Requirement selection is an essential component of software release planning, which finds, for a given budget, an optimal subset of the requirements with the highest value. However, due to the dependencies among software requirements, selecting or ignoring a requirement may impact the values of others. But such Value Dependencies are imprecise and hard to capture; they have been ignored by the existing requirement selection methods, increasing the risk of value loss in software projects. To address this, we have proposed a fuzzy-based optimization method with two main components: (i) a fuzzy-based technique for modeling value dependencies and capturing their imprecision, and (ii) an Integer Linear Programming (ILP) model that takes into account value dependencies in software requirement selection. The scalability and effectiveness of the method in mitigating value loss are demonstrated through simulations.