Abstract
Hyper-heuristics seek solution methods instead of solutions and thus provides a higher level of generality compared to bespoke metaheuristics and traditional heuristic approaches. In this paper, a hyper-heuristic is proposed to solve the ski-lodge problem which involves allocating shared-time apartments to customers during a skiing season in a way that achieves a certain objective while respecting the constraints of the problem. Prior approaches to the problem include simulated annealing and genetic algorithm. To the best of our knowledge, this is the first time the ski-lodge problem is approached from a hyper-heuristic perspective. Although the aim of hyper-heuristics is to provide good results over problem sets rather than producing best results for certain problem instances, for completeness and to get an idea of the quality of solutions, the results of the proposed hyper-heuristic are compared to that of genetic algorithm and simulated annealing. The hyper-heuristic was found to perform better than simulated annealing and comparatively to the genetic algorithm, producing better results for some of the instances. Furthermore, the hyper-heuristic has better overall performance over the problem set being considered.
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Hassan, A., Pillay, N. (2016). A Hyper-Heuristic Approach to Solving the Ski-Lodge Problem. In: Pillay, N., Engelbrecht, A., Abraham, A., du Plessis, M., Snášel, V., Muda, A. (eds) Advances in Nature and Biologically Inspired Computing. Advances in Intelligent Systems and Computing, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-319-27400-3_18
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DOI: https://doi.org/10.1007/978-3-319-27400-3_18
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