Solving routing problems efficiently is instrumental in minimizing operational costs in logistics. These routing problems are hard to solve and often take a lot of time to find a good solution. In this thesis, we present a methodology that tackles the challenge of efficiently solving recurring instances of the Vehicle Routing Problem with Time Windows by recycling solutions. By making more problem-specific assumptions, we introduce a solution recycling approach that can leverage shared solution structures across similar instances. This accelerates the solution-finding process. We implement our methodology in the framework of constraint programming and show that such a methodology is actually useful and is a concept that is yet unexplored in combinatorial optimization.