Abnormal Situation Management in a Refinery Supply Chain Supported by an Agent-Based Simulation Model

Oil refineries are of high importance for global economic health and energy supply; any disruptions to their operations may have major worldwide impact. In this paper the application of an agent-based refinery supply chain model to abnormal situation management is described. Agents represent the various decision makers in the supply chain. They own, operate and manage the elements of the physical network of the supply chain. A disruption in ship arrival is used to illustrate the applicability of the decision support system. The decision support system derives a suitable course of action for a given situation based on the outcomes of a number of simulation runs according to the Nelder-Mead zero-order optimization method. This method is based on identification of the best, the worst, and the second worst outcomes in each iteration for the pre-defined experiment. It can be concluded that the decision support system can interact with multiple actors in the supply chain to diagnose and compensate for unanticipated disruptions, with a substantial impact on refinery productivity.