The last pandemic led to tectonic changes in the business world. Some brands considered moving towards closing stores and offering pure online retailing by adding fulfillment centers to their supply chains. In contrast, others opened new stores as they found consumers craving to return to stores. This has made an unprecedented shift in retail business models and introduced a set of new challenges to retailers. One of these problems is to devise plans to close some stores and open new ones over several periods. In this paper, we deal with the problem of finding a timed reconfiguration plan of a retailer’s network as a bi-level optimization model with integer variables on both levels. The firm attempts to maximize the total profit over the planning horizon on the upper level. On the lower level, customers try to find solutions to minimize the total incurred cost given their utility functions. To solve the proposed model, we develop a sequential bargaining strategy, which assumes the firm has partial information about customers’ utility functions. Using a set of test problems, we demonstrate that the firm and customers can reach equilibrium (i.e., a sustainable agreement) using the developed strategy while not sharing sensitive information.