Abstract

The Orienteering Problem with Time Windows (OPTW) is the problem of finding a path that maximizes the profit available at the nodes in a time-constrained network. The OPTW has multiple applications in transportation, telecommunications, and scheduling. First, we extend an exact method for shortest path problems with side constraints into a general-purpose framework for hard shortest path variants. Then, using this framework, we develop a new method for the OPTW that incorporates problem-specific knowledge. Our method outperforms the state-of-the-art algorithm on instances derived from benchmark datasets from the literature achieving speedups of up to 266 times and is able to find optimal solutions to large-scale problems with up to 562 nodes in short computational times.

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