Model and Combinatorial Optimization Methods for Tactical Planning in Closed-Loop Supply Chains

Distribution planning in closed-loop supply chains is concerned with determining transfer and repair operations based on demand forecasts and subject to backordering, inventory, transfer and repair constraints. We present a mixed-integer programming model and a dedicated metaheuristics for this problem and show it is is NP-hard. The model is applicable to a wide range of closed-loop supply chains with different network topologies and site functions and it can also support different planning strategies by means of a weighted objective function. Comparative experiments on pseudo-random instances built on a case study in telecommunication service operations demonstrate the effectiveness and scalability of the metaheuristics. Lastly, we discuss possible extensions to address common supply chain requirements, including the ability to produce robust plans in uncertain environments.