Abstract
Meta-heuristics and hybrid heuristic approaches have been successfully applied to Periodic Vehicle Routing Problems (PVRPs). However, to be competitive, these methods require careful design of specific search strategies for each problem. By contrast, hyperheuristics use the performance of low level heuristics to automatically select and tailor search strategies. Hyperheuristics have been successfully applied to problem domains such as timetabling and production scheduling. In this study, we present a comprehensive analysis of hyperheuristic approaches to solving PVRPs. The performance of hyperheuristics is compared to published performance of state-of-the-art meta-heuristics.
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Notes
- 1.
The real-world data and associated best-performance results (Sect. 6) can be found at https://www-users.cs.york.ac.uk/~yujiec/.
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Acknowledgement
The authors would like to thank Gaist Solutions Ltd. for providing data. This research is part of the LSCITS project funded by the EPSRC.
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Chen, Y., Mourdjis, P., Polack, F., Cowling, P., Remde, S. (2016). Evaluating Hyperheuristics and Local Search Operators for Periodic Routing Problems. In: Chicano, F., Hu, B., GarcÃa-Sánchez, P. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2016. Lecture Notes in Computer Science(), vol 9595. Springer, Cham. https://doi.org/10.1007/978-3-319-30698-8_8
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