A novel rolling horizon strategy for the strategic planning of supply chains. Application to the sugar cane industry of Argentina

In this article, we propose a new method to reduce the computational burden of strategic supply chain (SC) planning models that provide decision support for public policy makers. The method is based on a rolling horizon strategy where some of the integer variables in the mixed-integer programming model are treated as continuous. By comparing with rigorous solutions, we show that the strategy works efficiently. We illustrate the capabilities of the approach presented by its application to a SC design problem related to the sugar cane industry in Argentina. The case study involves determining the number and type of production and storage facilities to be built in each region of the country so that the ethanol and sugar demand is fulfilled and the economic performance is maximized.

► A new method to reduce the computational expense of strategic supply chain planning models.
► The method is based on a relaxation of some integer variables.
► The case study is based on the Argentinean sugar cane industry.
► The proposed method provides solutions with less than 3% of optimality gap in a fraction of time spent by rigorous branch and cut methods.
► Sugar price has the largest effect on the economic performance and configuration of the network.