Based on economic, social, legislation and environmental facts design/redesign close loop supply chain gain special concern among researchers. The supply chain network which is provided in this research, consists of manufacturer, distribution centers, retailers and rework centers. Products flow from manufacturer to distribution centers and retailers to be available for customers. Products which do not satisfy customers desirable quality will be gathered and sent to rework centers. After reworking process, some of them flow within the network again and some of them will leave the network as damaged products. This paper considers a closed-loop supply chain network redesign in a multi-period horizon with price sensitive customers. At first, based on stochastic logit demand function price levels are set. Then based on these price levels customer’s demand is determined. According to available demand, increase or decrease in capacities will take place to secure maximum profit for the entire network. Capacity expansion includes opening new facilities and adding discrete capacities to existing/new facilities and capacity reduction consists of closing current facilities. An accelerated benders decomposition algorithm is proposed for solving this mixed integer programming problem. Results present a considerable reduction in solution time and effectiveness of the proposed algorithm. As prices are changing the aim of the model is to determine the best possible price to maximize the profit. Results which obtained from Gams software show that in medium and large scale problems considering both pricing and redesigning supply chain network need considerable time and cost. Besides, cost consuming nature of strategic decision makes using exact approaches necessary. That why accelerated benders decomposition method is used to solve the proposed model. Results proves that this method’s solutions have better quality in small, medium and large scale problems.