SBIR-STTR Award

The ARRVUE solution addresses the identified need for a novel approach to re-planning and re-scheduling when unanticipated work requirements (e.g., corrosion, stress failure, etc.) are identified during initial aircraft inspection at an Air Force Air Logistics Complex. Aircraft RAMP Reallocation and Visualization with Unanticipated Events (ARRVUE) is a genetic algorithm solution to a complex integer programming problem that can generate optimized solutions for Repair Bay allocation. It provides planners with the ability to consider standard maintenance process flows with now modified specific work plans. Repair bay allocation is achieved using actual data from ALC systems under real-world constraints such as physical spots on the RAMP, manpower by skill type, and estimated days in work (DIW) to optimize allocation. The Phase I plan will deliver new functionality for dynamic re-planning that builds upon existing infrastructure for necessary data access, adequate computing, and visualization of the solution and solution test and evaluation for the ALC customer. TDKC will extend the successful Aircraft RAMP Visualization and Simulation System (ARViSS) prototype and complete RAMP simulation as a starting point for the ARRVUE development. The resulting environment provides RAMP planners with a graphical process editor and simulation for re-planning and evaluation of work plan changes.

Benefit:
ARRVUE solves the topic"s dynamic RAMP operations re-planning problem with innovative algorithms and optimization. Commercial applications are more and more reliant upon static planning systems, and are often ignorant of that dependency. From an economic perspective, failure to dynamically re-plan work under way (i.e., power generator scheduled maintenance for critical infrastructure) could be catastrophic. Additionally, we believe that the dynamic re-planning with constraints capability provided by ARRVUE could have numerous applications in other domains.

Keywords:
Dynamic operations re-planning, genetic algorithm, simulation, performance optimization, complex integer problem solution

The Aircraft Ramp Reallocation and Visualization with Unanticipated Events (ARRVUE) Phase I solution addressed the identified need for a novel approach to re-planning and re-scheduling when unanticipated work requirements (e.g., corrosion, stress failure, etc.) are identified during initial aircraft inspection at an Air Force Air Logistics Complex (ALC). ARRVUE is a linear programming solution that employs multiple queuing methods to generate conflict-free timeframe and location solutions for ramp spot allocation. TDKC’s proposed Phase II efforts support the Air Force Systems Command’s (AFSC) “Complex of the Future” (2014) vision for more efficient depot operations and supply chain enterprise. This effort includes developing an overall design and approach for the integration of all existing and emerging re-planning and scheduling algorithms; specifying a standard set of Key Performance Indicators (KPI) that expands on those identified during Phase I to increase up-stream planning efficiency in Fleet Scheduling Systems (FSS) through scheduling adjustments; developing unique and effective role-based dashboards for Schedulers to support in vivo collaborative re-planning; and developing a robust decision support environment and technical re-planning framework that seamlessly integrates all the capabilities into an easy-to-use and easy-to-maintain, portable toolset.

Benefits:
The ARRVUE technology will become plug-ins for the TDKC Agile Work Environment (AWE) product line as options for existing AWE customers within the Air Force and other Services. The AWE product with the ARRVUE plug-in library will be marketed to the other agencies and departments. We anticipate commercial opportunities for ARRVUE technology within Phase II and plan to attend conferences, symposiums, and perform continued research to determine additional potential markets for a dedicated tool suite. Examples of potential markets are aircraft manufacturers, aircraft maintenance industries, automotive maintenance and repair facilities, Navy and commercial shipping yards, railways, port authorities, or any enterprise requiring superior oversight of large asset management.

Keywords:
Dynamic Operations Re-Planning, Linear Programming Problem Solution, Queuing Theory, Multi-Constraint Re-Planning, Genetic Algorithm, Simulation, Complex Integer Problem Solution, Performance Optimization