A hybrid paradigm for modeling of complex systems

Abstract

The network modeling approach to simulation provides the mo deler with simple and powerful concepts which can be used to capture the significant aspects of the system to be modeled. Yet the most advanced network modeling environments lack ex plicit concepts for the representation of complex behavior such as decision-making. Artificial intelligence research, because of its emphasis on knowledge representation, provides several meth odologies which can be successfully applied to the modeling of decision-making behavior. The approach to modeling complex behavior, outlined in this paper, is based on a hybrid methodology unifying the concepts of Object-Oriented programming, Logic Programming and the discrete event approach to systems modeling. We present SIM YON, an experimental network simulation environment which provides explicit constructs for the representation of complex behavior of real-world systems. SIMYON is implemented by de fining a library of logic objects in the Object-Oriented, Logic Programming environment CAYENE. These objects, which are analogous to the "nodes" of network simulation languages, are the building blocks for modeling. Examples are given in SIMYON to model a job scheduler in a manufacturing situation and an adaptive material handling dis patch mechanism for flexible manufacturing systems.