In recent years, the expansion of social responsibility concept, increased environmental considerations, economic incentives and governmental pressure on manufacturers for waste management have caused organizations to focus attention on the development of closed-loop supply chains (CLSC) and reverse logistics (RL) processes. The adoption of these approaches, will enable organizations to simultaneously meet economic, social and environmental goals and consider the manufacturing cycle from supply and production to reuse of products. Hence, this study deals with an optimization model within the framework of a multi-echelon, multi-product and multi-period CLSC with hybrid facilities where cross-docking strategy and vehicle routing with soft time windows have been included in the model. In the problem defined as a MILP model, decisions are made simultaneously at three levels of strategic, tactical and operational. Furthermore, to tackle the NP-hard problem and achieve near-to-optimal results in reasonable time, two meta-heuristic algorithms, NRGA and MOPSO are developed and the algorithms’ parameters are tuned using the Taguchi method. Finally, the computational results are examined by the performance measures and statistical analysis and the sensitivity analysis is performed regarding the impacts of demand and rate of returned product on the objective functions’ values.