Robust optimization applied to uncertain production loading problems with import quota limits under the global supply chain management environment

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Global supply chain management presents some special challenges and issues for manufacturing companies in planning production: these challenges are different from those discussed in domestic production plans. Globally loading production among different plants usually involves substantial uncertainty and great risk because of uncertain market demand, fluctuating quota costs incurred in the global manufacturing process, and shortening lead times. This study proposes a dual-response production loading strategy for two types of plants—company-owned and contracted—to hedge against the short lead time and uncertainty, and to be as responsive and flexible as possible to cope with the uncertainty and risk involved. Three types of robust optimization models are presented: the robust optimization model with solution robustness, the robust optimization model with model robustness, and the robust optimization model with the trade-off between solution robustness and model robustness. A series of experiments are designed to test the effectiveness of the proposed robust optimization models. Compared with the results of the two-stage stochastic recourse programming model, the robust optimization models provide a more responsive and flexible system with less risk, which is particularly important in the current context of global competitiveness.