Proceedings of the 2019 Winter Simulation Conference N. Mustafee, K.-H.G. Bae, S. Lazarova-Molnar, M. Rabe, C. Szabo, P. Haas, and Y.-J. Son, eds. ANALYZING PRE-POSITIONING WITHIN A DISRUPTED BULK PETROLEUM SUPPLY CHAIN Manuel D. Rossetti Julianna Bright University of Arkansas Department of Industrial Engineering 4207 Bell Engineering Center Fayetteville, AR 72701, USA ABSTRACT This paper examines the modeling of disruption events within a bulk petroleum supply chain through the use of an object-oriented simulation library. We describe how the library models disruption events and their effects on supply chain operations. This includes how to specify disruptions, mitigation strategies and metrics that can be used to assess the impact of the disruption. The modeling is illustrated via an analysis of a supply chain involving DLA Energy’s bulk petroleum supply chain as impacted by a category 4 hurricane scenario. The effectiveness of pre-positioning of inventory within the supply chain is illustrated. 1 INTRODUCTION The Defense Logistics Agency (DLA) Energy Division manages the global supply chain network for bulk petroleum products for the support of U. S. military bases. The bulk petroleum supply chain network (BPSCN) is a complex supply network that consists of external commercial suppliers, DLA fuel terminals, and end customer locations (mostly military bases). The network is supported through four major transportation modes (barge, truck, rail, and pipeline), most of which are commercial entities. The network supports eight different fuel products, which include three types of additives and five different fuel types. For the purposes of this paper, we only consider U.S. standard commercial jet fuel. The DLA Energy supply chain network is a global inter-connected network and has many unique issues that make its efficient operation very challenging. In addition to supporting normal peace time operations, the network must plan for and be able to support both war time and contingency operations for both local and global theaters of operation. A key management issue is the uncertainty of demand because of these operations, some of which have little advance warning. In addition, there is uncertainty in supply because events may occur that disrupt the availability of fuel. We call these types of events disruption events. A disruption event can cause changes in both supply and demand over a period of time. An example of such an event is Hurricane Harvey, which made land fall in the Houston, Texas area in the summer of 2017. Hurricane Harvey caused serious disruptions within the Houston area that had a rippling effect within the U.S. because Houston is a key location for fuel refinery operations. Rossetti and Bright (2018) describes the conceptual modeling required to represent bulk petroleum supply chain networks within a simulation context. In addition, they describe the basic simulation constructs of an object-oriented library that can be used to model the performance of a BPSCN over general planning horizons involving normal and contingency operations. This paper builds on the work of Rossetti and Bright (2018) to further describe the disruption scenario modeling that is supported by the simulation library as well as performance measures that are useful in understanding the behavior of a system under disruption events. In addition, the library supports the modeling of effectiveness of disruption mitigation strategies. In particular, we provide the ability to assess pre-positioning strategies within the BPSCN. 1813 978-1-7281-3283-9/19/$31.00 ©2019 IEEE