General variable neighborhood search for the multi-product dynamic lot sizing problem in closed-loop supply chain

The multi-product dynamic lot sizing problem with product returns and recovery is an important problem that appears in reverse logistics and is known to be NP-hard. In this paper we suggest a General Variable Neighborhood Search (GVNS) metaheuristic algorithm for solving this problem. It is the first time that such an approach is used for this problem in the literature. Furthermore, we present some encouraging computational results obtained on a new set of very large benchmark instances, compared with Gurobi optimizer.