A Multi-objective, simulation-based optimization framework for supply chains with premium freights

In this study, a multi-objective, simulation-based optimization framework is developed for supply chain inventory optimization. In this context, a supply chain consisting of a supplier and a number of plants is considered. The plants use a periodic-review order-up-to level policy and request premium freights from the supplier in case of a risky inventory position. Under this setting, the aim of the study is to determine supplier flexibility and safety stock levels that yield the best performance in terms of holding cost and premium freights. Accordingly, a decomposition-based multi-objective differential evolution algorithm (MODE/D) is developed for the proposed framework. As the proposed framework considers both holding cost and premium freight performance, it enables the managers to determine the best tradeoff between the objectives. Consequently, managers have a broad decision spectrum in determining supplier flexibility and safety stock levels. The proposed framework is implemented to a real world multi-national automotive supply chain. Purposely, the results obtained by the proposed framework with MODE/D are compared with the results of non-dominated sorting genetic algorithm II (NSGA-II) and current operating condition of the supply chain. The results reveal that MODE/D yields better holding cost and premium freight performance than those of NSGA-II and current operating condition of the supply chain.