A multi-modal discrete-event simulation model for military deployment

Abstract

This study introduces a logistics and transportation simulation as a tool that can be used to provide insights into potential outcomes of proposed military deployment plans. More specifically, we model a large‐scale real‐world military Deployment Planning Problem (DPP) that involves planning the movement of military units from their home bases to their final destinations using different transportation assets on a multimodal transportation network. We apply, for the first time, the Event Graph methodology and Listener Event Graph Object framework to create a discrete event simulation (DES) model of the DPP. We use and extend Simkit, an open‐source Java Application Programming Interface for creating DES models. The high‐resolution approach that we take in most part, allows us to estimate whether a given plan of deployment will go as intended, and determine prospective problem areas in a relatively short time compared to other existing simulations because of the absence of the need to use several models of differing resolutions in succession as often done in literature. For a typical deployment scenario for four battalions, run times are between 25 to 27 minutes for 60 runs of the model on a 1.6 GHz Pentium(R) M PC with 512 MB RAM. That is less than 30 seconds per run. To accurately incorporate real and detailed transportation network data into the simulation, we use GeoKIT, a state‐of‐the‐art, Java‐based Geographical Information System. The component‐based approach adopted in development of our simulation model enables us to easily integrate future additions to our model. The DES developed as part of this dissertation provides a test bed for currently existing deployment scenarios. While our DES model is not a panacea for all, it allows for testing the feasibility and sensitivity of deployment plans under stochastic conditions prior to committing members of the military into harm’s way. Our main contribution is to develop a comprehensive, multi‐modal, high‐ resolution, loosely‐coupled and modular, extendable, platform independent, state‐of‐ the‐art GIS based simulation environment that views the deployment operations as end‐to‐end processes. Such a simulation environment for multi modal deployment planning and analysis does not exist. Additionally, we simulate and analyze a typical real‐world case study by using conventional methods and the rather novice Nearly Orthogonal Latin Hypercube Sampling (NOLHS) technique. We use a space‐filling nearly orthogonal design of 29 factors and 257 runs to determine the factors that impact most on a deployment plan. We make 15 replications of each of the 257 runs (scenarios) to reach a total of 257x15=3855 computer runs compared to an experiment with 29 factors, each with only 2 levels and 15 replications per run, for a complete enumeration experiment (229 x15= 8,053,063,680 computer runs!).