<![CDATA[Tourism journeys always begin with a planning stage, involving the selection of destinations to visit and the accommodation to be used throughout the trip. The Orienteering Problem with Hotel Selection (OPHS) serves as a model for recommending multi-day travel itineraries that are both efficient and profitable, as it aims to maximize total reward (or profit) while keeping the total travel distance within reasonable limits. This research extends the conventional OPHS by incorporating the time-dependent satisfaction of destinations, resulting in a model referred to as the Orienteering Problem with Hotel Selection and Time-Dependent Satisfaction (OPHS-TDS). The motivation for this development arises from the observation that the quality of tourist experience at certain destinations varies depending on the time of arrival. This phenomenon is particularly evident in nature-based attractions, where visiting during specific period can significantly affect both the visitor’s experience and the perceived benefit gained from the trip. The study presents a mathematical formulation of the OPHS-TDS and proposes a solution approach based on the Skewed Variable Neighborhood Search (SVNS) algorithm by allowing exploration of wide range neighborhood of solutions to search for near optimum solution. Computational experiments on benchmark datasets demonstrate that SVNS is efficiently solve OPHS-TDS with only 3.17% average differential with ILP, and further experiments using real-world data from the Province of Bali confirm the model’s applicability and potential in practical multi-day tourism planning scenarios.]]>
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