Scheduling and discrete event control of flexible manufacturing systems based on Petri nets

A flexible manufacturing system (FMS) is a computerized production system that can simultaneously manufacture multiple types of products using various resources such as robots and multi-purpose machines. The central problems associated with design of flexible manufacturing systems are related to process planning, scheduling, coordination control, and monitoring. Many methods exist for scheduling and control of flexible manufacturing systems, although very few methods have addressed the complexity of whole FMS operations. This thesis presents a Petri net based method for deadlock-free scheduling and discrete event control of flexible manufacturing systems. A significant advantage of Petri net based methods is their powerful modeling capability. Petri nets can explicitly and concisely model the concurrent and asynchronous activities, multi-layer resource sharing, routing flexibility, limited buffers and precedence constraints in FMSs. Petri nets can also provide an explicit way for considering deadlock situations in FMSs, and thus facilitate significantly the design of a deadlock-free scheduling and control system. The contributions of this work are multifold. First, it develops a methodology for discrete event controller synthesis for flexible manufacturing systems in a timed Petri net framework. The resulting Petri nets have the desired qualitative properties of liveness, boundedness (safeness), and reversibility, which imply freedom from deadlock, no capacity overflow, and cyclic behavior, respectively. This precludes the costly mathematical analysis for these properties and reduces on-line computation overhead to avoid deadlocks. The performance and sensitivity of resulting Petri nets, thus corresponding control systems, are evaluated. Second, it introduces a hybrid heuristic search algorithm based on Petri nets for deadlock-free scheduling of flexible manufacturing systems. The issues such as deadlock, routing flexibility, multiple lot size, limited buffer size and material handling (loading/unloading) are explored. Third, it proposes a way to employ fuzzy dispatching rules in a Petri net framework for multi-criterion scheduling. Finally, it shows the effectiveness of the developed methods through several manufacturing system examples compared with benchmark dispatching rules, integer programming and Lagrangian relaxation approaches

Modeling sequential resource allocation systems using Extended Finite Automata

Deadlock avoidance for resource allocation systems (RAS) is a well-established problem in the Discrete Event System (DES) literature. This paper is mainly concerned with modeling the class of Conjunctive / Disjunctive sequential resource allocation systems (C/D RAS) as finite automata extended with variables. The proposed modeling approach allows for modeling multiple instance execution, routing flexibility and failure handling. With an appropriate model of the system, a symbolic approach is then used to synthesize the optimal supervisor, in the least restrictive sense. Furthermore, a set of compact logical formulae can be extracted and attached to the original model, which results in a modular and comprehensible representation of the supervisor

Simulationsgestützte Lösung von Deadlocks bei fahrerlosen Transportsystemen mit Hilfe von Deep Reinforcement Learning

This paper discusses the use of deep reinforcement learning to resolve deadlocks in material flow systems with automated guided vehicles (AGVs). The paper proposes a strategy for dealing with deadlocks based on a single Agent reinforcement learning approach (SARL). The agent will find the optimal solution strategy in real time. The proposed approach is evaluated using a material flow simulation for a real use case in industry. The effectiveness in reducing the occurrence of deadlocks as well as the number of collisions in the system is demonstrated. This study highlights the potential of deep reinforcement learning for improving the performance and efficiency of material flow systems with AGVs

A Framework for Applying Reinforcement Learning to Deadlock Handling in Intralogistics

Intralogistics systems, while complex, are crucial for a range of industries. One of their challenges is deadlock situations that can disrupt operations and decrease efficiency. This paper presents a four-stage framework for applying reinforcement learning algorithms to manage deadlocks in such systems. The stages include Problem Formulation, Model Selection, Algorithm Selection, and System Deployment. We carefully identify the problem, select an appropriate model to represent the system, choose a suitable reinforcement learning algorithm, and finally deploy the solution. Our approach provides a structured method to tackle deadlocks, improving system resilience and responsiveness. This comprehensive guide can serve researchers and practitioners alike, offering a new avenue for enhancing intralogistics performance. Future research can explore the framework’s effectiveness and applicability across different systems

Development of a completely decentralized control system for modular continuous conveyors

To increase the flexibility of continuous conveyor systems, a completely decentralized control system for a modular conveyor system was developed. The system is able to carry conveyor units without any centralized infrastructure. Based on existing methods of data transfer in IT networks, single modules operate autonomously and, after being positioned into the required topology, independently connect together to become a functioning conveyor system

Deadlock prevention and deadlock avoidance in flexible manufacturing systems using petri net models

Deadlocks constitute an important issue to be addressed in the design and operation of FMSs. It is shown that prevention and avoidance of FMS deadlocks can be implemented using Petri net models. For deadlock prevention, the reachability graph of a Petri net model of the given FMS is used, whereas for deadlock avoidance, a Petri-net-based online controller is proposed. The modeling of the General Electric FMS at Erie, PA, is discussed. For such real-world systems, deadlock prevention using the reachability graph is not feasible. A generic, Petri-net-based online controller for implementing deadlock avoidance in such real-world FMSs is developed