AMMUNITION ALLOCATION EXPERT SYSTEM

IN COMBAT OPERATIONS THE EXPENDITURE OF AMMUNITION IS ONE OF THE MOST SIGNIFICANT FACTORS IN DETERMINING THE OUTCOME OF A CONFLICT. REQUIREMENTS FOR AMMUNITION RESUPPLY VARY IN ACCORDANCE WITH EXPENDITURE RATES THAT ARE SUBJECT TO DYNAMICALLY CHANGING TACTICAL PLANS. MORE ACCURATE CONSUMPTION RATE FORECASTS CAN BE OBTAINED BY MODIFYING FORECASTS THAT ARE BASED ON STANDARD SUPPLY PLANNING DATA USING MODELS AND HEURISTICS OF THEATER AND SITUATION DEPENDENT VARIATIONS. THE OBJECTIVE OF THE PROPOSED RESEARCH IS TO DEVELOP AN AI-BASED DECISION SUPPORT SYSTEM THAT ENHANCES THE FIELD LOGISTICIAN'S ABILITY TO ACCURATELY PREDICT AMMUNITION RESUPPLY REQUIREMENTS. THE TECHNICAL APPROACH INTEGRATES MACHINE LEARNING, PERTURBATION THEORY, AND HIERARCHICAL OBJECT-BASED CLUSTERING TECHNIQUES.