A brief review of reverse logistics literature is presented in this chapter, with an emphasis on optimizing and simulating approaches to enhance efficiency and effectiveness. Economic, environmental, and legal factors drive reverse logistics, which involves the return of goods from their final destination to their origin for value recovery. Various optimization techniques, including linear programming, integer programming, and multi-objective optimization, can be used to address reverse logistics challenges. Discrete-event simulation, system dynamics, and agent-based modeling also provide insights into reverse logistics complexities. These two sets of analytical and modeling tools optimization and simulation in reverse logistics are overviewed in this chapter. We also shift the focus to a more specific and understudied area of RL modeling and transportation. Synergies are highlighted between the approaches, illustrating how their integration, especially for transportation in reverse logistics, enhances the decision-making process. Additionally, the chapter covers emerging trends as well as future directions, focusing on technological advances and sustainability within the broader circular economy context. It concludes with identifying gaps in the existing literature and suggesting potential areas for future study.