Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

Abstract

Due to the advancements in manufacturing system technology and the ever-increasing demand for personalized products, there is a growing desire to improve the flexibility of manufacturing systems. Multi-agent control is one strategy that has been proposed to address this challenge. The multi-agent control strategy relies on the decision making and cooperation of a number of intelligent software agents to control and coordinate various components on the shop floor. One of the most important agents for this control strategy is the product agent, which is the decision maker for a single part in the manufacturing system. To improve the flexibility and adaptability of the product agent and its control strategy, this work proposes a direct and active cooperation framework for the product agent. The directly and actively cooperating product agent can identify and actively negotiate scheduling constraints with other agents in the system. A new modeling formalism, based on priced timed automata, and an optimization-based decision making strategy are proposed as part of the framework. Two simulation case studies showcase how direct and active cooperation can be used to improve the flexibility and performance of manufacturing systems.

Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

“…The contacted agent, ag v , should be able to understand this request, deliberate, and respond to the DA-PA. Examples of how other PAs and RAs respond to request violations is provided in the supplementary material [64].…”

“…not authorize, a constraint violation request. Examples of why another agent would deny a constraint violation request are provided in the supplementary material [64]. If the constraint violation is not authorized, the DA-PA may still be able to find a path in the environment that can fulfill its goals.…”

Toward an Automated Learning Control Architecture for Cyber-Physical Manufacturing Systems

“…Each agent must have an individual decision maker. Note that conflicts, such as conflicting requests or learned information from other agents, in the manufacturing system can be resolved by the decision maker through cooperation [23], [24] or by leveraging higher authority agents (e.g., a central controller) [25].…”

“…e) The process must consider and address conflict resolution in the system. Agent-to-agent (e.g., [23], [24]) and non-agent-to-agent (e.g., human-to-agent [55], [56]) conflict resolution must be developed as part of the process.…”

“…For example, the CC can enable adaptation, bounded implemented learning, and exploratory implemented learning only for specific agent capabilities. If exploratory implemented learning authority is enabled for a capability, the agent is allowed to relax existing boundary conditions and send commands to the shop floor that violate these conditions (e.g., [23]). In addition, the agent frequently updates the CC with any information that it obtains from the data gathered from the floor.…”