This dissertation focuses on managerial and operational challenges associated with product return management and CLSC network design. The possibility of product return plays an important role in consumer's purchase decisions. It also motivates firms to extend their forward-only supply chain network structures to a Closed-Loop Supply Chain (CLSC) network and handle both forward and reverse flows of products. While the configuration of the CLSC network is a complex problem comprised of the determination of the optimal locations and capacities of factories, warehouses and collection centers, this problem becomes even more complex under the potential regulations on carbon emissions.

This dissertation follows a three-paper format. With a focus on product return management, the first paper studies the roles that pricing and return policy play in the product exchange process for refurbished products. We first apply netnography to study consumer attitudes, general opinions and experiences concerning refurbished electronics purchases, and then propose an analytical model that considers customers' purchasing and return behavior as a result of the firm's decisions regarding the pricing and return policy for refurbished products. The numerical results suggest that sellers should deliberately consider the market segmentation conditions, consumer valuation, and cost factors when choosing the appropriate price and return policy for refurbished products.

The second and third paper focus on different aspects of CLSC network design. The second paper investigates a problem to design facility configurations that are robust to variations in possible carbon regulations and their cost and constraint implications. We establish a two-stage, multi-period stochastic programming model to include uncertain demand and return quantities and then extended it to incorporate the uncertainties in carbon regulation policy by the robust optimization method. We propose a hybrid model to account for either carbon tax or cap-and-trade regulatory policies and derive tractable robust counterparts under box and ellipsoidal uncertainty sets. Implications for network configuration, product allocation and transportation configuration are derived. We also present computational results that illustrate how the problem formulation under an ellipsoidal uncertainty set allows the decision maker to balance the trade-off between robustness and performance.

The third paper formulates and solves an integrated model for product return management and CLSC network design considering uncertain carbon cost. We build a robust optimization model to address the carbon cost uncertainty, and develop a piecewise linear approximation for the nonlinear profit as a function of the refund. The results of the robust model are compared with those of deterministic models where no or only nominal carbon cost is considered. Extensive parametric analyses illustrate the impact of the cost, revenue and consumer profile parameters on the optimal refund, profit and network topology.