Optimizing Operations and Logistics Support Using Opus Evo

Abstract

Understanding the interplay between the reliability and maintainability of a fleet of complex systems, the logistics support organization, and the operational scenario, is vital from a short-term tactical perspective, as well as a strategic long-term Life Cycle Cost (LCC) perspective, as each of these areas has a direct impact on one another. A common method to analyze and evaluate the performance of the overall scenario, as well as getting insights into problem areas, bottlenecks and to perform analysis-of-alternatives is to use discrete event simulation. In this paper we present a methodology to extend a discrete event simulation tool with inherent optimization capabilities. Using established heuristic optimization techniques, we perform simulation driven optimization that optimizes parameters in the modeled scenario. Optimized parameters typically include: • sparing strategies such as inventory levels and locations • resource quantities and location • deployed system quantities to fulfill mission requirements • scheduled maintenance times. • transportation and resource schedules. A case study is presented that utilizes Opus Evo, an application that extends the commercial off the shelf Opus Suite with capability to perform heuristic optimization using simulation.