Evaluating Refinery Supply Chain Policies and Investment Decisions Through Simulation-Optimization

The dynamic, non-linear, and complex nature of a supply chain with numerous interactions among its entities are best evaluated using simulation models. The optimization of such system is not amenable to mathematical programming approaches. The simulation-optimization method seems to be the most promising. In this paper, we look at a refinery supply chain simulation and attempt to optimize the refinery operating policies and capacity investments by employing a genetic algorithm. The refinery supply chain is complex with multiple, distributed, and disparate entities which operate their functions based on certain policies. Policy and investment decisions have significant impact on the refinery bottom line. To optimize them, we develop a simple simulation-optimization framework by combining the refinery supply chain simulator called integrated refinery in silico (IRIS) and genetic algorithm. Results indicate that the proposed framework works well for optimization of supply chain policy and investment decisions