Metroplex-Wide Runway Configuration Management using COBRA (Configuration Optimization for Balanced Runway/Route Assignments) Tool
Small Business Information
Scientific Systems Company, Inc.
500 West Cummings Park, Suite 3000, Woburn, MA, 01801-6562
AbstractSSCI proposes to develop and test a Configuration Optimization for Balanced Runway/Route Assignments (COBRA) tool, which includes analysis and planner algorithms for optimizing runway selection and arrival/departure paths considering the neighboring airports in the metroplex that are competing for slots in the National Airspace System (NAS). Current practices are largely devised under the context of locally optimal planning, i.e. the plans are designed to yield as large a number of safe operations to a single airport during peak loads as possible. COBRA provides a semi-global look at the problem, considering that, periodically, what is best for a single airport may be not be the best plan for the system as a whole. During Phase I we will focus our efforts on 1) Formulating the Problem for a subset of Los Angeles metroplex, for which flight-data is available, 2) Algorithm development using a fast-time Evolutionary Algorithm (EA) to determine which routes provide the fewest conflicts and most efficient use of the terminal airspace, 3) Testing algorithms against Los Angeles metroplex scenarios, and 4) Providing thorough documentation of results. COBRA solutions will map flights to routes within a particular runway configuration and runway assignment. Another layer of the solver will review the nominal runway selection to determine if further balancing might be advantageous for surface operations. Designs will be flexible to incorporate a wide base of airportal constraints and objectives such as wind conditions, approved routes plans, airport surface geometry, and flight restrictions (e.g. noise abatements, separation requirements, etc.), and will be extensible to other metroplexes. Phase II work will seek to extend COBRA to a full metroplex, include uncertainty in the optimization, and test the algorithms against time-varying constraints.
* information listed above is at the time of submission.