Improving the intensification and diversification balance of the tabu solution for the Robust Capacitated International Sourcing problem (RoCIS)

This paper addresses the robust capacitated international sourcing problem (RoCIS), which consists of selecting a subset of suppliers with finite capacity, from an available set of potential suppliers internationally located. This problem was introduced by González-Velarde and Laguna in [1], where they propose a deterministic solution method based on tabu search memory strategies. The pro cess consists of three steps: build an initial solution, create a neighborhood of promising solutions and perform a local search in the neighborhood. In this work we propose improving the construction of the initial solution, the cons truction of the neighborhood, the local search, and the intensification and diversification balance. Experimental evidence shows that the improved tabu solution with diver sification outperforms the best solutions reported for six of the instances considered, increases by 18% the number of best solutions found and reduces by 44% the deviation of the best solution found, respect to the best algorithm reported