Decision support for integrated refinery supply chains: Part 2. Design and operation

Abstract Supply chain management has continually attracted much attention as companies are constantly looking into areas where they can cut costs and improve profit margin while maintaining customer satisfaction. Optimizing design and operation of the supply chain is vital for this purpose. Simulation models that capture the dynamics and uncertainties of the supply chain can be used to effectively conduct design and operation optimization studies. In Part 1 of this two-part paper, we proposed an integrated refinery supply chain dynamic simulator called Integrated Refinery In-Silico (IRIS). Here, we demonstrate the application of IRIS to provide decision support for optimal refinery supply chain design and operation based on a simulation–optimization framework. Three case studies are presented: identifying the optimal strategy to deal with supply disruptions, optimization of design decisions regarding additional capacity investments, and optimization of policies’ parameters. These decisions are optimized for two objectives: profit margin and customer satisfaction. The framework consists of a linkage between IRIS and a non-dominated sorting genetic algorithm, implemented in a parallel computing environment for computational efficiency. Results indicate that the proposed framework works well for supporting policy and investment decisions in the integrated refinery supply chain.

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