In this paper, a manufacturing facility with independent workstations to remanufacture returned products is investigated. Not only the stations have limited capacities so that an outsourcing strategy can be practiced, but also the capacities are decision variables. Each workstation is first modelled as an M/M/1/k queuing system with k being a variable. Then, a bi-objective integer nonlinear programming is developed to find optimum capacities. The first objective tries to minimize the total waiting times and the second one maximizes the minimum utilization of the workstations. To solve the complicated bi-objective integer nonlinear programming problem, the best out of the seven multi-objective decision-making (MODM) methods is selected to make the bi-objective optimization problem a single-objective one. Afterward, a meta-heuristic imperialist competitive algorithm (ICA) is developed to find a near-optimum solution of the single-objective problem. Since no benchmark is available in the literature, a genetic algorithm (GA) as well as a simulated annealing (SA) is utilized to validate the results obtained and to evaluate the performance of ICA. Besides, all of the important parameters of the algorithms are calibrated using regression analysis. The algorithms are statistically compared using the Duncan test. For further validation, the results obtained are compared to the ones using the GAMS software. The applicability of the proposed model and the solution algorithms is demonstrated via several illustrative examples.