<![CDATA[Tourist route planning plays an important role in improving travel efficiency and visitor experience in urban tourism areas. Batam City offers numerous tourist destinations with diverse characteristics, making route planning under realistic constraints a challenging task. Classical Traveling Salesman Problem (TSP) models assume that all destinations must be visited, which is often impractical in real-world tourism scenarios. Therefore, this study proposes an optimization model for tourist route planning in Batam City using Integer Linear Programming (ILP) with destination category constraints. The proposed model allows a subset of destinations to be selected from a larger candidate set while determining the optimal visiting sequence. The objective function minimizes the total travel distance based on fixed driving distances obtained from digital map services. The model incorporates several constraints, including route continuity, subtour elimination, and category restrictions that limit the number of destinations selected from specific categories, such as beaches, shopping malls, and cultural sites. The model is implemented in Python using the PuLP optimization library and evaluated across four scenarios with varying category constraint settings. In the main scenario, the model produces an optimal route visiting 11 destinations with a total travel distance of 123.6 km. Scenario analysis further demonstrates that destination category selection significantly affects the total travel distance, with differences of up to 53.8% across scenarios despite a constant number of selected destinations. These findings indicate that ILP provides a flexible and effective framework for constrained tourist route optimization in urban tourism contexts. Furthermore, category-based constraints offer valuable insights into the trade-off between destination diversity and route efficiency.]]>
