A discontinuous derivative-free optimization framework for multi-enterprise supply chain

Supply chain simulation models are widely used for assessing supply chain performance and analyzing supply chain decisions. In combination with derivative-free optimization algorithms, simulation models have shown great potential in effective decision-making. Most of the derivative-free optimization algorithms, however, assume continuity of the response, which may not be true in some practical applications. In this work, a supply chain inventory optimization problem is addressed that results in a discontinuous objective function. A derivative-free optimization framework is proposed that addresses the discontinuities in the objective function. The framework employs a sparse grid sampling and support vector machines for identification of discontinuities. Computational comparisons presented show that addressing discontinuity leads to more cost-effective decisions over existing approaches.