<title>Model identification and optimization for operational simulation</title>

Abstract

REPORT DOCUMENTATION PAGEForm Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503 AGENCY USE ONLY (Leave blank)2. REPORT DATE Aug 04 REPORT TYPE AND DATES COVEREDFinal Mar 03 -Feb 04 TITLE AND SUBTITLE MODEL IDENTIFICATION AND OPTIMIZATION FOR OPERATIONAL SIMULATION AUTHOR(S)Douglas A. Popken and Louis A. Cox FUNDING NUMBERSC -F30602-03-C-0167 PE -62702F PR -459S TA -MA WU -02 PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Systems View 9139 Roadrunner St. Highlands Ranch, CO 80129 The purpose of this research effort was to develop and test frameworks and algorithms for use in air warfare planning systems. Developing planning systems for this problem domain is particularly challenging due to their great complexity and uncertainty. The effort focused on predictive simulation models for generating potential outcomes of proposed operational plans. The planning process was organized as a hierarchy of decisions, with those at the top being broadest and longest term. The algorithms at the highest level of planning use a hill-climbing approach, wherein proposed "Blue" plans are evaluated, and the average marginal benefits of alternative force reallocations are computed. Evaluation and measurement of each proposed Blue plan is accomplished via a "Stochastic Evaluator" that draws multiple samples of potential outcomes and "Red" force levels for a given Blue force structure and a combined target composition. The evaluation metric is the net discounted value from enemy targets hit. Within the evaluator, linear programming and simulation generate optimized Red responses, assumed outcomes, and relative marginal force values. This project successfully demonstrated automated plan optimization, practical embedding of optimization algorithms into an operational planning cycle operating over a multi-period conflict, and use of hierarchical decisionmaking to decomposed planning and on-line optimization problems into computationally practical tasks. PERFORMING ORGANIZATION REPORT NUMBER SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES)AFRL SummaryThis research effort developed and tested frameworks and algorithms for use in air warfare planning systems. It focused on the use of simulation models as predictive tools for generating potential outcomes of proposed plans, and incorporating realistically high degrees of complexity and uncertainty into optimization of plans.In this effort, the p...

A Simulation-optimization Approach to Air Warfare Planning

“…In the case of Air Defense or Counter Air, the maximum allocation reflects the expected proportion of opposing aircraft destroyed. The default is 1.0 [30].…”

“…A full discussion of the composite function is beyond the scope of this paper. Interested readers are referred to [30]. Without loss of generality, we drop the target index, t, in the discussion below.…”

“…However, the Intelligence Assessment could be expanded to included updated understandings of weapon effectiveness, i.e., attrition rates, based on the experience of previous periods. Bayesian updating or even Hidden Markov Models could be introduced here [34].…”