Intelligent agents in multilevel simulation of manufacturing systems (1990)

Simulation plays an important role in the analysis and design of manufacturing systems. This research develops an object-oriented representation scheme, the intelligent simulation agent, to model the decision making functions as a part of simulation. Multiple levels of an organization structure can be represented using an appropriate number of agents. A prototype called the Intelligent Manufacturing Simulation Agents Tool (IMSAT) has been implemented as a proof of this concept. An intelligent agent is an object with its own knowledge and information bases. An agent's information base ( also known as a blackboard) is a structure holding facts about the part of the system for which that agent is responsible. The rules in an agent's knowledge base represent the decision making heuristics of the corresponding decision making function. The intelligent agent architecture employs Rete, an industry standard algorithm for efficient rule interpretation. Typical expert system tools provide for compiling all the rules into a single Rete net. The IMSAT approach is to consider the system to consist of multiple agents with their own Rete nets for rule interpretation. The intelligent agent structure resulted from two enhancements to an existing inference mechanism (developed by the author): connecting a Rete net to objects, and adding a capability to schedule rule activations and to embed rule agenda structures i• n a simulation calendar. The conceptual models of manufacturing systems emphasize a need to consider both the information flow and material flow in an organization as a part of simulation. The information flow is modeled in IMSAT using the communication of facts between intelligent agents. The IMSAT provides mechanisms to develop models representing the hierarchical structure of manufacturing systems. Objects corresponding to different parts of the organization can be instantiated and linked to represent the structure of the model. Each part of an organization may consist of decision making functions, stores, machines, and manufactured components. The intelligent agents reside in multiple levels of the organization structure. The decision making functions use the shop floor information extracted from the lower levels of the organization. A simulation employing intelligent agents to model these functions must provide mechanisms to model this process. This research proposes the use of a special class of objects associated with simulation processes for this purpose. Tqe IMSAT prototype includes the definition of a class called Lumped Operation to illustrate this approach. Using the instances of Lumped Operation, intelligent agents can collect summarized information (such as the total inventory level of a part) from the simulated processes. Simulation examples were built to illustrate the utility of IMSAT with respect to the research objective of modeling decision making functions. These examples include situations where the decision making activities of intelligent agents show improved outputs from a simulated system. Also, these examples illustrate the modeling of information flow between agents.