Abstract
Vehicle route optimization is an important application of combinatorial optimization. Therefore, a variety of methods has been proposed to solve different challenging vehicle routing problems. An important step in adopting these methods to solve real-life problems is to find appropriate parameters for the routing algorithms. In this chapter, we show how this task can be automated using parameter tuning by presenting a set of comparative experiments on seven state-of-the-art tuning methods. We analyze the suitability of these methods in configuring routing algorithms, and give the first critical comparison of automated parameter tuners in vehicle routing. Our experimental results show that the tuning methods are able to effectively automate the task of parameter configuration of route optimization systems. Moreover, our comparison shows that while routing algorithms clearly benefit from parameter tuning, and while there is no single tuner which consistently outperforms others, the tuning performance can be clearly improved with careful choice of a tuning method.
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Notes
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was disabled for its tendency to produce infeasible route
- 2.
- 3.
Version 0.9.93.4r2658, http://www.lri.fr/~hansen/cmaesintro.html.
- 4.
Version 0.9, http://iridia.ulb.ac.be/irace/.
- 5.
Version 2.3.5, http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/
Version 2.0.2, http://www.cs.ubc.ca/labs/beta/Projects/SMAC/.
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Acknowledgments
Support from colleagues from the Research Group on Computational Logistics of Department of Mathematical Information Technology (University of Jyväskylä) is gratefully acknowledged. Office for Jussi Rasku at University Consortium of Seinäjoki researcher residency was supported by European Regional Development Fund (ERDF): A31342. Nysret Musliu was supported by the Austrian Science Fund (FWF): P24814-N23. Tommi Kärkkäinen was supported by a research grant of Jenny and Antti Wihuri Foundation.
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Rasku, J., Musliu, N., Kärkkäinen, T. (2014). Automating the Parameter Selection in VRP: An Off-line Parameter Tuning Tool Comparison. In: Fitzgibbon, W., Kuznetsov, Y., Neittaanmäki, P., Pironneau, O. (eds) Modeling, Simulation and Optimization for Science and Technology. Computational Methods in Applied Sciences, vol 34. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9054-3_11
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