In this work, we present new approaches for solving multiagent planning and temporal planning problems. These planning forms are two types of concurrent planning, where actions occur in parallel. The methods we propose rely on a compilation to classical planning problems that can be solved using an off-the-shelf classical planner. Then, the solutions can be converted back into multiagent or temporal solutions. Our compilation for multiagent planning is able to generate concurrent actions that satisfy a set of concurrency constraints. Furthermore, it avoids the exponential blowup associated with concurrent actions, a problem that many multiagent planners are facing nowadays. Incorporating similar ideas in temporal planning enables us to generate temporal plans with simultaneous events, which most state-of-the-art temporal planners cannot do. In experiments, we compare our approaches to other approaches. We show that the methods using transformations to classical planning are able to get better results than state-of-the-art approaches for complex problems. In contrast, we also highlight some of the drawbacks that this kind of methods have for both multiagent and temporal planning. We also illustrate how these methods can be applied to real world domains like the smart mobility domain. In this domain, a group of vehicles and passengers must self-adapt in order to reach their target positions. The adaptation process consists in running a concurrent planning algorithm. The behavior of the approach is then evaluated.