austria, Research Center Hagenberg, Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media

A chapter in Applied Simulation and Optimization, 2015, pp 3-38 from Springer

Abstract: Abstract Dynamic and stochastic problem environments are often difficult to model using standard problem formulations and algorithms. One way to model and then solve them is 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 illustrate 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.

Keywords: Electric Vehicle; Priority Rule; Logistics Network; Policy Function; Simulation Optimization (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-319-15033-8_1

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