Industry at present days is concerned with optimization of production and profit, which can be achieved through the recovery options such as recycling, remanufacturing and reusing in the scope of Reverse Logistics (RL) and Closed-Loop Supply Chain (CLSC) concepts due to the environmental, economical and legal obligations. In this research at each output of the reverse portion of the supply chain uncertainty involved provides unknown parameters affecting the forward portion and thus making the whole supply chain environment uncertain. Therefore, fuzzy mixed integer linear programming model is implemented to represent the proposed framework in mathematical terms to maximize the total profit by optimally deciding the quantity of parts to be processed at each reverse supply chain facility and the number of parts to be purchased from multiple suppliers and thereby reduce the excess material and waste. Then determine the number of products to be collected and reused at the various collection/repair centers, the number of products disassembled at the disassembly centers and how many parts to be refurbished, all of this helps the company to make a profit and reduce cost. The fuzzy model is first converted into an equivalent crisp by using α-cut method, after adopting triangular fuzzy numbers to represent the fuzzy market demand and supply chain costs, by estimating optimistic, pessimistic and most likely parameters and the pattern of triangular distribution is commonly adopted due to ease in defining the maximum and minimum limit of deviation of the fuzzy number from its central value. The primary advantages of the triangular fuzzy number are the simplicity and flexibility of the fuzzy arithmetic operations. Finally, the crisp linear programming model is run with LINGO computer software to determine optimal solution. Results showed the maximum profit achieved also the optimal number of parts to be processed at each reverse supply chain facility and the number of parts to be purchased from multiple suppliers.