A case-based reasoning approach to fast optimization of travel routes for large-scale AS/RSs

Due to the increasing volume of stocks in the recent production and logistics environments, the scale of automated storage and retrieval systems (AS/RSs) is becoming significantly large. To optimize travel routes for such large-scale AS/RSs, an excessive computation complexity is unavoidable when the existing metaheuristics are applied due to their exhaustive nature to search for better travel routes. In this paper, we propose a method that aims to quickly optimize travel routes by using case-based reasoning. Specifically, in the casebase construction phase, the proposed method constructs a large number of cases each of which consists of the optimized travel route for a particular setting. In the reasoning phase, the travel routes in the cases are then repaired to determine the optimal travel route for the current setting. The experiment results show that the proposed method successfully yields optimized travel routes in a short time compared to the conventional methods for the real-world scale problems.

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