A transient stochastic simulation–optimization model for operational fuel planning in-theater

Army fuel planners are responsible for developing daily loading plans that specify which tankers to load, with what fuel, and where to send the loaded tankers. The tools used to accomplish this task are custom built spreadsheets which require large amounts of time and effort to use, update, and keep free of errors. This research presents a transient stochastic simulation–optimization model of the in-theater bulk fuel supply chain, where the simulation model is used to simulate the performance of the fuel supply chain under a particular fuel distribution policy and the optimization portion is used to update the policy so that it results in the performance desired by the Army fuel planner. The fuel distribution policy can then be used to derive the daily loading plan. Due to the multi-objective nature of the problem, the set of policies that form the efficient frontier are all candidate policies for the Army fuel planner to select from. Results of experimentation with a wide variety of supply chain scenarios indicate that, for a given supply chain scenario, the optimization portion of the model identifies a set of fuel distribution policies that address the objectives of the Army fuel planner. In addition, the simulation–optimization model comfortably solves the largest supply chain scenarios the Army fuel planner would reasonably be expected to encounter.