A Hyper-Heuristic for the Orienteering Problem With Hotel Selection

We present a hyper-heuristic approach to solve Orienteering Problem with Hotel Selection (OPHS). In practical applications, OPHS appears when a tourist is planning to visit various attractions and there is not enough time to reach all of them in a single day. Therefore, the tourist must build a tour within several days by selecting hotels, where each day has a different time budget. We propose a hyper-heuristic based on a Large Neighborhood Search, composed by a set of low-level heuristics that satisfy the different constraints associated with the problem. We put special emphasis on collaboration between low-level heuristics in order to guide the algorithm to more promising areas. We use 395 benchmark instances with known optimal solutions. This approach proves to be a more general method, with a simpler design compared to the literature, and is able to find 217 of the 395 known optimal solutions, in acceptable computational times.