Abstract
In this paper, we consider the Orienteering Problem with Time Windows (OPTW) where the goal is to visit customers within available time windows while maximizing the total reward provided by successive customer visits. For this problem, we introduce a new route recombination procedure that takes a set of solutions as input and returns the best combination containing at most k subsequences of customer visits from these solutions. This route recombination procedure is based on a dynamic programming algorithm enhanced with pruning strategies that can significantly reduce the size of the search space. It is also able to deal with time-dependent transition times. The experiments show that the algorithm proposed can be used as a lightweight and efficient post-optimization procedure working on elite solutions provided by a standard incomplete OPTW solver. Moreover, it can be used in a non-deterministic context where the reward values are not precisely known in advance; in this case, OPTW solutions can be first generated for various reward scenarios during an offline phase, and then combined during an online phase to quickly get a high-quality solution given the last-known reward values.
Published Version
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