Abstract
Agent-based intelligent manufacturing control systems are capable to efficiently respond and adapt to environmental changes. Manufacturing system adaptation and evolution can be addressed with learning mechanisms that increase the intelligence of agents. In this paper a manufacturing scheduling method is presented based on Timed Colored Petri Nets (CTPNs) and reinforcement learning (RL). CTPNs model the manufacturing system and implement the scheduling. In the search for an optimal solution a scheduling agent uses RL and in particular the Q-learning algorithm. A warehouse order-picking scheduling is presented as a case study to illustrate the method. The proposed scheduling method is compared to existing methods. Simulation and state space results are used to evaluate performance and identify system properties.
Highlights
Flexibility, cost reduction, production efficiency, improved inventory control, and ability to respond to fluctuations in demand are among the drivers for innovative automated solutions to all members of a supply chain
In heuristic search algorithm methods, Petri Nets (PNs) model the system and a search algorithm based on a heuristic function is used to expand a portion of the reachability graph, containing the most promising nodes that leads to an optimal scheduling solution [3,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]
Eachstates generated schedule is whereas policy actions implement related scheduling rules, such as dispatching rules. It corresponds to a path from the initial marking to a final marking in the Colored Petri Nets (CPNs)
Summary
Flexibility, cost reduction, production efficiency, improved inventory control, and ability to respond to fluctuations in demand are among the drivers for innovative automated solutions to all members of a supply chain. Petri Nets (PNs) and Colored Petri Nets (CPNs) are a discrete-event graphical and mathematical modeling tool applicable to systems characterized as being concurrent, asynchronous, distributed, parallel, nondeterministic, and/or stochastic [8,9] As such, they have been used extensively for modeling, scheduling, and control of Flexible Manufacturing Systems (FMS). As a graphical-oriented high-level language, it is used for the modeling and validation of systems in which concurrency, communication, and synchronization play a major role This is why it finds many applications in the area of distributed artificial intelligence (AI) where agents come from. Combinations of PNs- and AI-search based techniques have found applications to manufacturing scheduling, where PNs model the system and a heuristics based search through the reachability graph finds an optimal or near-optimal solution These methods are not as efficient for large, complex manufacturing systems in changing environments.
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