GOAL DIRECTED SIMULATION FOR LOGISTICS PLANNING SYSTEM

TWO QUESTIONS ARE OFTEN ASKED IN LOGISTICS MANAGEMENT: HOW CAN I (E.G. SUPPLY THE UNIT WITH AMMUNITION); WHAT IF (E.G. ENEMY DISTRUPTS SUPPLY LINES). THE COUPLING OF HUMAN EXPERTISE WITH ANALYTIC MODELS ALLOW SOME OF THESE QUESTIONS TO BE ANSWERED. BUT MANY OF THESE PROBLEMS ARE SO COMPLEX THAT THE MATHEMATICS ARE INTRACTABLE, REQUIRING THE USE OF SIMULATION, WHICH CAN BE BOTH EXPENSIVE AND TIME CONSUMING. MORE IMPORTANTLY, THE EXPERTISE REQUIRED TO USE SIMULATION EFFECTIVELY MAY NOT BE AVAILABLE. IT APPEARS THAT AI PLANNING AND KNOWLEDGE BASED SIMULATION TECHNIQUES, WHEN COUPLED TOGETHER, CAN ENHANCE THE PRODUCTIVITY AND EFFECTIVENESS OF HUMAN LOGISTICS PLANNERS. THIS PROPOSAL FOCUSES ON TWO ISSUES: PLANNING TECHNIQUES FOR ANSWERING "HOW CAN I" QUESTIONS, AND ITS LINKAGE TO; KNOWLEDGE BASED SIMULATION TECHNIQUES FOR ANSWERING "WHAT IF" QUESTIONS. OUR APPROACH TO PLANNING WILL BE TO CONSTRUCT AN INTERACTIVE PLANNER WHICH USES KNOWLEDGE TO CRITICIZE AND GUIDE THE PLANNING PROCESS. THE PLANNER WILL BE CONSTRUCTED USING A BLACKBOARD ARCHITECTURE, ENABLING IT TO BE PERUSED BY THE LOGISTICIAN AND EFFECTIVELY COUPLED WITH THE SIMULATION SYSTEM. THE MAJORITY OF OUR EFFORT WILL FOCUS ON SIMULATION TOOLS. FOR THE CLASS OF COMPLEX PROBLEMS FOR WHICH SIMULATION IS THE MOST APPROPRIATE TECHNOLOGY, KNOWLEDGE BASED SIMULATION (KBS) (REDDY ETAL., 1986) CAN BE USED MOST EFFECTIVELY TO ANSWER THESE QUESTIONS. HOWEVER, THE CONSTRUCTION AND ANALYSIS OF KBS MODELS STILL REMAINS EXPENSIVE. ALTHOUGH AI REPRESENTATION TECHNIQUES ARE USE, THE DESIGN, IMPLEMENTATION, AND ANALYSIS OF KBS MODELS REQUIRES A VARIETY OF KNOWLEDGE (E.G., DOMAIN INFORMATION, STATISTICAL ANALYSISTECHNIQUES) AND SKILLS (E.G., EXPERIMENT DESIGN) POSSESSED BY FEW. CONSEQUENTLY, WE WILL FOCUS ON THE DEVELOPMENT OF KNOWLEDGE BASES WHICH EXTEND KBS IN THE AREAS OF EXPERIMENT DESIGN AND ANALYSIS.